Streptococcus pneumoniae and Staphylococcus aureus Pneumonia

Induce Distinct Metabolic Responses

Carolyn M. Slupsky,*,† Andriy Cheypesh,†,# Danny V. Chao,†,# Hao Fu,‡ Kathryn N. Rankin,‡

Thomas J. Marrie,† and Paige Lacy†

Department of Medicine, and Magnetic Resonance Diagnostics Centre, 550A HMRC, University of Alberta,Edmonton, Alberta, Canada T6G 2S2

Received February 8, 2009

Pneumonia is an infection of the lower respiratory tract caused by microbial pathogens. Two suchpathogens, Streptococcus pneumoniae and Staphylococcus aureus, are the most common causes ofcommunity-acquired and hospital-acquired pneumonia respectively. Each expresses strains highlyresistant to penicillin and other antibiotics, and a significant number of people succumb to infectionby these pathogens every year. Urinary metabolite changes in a C57Bl/6 mouse model with lunginfection from either S. pneumoniae or S. aureus were characterized using multivariate targeted profilingdata obtained from 1H NMR spectra. Marked changes in the urinary metabolite profile occurred within24 h after infection with either pathogen. Specifically, significant decreases in TCA cycle intermediates,coupled with increases in fucose, creatine, and taurine were observed in the urine of S. pneumoniae-treated mice. Infection with S. aureus resulted in the decrease of a number of urinary metabolitesincluding 1-methylnicotinamide, 3-methyl-2-oxovalerate, 2-oxoisocaproate, N-isovaleroylglycine andothers. Disturbances in gut-derived microbial metabolites were also observed. Analysis of metabolictrajectory data indicated that, as the mice recovered from infection, their urinary metabolic profilebecame similar to that of the preinfected state. These results underline the potential of metabolomicsas a tool for diagnosis, health monitoring, and drug development, and show its usefulness forunderstanding microbial-host interactions.

Keywords: NMR • metabolomics • metabonomics • infection • targeted profiling • MRSA • pneumonia• urine

Introduction

Streptococcus pneumoniae and Staphylococcus aureus arefacultative anaerobic Gram-positive cocci bacteria. S. pneu-moniae is a major pathogen in community-acquired pneumo-nia, whereas S. aureus, particularly methicillin-resistant strains,is the leading cause of nosocomial pneumonia. Both bacteriaare associated with colonization of the upper respiratory tract,can cause significant morbidity, and with the increasingprevalence of antibiotic-resistant strains, are becoming aconsiderable cause of mortality. Studies have shown that theoverall penicillin resistance of S. pneumoniae strains haveincreased from 4% to 21% over a 3-year period,1 and that newisolates resistant to vancomycin2 and levofloxacin3 are emerg-ing. In addition, S. aureus strains have acquired multipleresistance mechanisms to all conventional antibiotics,4 espe-cially penicillin. Although the emergence of methicillin-resistantS. aureus (MRSA) in the 1960s was primarily in health-carefacilities, over the past decade it has seen a marked increase

in the general community.4 Furthermore, many community-acquired strains of MRSA contain Panton-Valentine leukocidin,a pore-forming exotoxin that has been shown to be associatedwith increased virulence and the cause of necrotic lesions.5,6

Studying host metabolic response to infection is a potentialway of increasing our understanding of this process, therebyaiding in finding novel approaches for diagnosis and treatment.Metabolomics offers a unique approach to characterize suchchanges in accessible biological samples such as urine andserum, as metabolite profiles generally vary as a result ofperturbations of the genome and proteome. We have shownthat NMR spectroscopy, coupled with targeted profiling tech-niques, offers a powerful approach to generate high-densitymetabolic data on biofluids.7-10 Through the use of multivari-ate statistical techniques,11 analysis of metabolic differencesgiving rise to gender, diurnal, diet, age or disease phenotypeshave been observed.9,12-18

In this study, we report a comparison of urinary metaboliteprofiles from C57Bl/6 mice infected with either S. pnemoniae(serotype 14) or S. aureus (MRSA). Significantly different urinarymetabolite profiles were observed with infection by eachpathogen. These studies suggest that metabolomics may be apotential tool for discriminating between etiological pathogensresponsible for pneumonia.

* Corresponding author: Carolyn Slupsky, Department of Medicine, 550AHeritage Medical Research Centre, University of Alberta, Edmonton, Alberta,Canada T6G 2S2. Phone: (780) 492 - 8919. Fax: (780) 492 - 5329.

† Department of Medicine, University of Alberta.# Contributed equally to this work.‡ Magnetic Resonance Diagnostics Centre, University of Alberta.

10.1021/pr900103y CCC: $40.75 2009 American Chemical Society Journal of Proteome Research 2009, 8, 3029–3036 3029Published on Web 04/15/2009

Experimental Section

Bacteria. Individual colonies of S. pneumoniae serotype 14(strain 04SR2228) or methicillin-resistant S. aureus (Panton-Valentine-positive, R toxin-positive clinical isolate), grown onblood agar plates, were cultured in Todd-Hewitt (THB) brothuntil the midlog phase of exponential growth. Bacterial suspen-sions were resuspended in prewarmed THB prior to allexperiments.

Mice. Male C57Bl/6 mice (infected between 8 and 10 weeksof age) were purchased from Charles River Laboratory, Canada.Mice were housed and handled according to approved proto-cols of the institutional animal ethics committee, and keptunder artificial light on a 12 h light/dark cycle for 1 week priorto infection. Mice had access to water and food ad libitum.For infection with S. pneumoniae, 30 mice were subjected toinhalation narcosis with 4% isoflurane and 96% oxygen with aflow rate of 0.5 L/min followed by injection of either 40 µL ofTHB (vehicle-alone control mice), or 40 µL of S. pneumoniaeserotype 14 (30 × 106 cfu/mouse) in THB (infected mice)between the second and third tracheal cartilages. Skin wassutured after surgery. For infection with MRSA, 30 mice weresubjected to inhalation narcosis as described above, andintranasally injected with either 40 µL of THB (vehicle-alonecontrol mice), or MRSA (20 × 106 cfu/mouse) in THB (infectedmice). Intranasal infection was used for MRSA since micebecame readily infected using this route as determined byhistology and BAL measurements. All groups of mice (sham-treated and pathogen-treated) contained 15 animals, althoughsamples were only analyzed from animals that fell within twocriteria: (1) lung infection was evident from increased lunginflammatory infiltrates (total cell count in bronchoalveolarlavage (BAL) samples above the average of sham-treatedvalues); and (2) a sufficient urine volume could be collectedallowing NMR analysis (>50 µL per animal).

After the mice were revived from treatment, they were placedinto prelabeled cages and monitored every 6 h to determinehealth status and signs of disease, and evaluated according toanimal care criteria. At 24 h, mice were sacrificed by overdosewith intraperitoneal ketamine/rompun. Urine was obtained bypuncturing the base of the bladder with a syringe. BAL sampleswere obtained by accessing the mouse trachea as describedabove, and injecting 2 mL of ice-cold Hank’s Balanced SaltSolution (HBSS) into the lungs. BAL fluid was collected andcentrifuged at 14 000g at 4 °C for 10 min to pellet cells andbacteria.

For longitudinal studies, spot urine samples were collectedevery 6 h, starting 6 h prior to infection by placing mice overweigh-boats, allowing them to urinate, and measuring urinevolume prior to preparation for NMR analysis. From 24 h priorto treatment, and for several days after treatment, mice wereobserved for general health status as reflected in activity levels,general appearance (reflected in coat), and appetite levels.Activity levels, appearance and appetite levels were each gradeda score of 0-4, with 0 representing no activity, poor appear-ance, and no appetite, and 4 representing normal activity,healthy appearance and normal appetite, respectively. Theaverage score for each of the sham and infected groups wereplotted over time.

Histology. Lungs from mice for histology were fixed with 10%formalin, embedded in paraffin wax, sectioned, stained withhematoxylin eosin stain (H/E), evaluated for the presence andseverity of inflammation, and photographed.

Cell Count. Total cell counts were measured in BAL samplesby hemocytometer, and differential counts were determinedusing cytospins of BAL cells stained with Diff-Quick.

Data Collection. Urine samples obtained from mice werefiltered through 3 kDa MW cutoff filters, and diluted whereappropriate with H2O to a total volume of 585 µL. Internalstandard containing DSS-d6, NaN3 and D2O (65 µL) was added,and the pH was adjusted to ∼6.8 prior to NMR acquisition aspreviously described.9 Analysis of these data was accomplishedusing targeted profiling through the use of Chenomx NMR Suite4.6 (Chenomx Inc. Edmonton, Canada) and concentrationsdetermined as previously described9 using the 600-MHz libraryfrom Chenomx NMR Suite 4.6 which compares the integral ofa known reference signal (DSS-d6) with signals derived from alibrary of compounds containing chemical shifts, couplingconstants, and peak multiplicities. For each mouse, 41 me-tabolites were measured and quantified from the urine andincluded 1-methylnicotinamide, 2-oxoglutarate, 2-oxoisoca-proate, 3-indoxylsulfate, 3-methyl-2-oxovalerate, acetate, ala-nine, allantoin, betaine, carnitine, singlet (choline), citrate,creatine, creatinine, dimethylamine, ethanolamine, formate,fucose, fumarate, glucose, glycine, singlet (glycolate), singlet(guanidoacetate), hippurate, lactate, methanol, methylamine,N,N-dimethylglycine, N-carbamoyl-�-alanine, N-isovaleroylg-lycine, phenylacetylglycine, succinate, taurine, trigonelline,trimethylamine, trimethylamine-N-oxide, uracil, urea, xylose,cis-aconitate, and trans-aconitate. Chemical shifts for signifi-cant metabolites as measured using Chenomx NMR Suite areshown in Tables 1 and 2.

Data Analysis. Multivariate data analysis, Principal Compo-nent Analysis (PCA) and Partial Least Squares-DiscriminantAnalysis (PLS-DA) was performed on log10-transformed me-tabolite concentrations that had been mean centered with unitvariance scaling applied using SIMCA-P (version 11, Umetrics,Umeå, Sweden). Log10 transformation was applied to accountfor the non-normal distributive nature of the metabolite data.The number of components for both PCA and PLS-DA modelswere chosen using a 7-fold cross validation rule. Data qualitywas assessed by permutation testing, where each data pointwas randomly assigned a class 20 times, and the correspondingR2 and Q2 charted in a correlation plot with the original model.

For generation of the metabolic trajectory plot, PCA wasperformed on targeted profiling data, and the scores of the firstand second principal components were averaged for the sham-and S. pneumoniae-treated mice. For ease of presentation,time-points with similar distribution were pooled (i.e., the datacollected from mice at -6 h was averaged with the datacollected at time 0; data at 6 and 12 h time points wereaveraged; 18, 24, and 36 h time points were also averaged).Beyond 48 h, data from infected mice overlapped with shamvalues in the upper half of the PCA plot, and were excludedfrom the plot.

Results

Mice infected with S. pneumoniae showed increased mac-rophages and polymorphonuclear leukocytes in the BAL fluid(Figure 1A). Histologically, the alveolar spaces were filled withan inflammatory exudate consisting of polymorphonuclearleukocytes and macrophages (Figure 1B). Mice infected withMRSA showed no change in macrophages and an increase inpolymorphonuclear leukocytes in the BAL fluid (Figure 1C),with alveolar spaces filled primarily with polymorphonuclearleukocytes (Figure 1D). These results indicate that infected mice

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mounted an intense inflammatory response with either S.pneumoniae or MRSA generating a histological profile resem-bling pneumonia.

Representative examples of 1H NMR spectra obtained frommouse urine after treatment with either sham, S. pneumoniae(SPN), or MRSA are shown in Figure 2. Resonance assignmentswere made using Chenomx v4.6 software. Comparison of theNMR spectra between sham- and MRSA-treated mice revealedsubtle changes in this region of the spectrum, with a slightincrease in creatine, and decrease in 2-oxoglutarate andsuccinate. However, comparison of sham- with S. pneumoniae-treated mice revealed significant decreases in citrate, 2-oxo-glutarate, and succinate, as well as an increase in creatine.

PCA was performed using metabolite concentrations ob-tained from analysis of urine collected from mice 24-h aftersham or S. pneumoniae infection (Figure 3A). To maximizeseparation between sham and infected mice, PLS-DA wasemployed (Figure 3B). Three components were calculated, withthe corresponding model explaining 94% of the total variance.To ensure that discrimination in the model was not the resultof overfitting, a permutation test was performed in which classlabels were randomly shuffled into 20 different combinations.

Comparison between R2 and Q2 of the model and randomlypermutated data revealed a valid model (Figure 3C) withpositive slopes for both R2 and Q2. Twenty metabolites wereprimarily responsible for the observed difference between shamand S. pneumoniae-treated mice and are summarized in Table1, with the corresponding variable importance and rank for thePLS-DA model. The TCA-cycle intermediates (2-oxoglutarate,trans-aconitate, citrate, fumarate, and succinate) as well asmetabolites related to gut microbes (3-indoxylsulfate, hippu-rate, and trimethylamine) and other metabolites (betaine,formate, glycine, glycolate, lactate, N-carbamoyl-�-alanine, andtrigonelline) decreased in concentration, while others increased(creatine, ethanolamine, fucose, taurine, and uracil).

PCA analysis of urinary metabolite concentrations deter-mined in mice 24 h after sham- or MRSA-treatment revealedseparation (Figure 3D). PLS-DA was employed (Figure 3E), and2 components were subsequently calculated with the corre-sponding model explaining 91% of the total variance. Asignificant degree of separation was observed in the firstcomponent. To ensure that discrimination was not the resultof overfitting, a permutation test was performed. Comparisonbetween R2 and Q2 of the model with the randomly permuted

Table 1. Metabolite Changes in Mouse Urine Induced by S. pneumoniae Lung Infection When Compared to Sham Treatment

metabolitea δ (ppm)%

changebimportance

(rank)c

Ethanolamine 3.141, 3.818 +184 1.45 (4)Fucose 1.199, 1.240, 3.442, 3.636, 3.743, 3.760, 3.797, 3.805, 3.853, 4.189, 4.551, 5.200 +176 1.51 (2)Creatine 3.033, 4.046 +126 1.14 (9)Uracil 5.793, 7.524 +67 0.99 (18)Taurine 3.257, 3.424 +54 0.94 (20)Hippurate 3.961, 7.541, 7.628, 7.825, 8.518 -22 0.97 (19)Trigonelline 4.428, 8.072, 8.819, 8.835, 9.114 -38 1.02 (16)Glycine 3.558 -40 1.03 (15)Betaine 3.254, 3.891 -44 1.09 (13)Succinate 2.402 -50 1.13 (10)N-Carbamoyl-�-alanine 2.368, 3.299, 6.212 -51 1.06 (14)trans-Aconitate 3.446, 6.589 -53 1.02 (17)Singlet (Glycolate) 3.944 -53 1.24 (8)3-Indoxylsulfate 7.193, 7.267, 7.350, 7.493, 7.692 -66 1.37 (6)Trimethylamine 2.887 -67 1.12 (12)Formate 8.450 -68 1.13 (11)Citrate 2.555, 2.705 -72 1.30 (7)2-Oxoglutarate 2.435, 3.000 -89 1.50 (3)Lactate 1.324, 4.113 -96 1.40 (5)Fumarate 6.515 -99 1.64 (1)

a Metabolites ranked according to % change. b Calculated as difference in median concentration between infected and sham. c Variable importance andrank was determined from PLS-DA variable importance to projection (VIP) for the model S. pneumoniae-treated versus sham-treated.

Table 2. Metabolite Changes in Mouse Urine Induced by S. aureus (MRSA) Lung Infection When Compared to Sham Treatment

metabolitea δ (ppm)%

changebimportance

(rank)c

1-Methylnicotinamide 4.467, 8.169, 8.883, 8.952, 9.263 -60 1.40 (6)3-Methyl-2-oxovalerate 0.886, 1.090, 1.448, 1.692, 2.937 -45 1.69 (1)3-Indoxylsulfate 7.193, 7.267, 7.350, 7.493, 7.692 -41 1.31 (12)Phenylacetylglycine 3.668, 3.752, 7.353, 7.412, 7.978 -39 1.32 (11)Fucose 1.199, 1.240, 3.442, 3.636, 3.743, 3.760, 3.797, 3.805, 3.853, 4.189, 4.551, 5.200 -36 1.41 (5)2-Oxoisocaproate 0.928, 2.086, 2.605 -32 1.59 (2)Xylose 3.217, 3.312, 3.424, 3.516, 3.599, 3.617, 3.653, 3.666, 3.688, 3.917, 4.573, 5.192 -28 1.39 (8)Ethanolamine 3.141, 3.818 -27 1.40 (7)N-Isovaleroylglycine 0.928, 1.992, 2.165, 3.750, 7.961 -18 1.47 (4)Allantoin 5.385, 6.028 -18 1.35 (9)Taurine 3.257, 3.424 -19 1.34 (10)

a Metabolites ranked according to %change. b Change calculated as difference in median concentration between infected and sham. c Variableimportance and rank was determined from PLS-DA variable importance to projection (VIP) for the model S. aureus-treated versus sham-treated.

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data revealed significant differences (Figure 3F) indicating avalid model. Eleven metabolites were responsible for theobserved difference between sham and MRSA-treated mice,and are summarized in Table 2 with the variable importanceand rank for the PLS-DA model. Decreases were seen for allmetabolites that changed with MRSA infection, and included1-methylnicotinamide, 2-oxoisocaproate, 3-methyl-2-oxoval-erate, ethanolamine, fucose, N-isovaleroylglycine, taurine, andxylose as well as the gut-microbe associated metabolites3-indoxylsulfate, allantoin, and phenylacetylglycine.

Since 1-methylnicotinamide decreased with infection inMRSA-treated mice, NMR spectra were further examined todetermine whether the metabolites 1-methyl-2-pyridone-4-

carboxamide (2-Py), and 1-methyl-4-pyridone-5-carboxamide(4-Py), breakdown products of 1-methylnicotinamide, alsodecreased with infection. Since these metabolites were notpresent in the Chenomx database, identification was basedupon literature values for the chemical shifts.19 Figure 4A showsa comparison of three NMR spectral regions containing someresonances from 2-Py and 4-Py for sham and infected mouseurine. Comparison of the difference in spectral area betweensham- and MRSA- or S. pneumoniae-treated mice for thesemetabolites is illustrated in Figure 4B. Overall, a generaldecrease in metabolites belonging to this pathway was observedfor mice infected with MRSA, whereas an increase was seenwith S. pneumoniae infection.

To determine whether metabolite changes with infectionresolved over time, a longitudinal study was performed onsham- and S. pneumoniae-treated mice. PCA was performedon the targeted profiling data obtained from urine for each timepoint, and a metabolic trajectory plot obtained. In addition,phenotypic characteristics of the mice were followed through-out the experiment. Figure 5 shows that 6-36 h after infection,maximal separation between sham- and S. pneumoniae-infected mice could be observed. By 48 h, sham mice hadnearly identical urinary metabolite profiles to preinfected mice,and S. pneumoniae-treated mice nearly recovered to thepreinfected state. These results parallel the observed pheno-typic changes of the mice (Figure 5B). While the sham micebehavior changed only slightly (reflected in slightly less foodconsumed, and a bit less activity immediately after surgery),the infected mice were observed to become very ill, with a lossof appetite, lack of grooming, and a propensity to sleep in theirnests. These behaviors steadily declined from 6 h to a maxi-mum at 24-30 h, whereupon the mice started to recover, andwere observed to start eating more, and resume more activity.By 96 h post-treatment, the infected mice were indistinguish-able from the control mice with the same activity levels, eatingpatterns, and grooming behavior as before surgery. A summaryof the metabolite changes, and comparison between sham- andS. pneumoniae-treated mice, is shown in Figure 5C-E. Certainmetabolites were not significantly different between sham- andS. pneumoniae-treated mice throughout the experiment (Figure5B), although their concentrations changed from the prein-fected state. In particular, creatinine did not significantly differbetween sham and infected mice throughout the study. Somemetabolites were observed to change within 6 h of infection(citrate (and other TCA cycle intermediates (not shown)), andtrigonelline) (Figure 5D), while others changed after 18 h(creatine and taurine) (Figure 5C).

Discussion

This study demonstrates for the first time that urinarymetabolic profiles show changes correlating with lung infectionin an animal model of pneumonia. Since both experimentswere performed in different ways (intranasal versus intratra-cheal infection), and at different times, direct comparisonbetween MRSA and S. pneumoniae infection was not made.However, comparison of the metabolite changes in sham-versus pathogen-treated mice within each set of experimentsprovides an indication of mouse responses to infection.

It is not unexpected that we would observe differences inurinary metabolic profiles of mice with infection. Bourne etal.20 showed that NMR metabolomics could be used to dif-ferentiate and identify Streptococcus from Staphylococcus orEnterococcus simply from plate cultures. Furthermore, differ-

Figure 1. (A) Inflammatory infiltrates in BAL of mice infected withS. pneumoniae versus sham. (B) Lung histology of mice infectedwith S. pneumoniae compared with sham treatment. (C) Inflam-matory infiltrates in BAL of mice infected with MRSA versussham. (D) Lung histology of mice infected with MRSA comparedwith sham treatment. Histology sections are representative ofat least three different mice. Bar represents 100 µm at 20×, and50 µm at 40×.

Figure 2. Comparison of the 1H NMR spectral characteristics ofmetabolites measured in the urine of sham-treated, MRSA-treated, and S. pneumoniae-treated mice. Various metabolitesare indicated. (Abbreviations: DMG, N,N-dimethylglycine; TMA,trimethylamine; DMA, dimethylamine; MMA, methylamine.)

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ences in urinary profiles were seen in urinary tract infectionscaused by Pseudomonas aeruginosa or Klebsiella pneumoniaewhich could be directly related to pathogen metabolism,21,22

and differences in urinary metabolite patterns were found withinfection by different parasites.17,23

Although S. pneumoniae and S. aureus are both Gram-positive bacteria, it is generally agreed that the host immuneresponse to infection differs for both.24-27 Furthermore, thefull extent of the host-bacterial interaction for either pathogenis not fully understood. We observed significant increases inthe total cell count of mouse BAL fluid following infection, withnearly all of the cellular composition made up of polymorpho-nuclear leukocytes for the MRSA-infected mice, while ap-proximately equal numbers of macrophages and polymorpho-nuclear leukocytes were observed for the S. pneumoniae-infected mice. Indeed, it has been suggested that S. aureusR-toxin potentiates CXC chemokine-induced homing of neu-

trophils into the lung and airway,28 and that the immuneresponse to pneumococcal infection is initiated by macroph-ages, which become activated to promote an influx of neutro-phils.29

While both S. pneumoniae- and MRSA-infected mice haddifferences in their metabolite patterns when compared to theirsham counterparts, the metabolites that differentiated theirurinary metabolic profiles were profoundly different. The onlymetabolite that changed similarly upon infection for both S.pneumoniae and MRSA was an overall decrease in 3-indoxyl-sulfate. Other metabolites (ethanolamine, fucose and taurine)changed in opposite directions (increased in S. pneumoniae-infected mice and decreased in MRSA-infected mice). Perhapsthe most prominent difference in urinary metabolite profileswith infection was the large decrease in urinary TCA cycleintermediates with S. pneumoniae infection. This decrease wasnot observed in MRSA-infected mice. Indeed, this change in

Figure 3. (A) PCA model of urinary metabolite concentrations determined 24 h post-treatment of mice with either S. pneumoniae(SPN) (O, n ) 14) or sham (9, n ) 12) (R2 ) 0.779; Q2 ) 0.725). (B) PLS-DA model of urinary metabolite concentrations determined 24 hpost-treatment of mice with either S. pneumoniae (SPN) (O, n ) 14) or sham (9, n ) 12) (R2 ) 0.961; Q2 ) 0.868). (C) Model validationproviding an indication of the degree of correlation between the scrambled models and the original model. R2 (2) is the explainedvariance, and Q2 (gray 9) is the predictive ability of the model. (D) PCA model of urinary metabolite concentrations determined 24 hpost-treatment of mice with either S. aureus (MRSA) (O, n ) 9) or sham (9, n ) 9) (R2 ) 0.725; Q2 ) 0.196). (E) PLS-DA model of urinarymetabolite concentrations determined 24 h post-treatment of mice with either S. aureus (MRSA) (O, n ) 9) or sham (9, n ) 9) (R2 )0.909; Q2 ) 0.695). (F) Model validation providing an indication of the degree of correlation between the scrambled models and theoriginal model. R2 (2) is the explained variance, and Q2 (gray 9) is the predictive ability of the model.

Figure 4. (A) Comparison of 1H NMR urine spectra of the regions containing resonances corresponding to H2 and H4 from 2-Py (1-methyl-2-pyridone-5-carboxamide) and 4-Py (1-methyl-4-pyridone-5-carboxamide) for sham-, S. pneumoniae-, and MRSA-treated mice.(B) Comparison of the percent change in median spectral area for some metabolites in the nicotinamide metabolic pathway betweensham-treated and either S. pneumoniae- (black bars) or MRSA-treated (white bars) mice.

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the S. pneumoniae-infected mouse urine is likely to be relatedsolely to a host response since S. pneumoniae lacks the genesexpressing TCA cycle enzymes, and is therefore unable togenerate these intermediates.30 It is known that pneumolysin,a toxin released by S. pneumoniae, can introduce pores intocell and mitochondrial membranes, thereby causing leakageand altering mitochondrial potential.31 This may, in part,explain the decrease in TCA cycle intermediates observed inthe urine of S. pneumoniae-infected mice.

Fucose, ranked second in importance for differentiationbetween sham and S. pneumoniae-infected mice, increased inthe urine by 176% upon infection with S. pneumoniae. Thegenome of S. pneumoniae contains a fucose operon that isinduced by fucose and repressed by glucose or sucrose.32 Thisoperon controls several genes with homologies to fucosecatabolism genes in Gram-negative bacteria, as well as somewith unknown function.32 Fucose has been shown to beimportant for host-microbe interactions,33,34 and fucosylatedglycoconjugates have been implicated in pathological pro-cesses.33 Indeed, it has been shown that SpGH98, a predictedS. pneumoniae extracellular protein that is encoded within the

fucose operon, has the ability to recognize host glycans bearingthe ABH blood group antigen as well as the Lewisy antigen,and specifically binds fucose.35 Indeed, it is interesting tospeculate that SpGH98 and the fucose operon are involved inthe release of fucosylated host glycans, which then may berecycled by the bacterium.35 Furthermore, free fucose has beenshown to play a role in the activation of genes involving theinflammatory response including TNF-R, TLR2, and genesdownstream of TLR2 in the TLR2-signaling pathway, which areknown to affect the human intestinal epithelium.36 Indeed, wehave shown massive increases in fucose concentration in theurine of mice with the development of inflammatory boweldisease.8 Changes in concentration of known gut-microbemetabolites with S. pneumoniae infection (3-indoxylsulfate,hippurate, and trimethylamine) support this hypothesis.

Another interesting aspect of S. pneumoniae pathogenesisis the requirement of choline. Choline is covalently attachedto TA where it anchors choline-binding proteins that areimportant virulence factors.37 S. pneumoniae also containsPotD, a polyamine transporter that may function to increaseintercellular choline as well as intracellular polyamines (such

Figure 5. (A) Score plot obtained from PCA of metabolite concentrations obtained from sham- (9) or S. pneumoniae (O)-treated miceover different time intervals. Each point represents the average position of all points in each group and time-interval with standarderror of the mean indicated. Pretreatment is the average of urine samples collected at -6 h, and immediately prior to infection; Post-treatment samples included those collected at 6 and 12 h; 18, 24, and 36 h; and 48 h after treatment with either sham or S. pneumoniae.(B) Health scores of sham (black bars), and S. pneumoniae (white bars) treated mice over time. Scores represent the average of thesum of activity levels (0 (no activity) to 4 (normal activity)), appearance (0 (untidy, hatched hair) to 4 (healthy, shiny hair)), and appetite(0 (no appetite) to 4 (normal appetite)) calculated for each mouse. (C-E) Column graphs indicate average metabolite concentrationdifferences between sham mice (clear bars) and mice infected with S. pneumoniae (gray bars). Error bars reflect the averageconcentration among 3-8 mice. Metabolites shown are those that (C) remain similar for both groups, (D) increase in concentrationwith infection, and (E) decrease in concentration with infection.

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as ethanolamine) which may substitute for choline underconditions of choline limitation.38 This may explain the de-creased concentration of betaine, a primary oxidation productof choline, as well as the increased concentration of ethanol-amine. With decreased choline levels, the potential side effectmay be to impact liver function.39,40 Decreased TCA cycleintermediates (2-oxoglutarate, citrate, trans-aconitate, fumarateand succinate), increased nicotinamide metabolism (1-meth-ylnicotniamide, 2-Py, and 4-Py), an altered pyramidine pathway(N-carbamoyl-�-alanine and uracil), as well as changes intaurine, creatine, glycine, glycolate, lactate, and formate me-tabolite patterns may all be related to changes in hepaticcellular function in the host.

Other support for these hypotheses come from the metabo-lite changes induced by pulmonary infection by MRSA. MRSAdoes not contain a fucose operon, nor does it have a require-ment for choline,41 and other than a decrease in the amountof 3-indoxylsulfate in the urine has no other similarities in urineprofile to S. pneumoniae. However, staphylococcal enterotoxinB has been shown to directly inhibit cytochrome P450, whichis involved in oxidative phosphorylation. Cytochrome P450 isinvolved with nicotinamide metabolism and is generally en-riched in hepatic tissues.42,43 With inhibition of cytochromeP450, it is expected that decreases of metabolites involved withnicotinamide metabolism (1-methylnicotinamide, 2-Py, and4-Py) as well as other compounds, such as 2-oxoisocaproate,3-methyl-2-oxovalerate, N-isovalerylglycine, and taurine, shouldbe observed. Since ethanolamine, fucose, and xylose aremetabolized in the liver,44-46 decreases in the level of thesemetabolites may indicate that the urinary response observedmay be related with altered liver function, although more workneeds to be done to prove this hypothesis. Decreases in fucoselevels may result in gut microbes perturbations that may, inturn, result in changes in the concentrations of 3-indoxylsulfate,phenylacetylglycine and allantoin.

With S. pneumoniae pulmonary infection, it is apparent thatas mice recover, their urinary profiles recover to baseline levels,with no permanent metabolite changes. This result is remark-able as it suggests that the urinary profile is a snapshot of thecurrent status of an organism. These results are particularlyimportant, as they are the first step to a deeper understandingof the infectious process in the lung by various pathogens, andoffer a potential method to understand response to therapeu-tics. Ongoing studies investigating the changes in varioustissues and mitochondrial metabolism in the liver and kidneyas a result of infection and/or toxin exposure should allow usto enhance our knowledge of the effect of individual virulencefactors.

In conclusion, we have profiled the urinary metaboliteresponse of S. pneumoniae and MRSA lung infection in mice,and found distinct changes in the profile with infection. Theresults obtained here exemplify the fact that each pathogenhas a unique mechanism of pathogenesis with correspondingeffects on the immune system that can be readily monitoredover time noninvasively. The insights obtained into the biologi-cal response could provide a basis for novel drug discovery andindicate that we may be able to find novel diagnostic tools forhumans. This investigation further demonstrates the usefulnessof metabolomics for investigating host metabolic responses tolung infection, and for monitoring disease development andresolution.

Acknowledgment. This research was funded in partby an establishment grant from Alberta Heritage Foundationfor Medical Research, by operating grants from theUniversity Hospital Foundation, and The Lung Associationof Alberta and the Northwest Territories. This work was alsosupported by Western Economic Development, and AlbertaAdvanced Education and Technology. We would like tothank Chenomx, Inc. for software support, Dr. Gregory J.Tyrrell for bacterial cultures and training of A.C., and Dr.Lakshmi Puttagunta for help with mouse lung tissuehistology and histopathology. We are also grateful to ShanaRegush, Chris St. Saurent and James Dooley for technicalsupport.

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