the emergence of multiple antibiotic - resistant 
 gram - positive and 
 gram - negative bacteria in hospital and community settings requires 
 novel approaches for rapid diagnosis and treatment . the clinical 
 diagnosis of bacterial infections is currently still mostly based 
 on recovering and culturing of microorganisms from patients 
 blood or other bodily fluids . molecular tests to detect infectious 
 agents are increasingly being used in many clinical laboratories ; 
 however , they have not yet become part of routine practice . the nonmolecular 
 diagnostic tests lack diagnostic accuracy , are time - consuming , labor - intensive 
 and can give erroneous information , particularly in immunodeficient 
 patients . most importantly , the tests 
 are not sensitive , and as such at least 50% of bacterial infections 
 go undetected by state of the art clinical methods . these problems 
 have led to an emerging interest in identifying disease specific biomarkers 
 or other physiological indicators that can predict the severity and 
 the nature of bacterial infections . moreover , collectively , such biological 
 indicators can potentially guide the selection of the optimal antibiotic 
 treatment . for example , recently , procalcitonin , 
 a peptide hormone and cytokine that is mainly produced in response 
 to bacterial infections , was found to be a convenient diagnostic marker 
 to distinguish between bacterial and viral infections . however , a single biomarker has major limitations and is often equivocal . 
 moreover , to date , the differentiation between infections caused by 
 different bacterial strains is still challenging yet would certainly 
 aid physician treatment decisions in recent years , the focus 
 for finding novel diagnostic tools has 
 moved from single disease - specific markers to bioprofiles or biosignatures 
 comprising a well - defined set of reliable molecular indicators using platforms such as proteomics , transcriptomics , genomics , and metabolomics . metabolomics allows researchers to characterize 
 and quantify a plethora of low molecular mass metabolites from biofluids 
 such as urine , serum , and cerebrospinal fluid . like the other technologies , metabolomics also relies on systematic data analysis 
 using multivariate pattern recognition techniques . nmr metabolomics 
 has been shown to produce highly quantitative and reproducible results . in previous studies aimed at developing new 
 methods for early diagnosis of bacterial infections , the nmr - metabolomics 
 approach has already been applied to growing bacterial cultures as well as for the differentiation between bacterial 
 and viral meningitis . furthermore , different 
 bacterial lower respiratory tract infections have been investigated 
 in mouse models and human patients as well . metabolomics offers a unique approach to characterize all 
 the metabolic 
 changes that occur in an organism in response to biological and toxicological 
 stimuli . in the case 
 of infectious diseases , the metabolic profiles of bodily fluids are 
 expected to reflect both the molecules generated by the innate defense 
 response as well as metabolites that are secreted by the pathogens . in addition , host cellular events , such as altered energy metabolism , 
 which are a direct result of the disease , will occur in parallel . the pathogenicity of bacteria is usually caused by bacterial molecular 
 elements such as toxins , exoenzymes , adhesins , as well as immune - modulating 
 proteins that are released . many of these pamps are recognized by 
 the toll - like receptor transmembrane protein family which plays a 
 key role in mediating the systemic responses to invading pathogens . in in vitro studies specific 
 ligands have been identified for different tlrs such as gram - negative 
 bacterial lipopolysaccharide for tlr4 and lipoteichoic acid from the 
 gram - positive cell wall for the tlr2/tlr6 heterodimer . while the tlr4-dependent recognition of lps is very well - established 
 as the central line of host defense against invading gram - negative 
 pathogens , there is now growing evidence that lta may in fact activate 
 additional anti - inflammatory properties and pathways . consequently , rather than using lta , synthetic peptides such as 
 the tlr1/tlr2 agonist pam3cys and the tlr2/tlr6 agonist 
 malp2 are used instead to activate these tlrs . since gram - negative 
 and gram - positive bacteria act on different 
 receptors , we can hypothesize that the host metabolic response will 
 be distinct in each case . therefore , in this work we have analyzed serum from mouse 
 models of bacterial infections ( staphylococcus aureus , streptococcus pneumoniae , escherichia 
 coli , and pseudomonas aeruginosa ) and examined 
 low - molecular - weight pathogen - specific biomarkers through nmr metabolomics . an extensive array of physiological and immunological data was measured 
 to confirm that a systemic infection was established in all cases . 
 to differentiate the metabolites according to their origin , we compared 
 the results of the in vivo studies with those obtained in bacterial 
 culture experiments . by comparing serum metabolite changes resulting 
 from lps or malp2 treatment of the mice and from studies of tlr4 and 
 tlr2 knockout mice we could identify those metabolites generated by 
 the host response . finally , we incorporated all the metabolic , physiological , 
 and immunological data into one descriptive model of disease , which 
 showed good correlations between the various parameters . experiments were performed with two 
 gram - positive bacterial strains ( streptococcus pneumoniae ( spn 15814 strain ) and staphylococcus aureus ( xen29 ) ) 
 and with two gram - negative bacteria ( pseudomonas aeruginosa ( pa01 ) and escherichia coli ( xen14 ) ) . both the e. coli and the s. aureus strain ( caliper 
 life sciences , hopkinton , ma ) possess a stable copy of the phosphorhabdus luminescens lux operon abcde on the bacterial 
 chromosome which allowed us to monitor the progression of the infection 
 by bioluminescence .   prior to analysis , frozen stock suspensions 
 of bacteria were cultured overnight in 5 ml of luria - bertani medium 
 ( s. aureus , p. aeruginosa , e. coli ) or brain - heart infusion broth ( s. pneumoniae ) at 37 c with shaking at 200 rpm . to guarantee strain selectivity , media contained kanamycin in an appropriate concentration ( 200 g / ml 
 for s. aureus and 30 g / ml for e. coli ) . for the bacterial foot- and fingerprint analysis , 1 ml of the 
 overnight culture was reinoculated into 50 ml of the corresponding 
 culture broth , and bacteria were cultured in 500 ml flasks under the 
 same conditions up to an od600 ( optical density at 600 nm ) of 0.6 . 
 for each bacterial strain for 
 the in vivo experiments in mice , 1 ml of the overnight culture was 
 reinoculated into 5 ml of medium and incubated for 35 h at 
 37 c with shaking at 250 rpm . the cell concentration was determined 
 by measuring the od600 spectrophotometrically and by counting the 
 colony - forming units ( cfu ) . a bacterial sample was serially diluted , 
 and a known volume was plated either onto lb or bhi agar . the cell 
 concentration was obtained by counting the number of colonies that 
 had formed . for each bacterial culture a total amount of 50 ml was collected 
 in the exponential phase ( od600 = 0.6 ) on separate 0.22 m filters . immediately after the disappearance of the culture 
 medium the unwashed filters were transferred to liquid nitrogen . subsequently 
 each filter was crushed into separate conical tubes and was suspended 
 in 5 ml of 100% cold methanol which was cooled to below -40 c 
 in a dry ice - ethanol bath . three freeze - thaw cycles were applied to 
 break the cell wall and to release the intracellular metabolites . the lysates were centrifuged at 4000 g for 15 min 
 at 4 c , and the supernatants were transferred into a clean tube . the metabolites were purified by adding 5 ml of water and 10 ml of 
 chloroform and shaking for 5 min . the water - methanol phase contained 
 the metabolites and was separated from the lipophilic chloroform - phase 
 by centrifugation for 15 min at 4000 g . the metabolite 
 solution was transferred to a clean tube , and 1 ml was aliquoted into 
 1.5 ml tubes 3. the samples were dried in a speed - vacuum evaporator , 
 resuspended in 500 l of d2o , and stored at 20 
 c . after bacterial cells were separated by filtration , the 
 filtrate was stored at -20 c until the nmr measurements were 
 performed . experiments were performed on 5 to 8 weeks - old 
 ( 20 - 35 g ) male mice . tlr4-/- mice and c57bl/6 mice were purchased 
 from the jackson laboratory ( bar harbor , me ) . all procedures performed were approved by the 
 university of calgary animal care committee and were in accordance 
 with the canadian guidelines for animal research . bacterial 
 cultures in the exponential phase ( see bacteria 
 and growth conditions ) were centrifuged for 10 min at 3000 
 rpm and washed twice with saline , and the cell concentration of the 
 bacterial suspension was adjusted by spectrophotometry at 600 nm . 
 to induce the different bacterial infections in mice , animals were 
 injected intraperitoneally with 1 ml of the bacterial suspensions 
 adjusted to the following concentrations : s. aureus : 1  10 cfu / ml ; s. pneumoniae : 
 1  10 cfu / ml ; p. aeruginosa : 1 
  10 cfu / ml ; e. coli : 1  10 cfu / ml . for p. aeruginosa concentrations 
 of 7  10 cfu / ml , 8  10 malp2 and lps 
 ( lps from escherichia coli o111:b4 ) were purchased 
 from alexis biochemicals ( enzo life sciences , inc . , plymouth meeting , 
 pa ) and list biologicals ( burlington , ontario , canada ) , respectively . malp2 ( 5 g ) was dissolved in 200 l of saline and intraperitoneally 
 injected into wild - type and tlr2-/- mice . for the corresponding lps 
 experiment , 200 l of saline containing 0.5 mg of lps / kg mouse 
 were administered in wild - type and tlr4-/- mice . control mice received 
 200 l of saline ip . prior to administration of bacteria , lps , malp2 
 or saline and 24 h post administration , body weight ( metler toledo , 
 columbus , oh ) was determined and calculated as weight loss normalized 
 to the primary body weight . twenty - four hours 
 after treatment and infection , mice were anesthetized by inhalation 
 with isoflurane ( bimeda - mtc , cambridge , ontario , canada ) , and 500 
 l of blood was collected via cardiac puncture . circulating 
 leukocyte counts were determined with a bright - line hemocytometer 
 ( hausser scientific , horsham , pa ) . blood smears were stained with 
 wright - giemsa ( vwr , edmonton , alberta , canada ) and leukocyte differentials 
 determined from a count of 100 cells . the remaining blood volume was 
 allowed to sit at room temperature for 10 - 30 min to clot and spun 
 down at 5000 rpm for 10 min , and the top layer of serum was collected 
 and frozen at -20 c to be used later for metabolite and cytokine 
 measurements . after the collection of blood , mice 
 were sacrificed , and a peritoneal lavage was performed using 3 ml 
 of warm saline . the exudate was recovered following a 60 s gentle 
 manual massage , and leukocytes were counted with a hemocytometer . from sacrificed mice , mpo 
 is a myeloid cell - specific enzyme in neutrophils , and its activity 
 was measured to determine the neutrophil influx into the lung . lung tissues were prepared in 96-well plates and processed using 
 hexadecyltrimethylammonium bromide ( for enzyme extraction ) and dianisidine - h2o2 ( as the colorimetric substrate ) ( sigma , st . the change in absorbance at 
 450 nm in the 96-well plates was determined over 60 s using a kinetic 
 microplate reader ( molecular devices , sunnyvale , ca ) . at 24 h after inoculation mice were sacrificed 
 and lungs were collected and homogenized in 1 ml of sterile pbs . bacterial 
 cfu were determined by culturing serial dilutions on lb agar plates 
 overnight at 37 c . the analysis of the cytokines 
 and chemokines in mouse serum was performed with a bioplex 8 plex 
 immune assay kit ( il-6 , il-10 , g - csf , kc , mcp-1 , mip-1a , rantes , tnfa ) 
 from biorad ( herkules , ca ) according to the manufacturer s 
 specifications . the plate was read on a luminex 200 apparatus ( applied 
 cytometry systems , uk ) . the acquisition and analysis were performed 
 with the starstation v.2.3 software from applied cytometry systems . all of the serum samples from 
 mice as well as the samples for footprint and fingerprint - analysis 
 were filtered in 3-kda - cutoff spin columns ( 3 k nanosep : omega ; vwr , 
 edmonton , alberta , canada ) , and filtered protein was rinsed using 
 an additional 100 l of d2o . prior to use , filters 
 were washed 5 times with water and then d2o to remove the 
 glycerol conservation agent . the final sample volume ranged from 300 
 to 500 l and was transferred to clean microfuge tubes . samples 
 were brought to a total volume of 650 l by addition of d2o , 40 l of sodium azide ( 1 m nan3 solution ) , 
 and 140 l of phosphate buffer ( 500 mm nah2po buffer 
 solution at ph 7.0 ) containing 2.5 mm of 2,2-dimethyl-2-silapentane-5-sulfonate 
 ( dss ) as an internal chemical shift and concentration standard , and 
 the ph was adjusted to 7.00  0.05 . all nmr experiments were acquired on 
 a bruker avance 600 spectrometer equipped with a 5 mm txi probe ( bruker , 
 milton , ontario , canada ) at 298 k. the 1d h nmr spectra 
 were obtained using a standard bruker 1d nuclear overhauser enhancement 
 spectroscopy ( noesy)-presaturation pulse sequence ( noesypr1d ) in which the residual water peak was irradiated during the relaxation 
 delay of 1.0 s and during the mixing time of 100 ms . for an overall 
 recycle time of 5 s an acquisition time of 2 s and an initial relaxation 
 delay of 3 s was used . a total of 2048 and 1024 scans were collected 
 for samples of mouse serum and culture experiments , respectively . the spectra were manually corrected for phase and baseline distortions 
 within topspin ( bruker biospin , ltd . ) and were referenced to the dss 
 resonance at 0.0 ppm . additional two - dimensional nmr experiments such 
 as total correlation spectroscopy ( 2d h - c 
 tocsy ) and heteronuclear single quantum coherence spectroscopy ( 2d h - c hsqc ) were performed for chemical shift assignments 
 and verification . 1d h nmr 
 spectra were imported into chenomx nmr suite version 4.6 ( chenomx 
 inc . , edmonton , canada ) for metabolite identification and quantification 
 by targeted metabolite profiling analysis in the profiler module which 
 is linked to a library representing over 260 metabolite entries . to determine the concentration of individual 
 compounds the concentration of dss was used as reference . each compound 
 concentration was then normalized to the sum of all concentrations , 
 excluding the two highest concentrated metabolites , glucose and lactate , 
 which otherwise would dominate the normalization . a total of 43 metabolites were profiled in serum spectra . 
 for the fingerprint and footprint spectra the final data set used 
 for the multivariate analysis consisted of eight wild - type c57bl/6 
 mice for each bacterial infection ( s. aureus , s. pneumoniae , e. coli , p. aeruginosa ) , eight wild - type mice with lps injection , seven wild - type mice 
 with saline , and six mice with malp2 treatment . four tlr4-/- mice 
 were injected with either e. coli or lps , and four 
 tlr2-/- mice were infected with s. aureus and treated 
 with malp2 , respectively . for control experiments , four saline - treated 
 knockout mice were used for each group . for foot- and fingerprint 
 analysis , six samples per bacterial strain were prepared , respectively , 
 using two biological duplicates . chemometric analysis was performed 
 using simca - p version 11.5 ( umetrics , umea , sweden ) and both supervised 
 ( partial least - squares , partial least - squares discriminant analysis , 
 orthogonal partial least - squares discriminant analysis ) , and unsupervised 
 ( principle component analysis ) projection techniques were applied 
 to the different data sets . pca models were applied to each 
 treatment - control data set as well as to all serum samples collected 
 from s. aureus , s , pneumoniae , e. coli , and p. aeruginosa infected mice . 
 as an unsupervised method , pca defines the major sources of variance 
 without over interpretation and allows outliers to be identified . to reveal differences in the metabolomic profiles 
 which may be masked by pca using all data points , supervised pls - da 
 modeling was applied using a binary variable as class - membership for 
 different bacterial infections and treatments . by opls - da an 
 orthogonal projection was conducted for each individual 
 bacteria - host and footprint - medium data set . however these supervised 
 models may also contain variation that is not class - related and which 
 can be avoided applying pls analysis to the h nmr spectral 
 information with additional orthogonal class - characteristics . here we conducted pls analysis using a metabolomic matrix x and an identity - matched matrix y containing a set of 
 physiological - immunological data in order to find connections between 
 the two sets of data . in our calculations , we used a total loadings 
 set consisting of eight physiological ( weight , wbc counts , lymphocytes , 
 monocytes , neutrophiles , eosinophiles , mpo , leukocytes in peritoneal 
 lavage ) , eight immunological parameters ( il-6 , il-10 , g - csf , kc , mcp-1 , 
 mip-1a , rantes , tnfa ) , and 43 quantified metabolites . r2 is the 
 goodness - of - fit parameter , the percentage of variation explained in 
 the data , and q2 the goodness of prediction parameter based on a 7-fold 
 cross validation approach . to establish a reliable mouse model , s. aureus infections were induced with the bioluminescence xen29 strain in 
 wild - type c57bl/6 mice by intraperitoneal ( ip , 1  10 cfus ) , intravenous ( iv , 1  10 cfus ) , and subcutaneous 
 ( sc , 1  10 cfus ) administration . their progression 
 was monitored by bioluminescence as well as by nmr - based metabolomics . 
 over the course of 24 h , the intensity of the bioluminescence changed 
 significantly when following the concentration of detectable pathogens 
 located closely to the skin surface ( figure 1a ) . the fast systemic distribution of bacteria after intraperitoneal 
 invasion led to a loss in intensity after 24 h compared to 4 h , while 
 for the subcutaneous injection both an increase in bacterial density 
 as well as an expanded infected skin area developed . because of immediate 
 dispersion in blood after intravenous administration , much of the 
 bacteria was rapidly cleared and no bioluminescence signal could be 
 detected in these mice after 4 or 24 h. after bioluminescence monitoring , 
 serum was collected by cardiac puncture , and 43 metabolites were identified 
 and quantified by h nmr and compared with those detected 
 in controls ( untreated , n = 14 ; sham infected with 
 1 ml of saline ip injected , n = 7 ) using multivariate 
 statistical pattern recognition techniques . principle component analysis 
 ( pca ; three components , r2 = 0.48 , q2 = 0.25 ) scores plots revealed 
 complete class distinction in the second component between infected 
 and healthy subjects ( figure 1b ) . no separation 
 was observed between untreated animals and those having received an 
 ip injection of 1 ml saline . however in the group of the infected 
 subjects , serum samples of infected mice having the same site of infection 
 grouped together in the first component according to the similarities 
 in their metabolomic profiles . additionally , cfu counts in lung tissue 
 confirmed reliable systemic progression of the infection for both 
 the intraperitoneal and intravenous routes of administration . since 
 ip invasion of bacteria is one of the more clinically relevant routes 
 of infection leading to life threatening septicemic conditions in 
 patients , the ip infection model was utilized for all further investigations . comparison 
 of intravenous ( iv ) , intraperitoneal ( ip ) , and subcutaneous 
 ( sc ) administration of s. aureus . ( a ) s. 
 aureus xen29 possessing a stable copy of the luxabcde operon 
 was imaged to study the redistribution of bacteria after ip and sc 
 injection . while there 
 appears to be little redistribution from the initial injection site 
 after sc injection , good redistribution is observed following ip injection . ( b ) pca analysis ( three components , r2 = 0.48 , q2 = 0.25 ) of serum 
 of s. aureus infected mice and controls . the analysis 
 shows that the three routes of administration ( sc , ip , iv ) give comparable 
 results when compared against control mice and mice injected with 
 saline . in particular , ip and sc administration show good agreement 
 with each other . nontreated mice and saline injected mice behaved 
 in a very similar manner . in our study , we investigated host - microbe interactions 
 using two 
 gram - positive and two gram - negative organisms : s. aureus , s. pneumonia , e. coli , and p. aeruginosa . the bacterial dose was adjusted to 10 cfu / ml for s. aureus , s. pneumoniae , and e. coli and 10 cfu / ml for p. aeruginosa and resulted in a survival rate of 100% on 
 day one post injection . twenty - four hours post bacteria administration , 
 mice ( n = 8 for each kind of infection ) showed cfus 
 in the lungs ( results not shown ) as a result of a systemic progression 
 of the bacterial infection . additionally , mice were examined according 
 to their health condition and status of inflammation by comparing 
 body weight , myeloperoxidase ( mpo ) activity in the lungs , white blood 
 cell counts in blood and peritoneal lavage , as well as cytokine / chemokine 
 levels ( il-6 , il-10 , g - csf , kc , mcp-1 , mip-1a , rantes , tnfa ) in serum 
 ( figure 2 ) . the data were compared with sham 
 mice ( ip injection of 1 ml of saline , n = 7 ) and 
 animals having obtained an intraperitoneal injection of lps ( n = 8) and malp2 ( n = 6 ) . for lps and malp2 , a dose of 0.5 mg / kg and 5 g / mouse dissolved in a volume of 
 200 l saline was applied , respectively . twenty - four h after 
 infection , some animals showed signs of diarrhea and stopped eating 
 and drinking , which resulted in a higher weight loss compared to the 
 control group ( figure 2a ) . all infected mice 
 developed to a similar extent significantly elevated leukocyte counts 
 in the peritoneal lavage ( p < 0.01 ) as well as 
 increased mpo activity ( p < 0.05 ) in the lungs 
 as a result of neutrophilic influx while white blood counts in blood 
 did not change significantly compared to the controls . in addition , 
 animals showed enhanced serum levels of different cytokines ( il-6 , 
 il-10 , g - csf , and tnfa ) and chemokines ( kc , mcp-1 , mip-1a , rantes ) 
 demonstrating that an intense systemic inflammatory response has developed 
 ( figure 2b ) . extremely high cytokine and chemokine 
 concentrations were found for s. aureus and e. coli infections and for mice having been treated with 
 lps . taken together , these data show that in all cases a systemic 
 infection was obtained . although the responses did vary in magnitude 
 with s. aureus and e. coli having 
 the highest responses , the responses for all bacteria , lps and malp2 
 were elevated in a similar direction . physiological and immunological conditions 
 in response to bacterial 
 infections and to lps and malp2 administration , 24 h post - infection . 
 c57bl/6 wild - type 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 . ) ( a ) weight 
 loss , mpo activity from lung tissue and leukocyte counts from both 
 blood and peritoneal lavage . significance of paired t tests on differences in physiological parameters between controls 
 and infected mice ( 1 ) p < 0.01 , ( 2 ) p < 0.002 , ( 3 ) p < 0.05 . white blood cell counts 
 did not differ significantly between the control group and infected 
 mice . ( b ) levels of cytokines and chemokines in mouse serum . to determine whether the 
 metabolomic profiles of different infections were sufficiently unique 
 to facilitate the identification of bacteria specific biomarkers , 
 supervised multivariate statistical modeling was applied to a set 
 of 43 identified serum metabolites obtained from s. aureus , s. pneumoniae , e. coli and p. aeruginosa infected mice . metabolites that were identified 
 included several intermediates of the tricarboxylic acid ( tca ) cycle , 
 glycolysis , as well as fermentation and lipid precursor metabolites . in addition , ketone bodies , amino acids , as well as carboxylic acids 
 which were caused by direct cellular events were detected . some metabolites 
 such as taurine are related to the innate immune system and released 
 by the polymorphonuclear cells , while others derive from bacteria 
 in the gut and at the site of infection . a full list of metabolites 
 and their biological relevance is summarized in table 1 of the supporting information . using pls - da , a 
 clear separation between the four bacterial strains was obtained ( figure 3a ) . three components of the pls - da analysis encapsulated 
 71% of the interclass variation ( r2 ) with a corresponding cross - validation 
 accuracy of 56% ( q2 ) . coefficients of metabolites for different bacterial 
 infections showed that each infection was correlated to specific changes 
 in certain metabolic intermediates ( table 2a  d , supporting information ) . mice infected with s. aureus showed extremely enhanced serum levels of metabolites 
 related 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 were 
 identified as negative contributors , and tca cycle intermediates ( 2-oxoglutarate , 
 citrate , and fumarate ) as well as glucose and pyruvate showed the 
 highest serum levels compared to all infections investigated . hippurate , 
 a carboxylic acid and endogenous conjugate which is not further metabolized 
 but actively secreted by tubular cells and excreted in urine , became a metabolite of special interest as its 
 serum concentrations rose by almost 100% during the pneumococcal infection . it is produced by condensation of benzoic acid and glycine in the 
 mitochondria of liver and kidney , and its synthesis 
 is stimulated by metabolic acidosis . it belongs to the group of uremic 
 toxins , and its enhancement in urine is often used as an indicator 
 of intrahepatic tracer dilution to determine the activity of specific 
 human enzymes . therefore , unlike the cytokines 
 and chemokines which simply indicated indiscriminate inflammation , 
 metabolites highlighted striking differences . scores plots representing h nmr spectral data from 
 serum of c57bl/6 wild - type mice infected with four different bacterial 
 strains . 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 ) showing 
 differentiation between the four bacterially infected mouse models 
 ( three components ; r2 = 0.71 , q2 = 0.56 ) . the 3d plot represents similarities 
 along the third principle component for e. coli and p. aeruginosa infected groups and along the x  y 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 ) . as for the gram - negative bacterial strains , the 
 distinction between 
 the metabolic profiles of e. coli and p. 
 aeruginosa infections was mainly based on changes in amino 
 acid blood levels , whereas varying concentration changes of energy 
 metabolites were observed . besides significant distinction between 
 different bacterial infections , 
 similarities also became obvious in the 3d pls - da scores plot ( figure 3b ) . the clustering of s. aureus and s. pneumoniae infected mice showed similar 
 metabolite changes in the third principal component while similar 
 values for the first and second principal component along the x  y axis , 
 were obtained for e. coli and p. aeruginosa infections . using the supervised opls - da ( orthogonal partial least - squares 
 discriminant analysis ; two components , r2 = 0.81 , q2 = 0.59 ) analysis 
 ( figure 3c ) , serum samples of mice infected 
 with gram - positive and gram - negative bacteria clustered as two separate 
 groups . five of 10 metabolites that were identified as significant 
 positive contributors in the class of gram - positive bacterial infections 
 ( table 2e , supporting information ) were 
 glycolysis and tca cycle intermediates such as glucose , pyruvate , 
 citrate , fumarate , and 2-oxoglutarate . amino acids , in contrast , were 
 found in higher levels in blood of mice infected with gram - negative 
 bacteria . taken together , these results demonstrate that different 
 bacterial 
 infections give rise to individual metabolomic profiles with overlapping 
 similarities in metabolite classes according to their gram nature . to investigate 
 whether some of the metabolites which were identified as positive 
 contributors for s. aureus , s. pneumoniae , e. coli , and p. aeruginosa infections 
 in vivo were derived directly from bacterial metabolism , we conducted 
 in vivo experiments with bacterial cell cultures . by metabolic footprinting 
 ( extracellular bacterial metabolites released into the culture medium ) 
 and fingerprinting ( intracellular metabolites , extracted by organic 
 solvents ) we defined the pattern of extra- and intracellular bacterial 
 metabolites , respectively . in our experiments , bacteria were cultured 
 to an od600 of 0.6 in either lb ( s. aureus , p. aeruginosa , e. coli ) or bhi ( s. pneumoniae ) medium and were separated subsequently using 
 the filtration method ( 0.2 m ) . intracellular metabolites were 
 extracted by cold methanol , and nmr data were collected from both 
 extracts ( fingerprints ) and culture media ( footprints ) . in these spectra 
 we identified and quantified 39 metabolites including amino acids , 
 alcohols , and organic acids . since cultivation of the different bacterial 
 strains required different culture media , we compared the metabolic 
 footprint of each pathogen directly with that of the corresponding 
 culture medium using opls - da . in s. pneumoniae , s. aureus , and e. coli cultures , metabolite 
 levels linked to pathways of energy supply increased significantly 
 during cell growth , while most of the amino acid concentrations decreased 
 and were taken up by the bacteria to be used for protein , dna / rna , 
 and cell wall synthesis . for p. aeruginosa , 
 in contrast , metabolomic footprints displayed increased concentrations 
 for a variety of different amino acids , while positive contributors 
 derived from anaerobic or aerobic energy metabolism were not discovered . 
 a comparison of the metabolic profiles with those from serum of p. aeruginosa infected mice showed similarities for a large 
 number of metabolites ( figure 4a ) , indicating 
 that p. aeruginosa secretes specific metabolites 
 which contribute directly to the serum metabolome . in subsequent experiments , p. aeruginosa suspensions were injected at different concentrations 
 ( 7  10 , 8  10 , 1  10 , 5  10 cfu / ml ) to investigate the effects 
 of increasing bacterial load and disease stages in the mouse model . even though mpo and leukocyte counts in the peritoneal lavage increased 
 significantly beginning with a bacterial concentration of 1  
 10 cfu / ml , no significant changes in wbc or severe disease 
 symptoms as illustrated by the neutrophil - lymphocyte ratio in figure 4d were induced for the first three bacterial concentrations . 
 however , serum levels of the five significantly elevated metabolites 
 tryptophan , lysine , threonine , valine , and phenylalanine continuously 
 increased in serum with higher number of bacteria . this indicates 
 that these metabolites may be released by the bacteria as bacterial 
 footprints into the bodily fluids ( figure 4b ) . on the other hand , a more profound host response was observed 
 when a p. aeruginosa suspension with a concentration 
 of 5  10 cfu / ml was applied . at 24 h post administration , 
 these mice developed signs of severe sickness ( decreased wbc , increased 
 neutrophil - lymphocyte ratio ) and also experienced decreased serum 
 amino acid levels ( figure 4b ) as well as elevated 
 serum concentrations of ethanol , taurine , and creatine ( figure 4c ) and a mortality rate of 50% . bacterial exometabolome 
 and metabolic host response to p. aeruginosa infected 
 c57bl/6 wild - type mice 24 h post - infection . some of the elevated serum metabolites of p. aeruginosa infected mice were identified as part of the bacterial exometabolome . ( a ) coefficient plot generated from opls - da analysis of the samples 
 from the p. aeruginosa mouse model and control animals 
 representing the relative contribution that each metabolite makes 
 to the distinction between the two classes ( two components ; r2 = 1.00 , 
 q2 = 0.99 ) . highlighted metabolites ( red circles ) were elevated both 
 in serum samples of infected mice and in the footprints of p. aeruginosa cultures grown in lb medium . tryptophan , lysine , 
 threonine , valine , and phenylalanine were significantly elevated both 
 in vivo and in the bacterial culture media ( p < 
 0.05 ) . ( b ) metabolite levels of tryptophan , lysine , threonine , valine , 
 and phenylalanine found in serum of mice infected with p. 
 aeruginosa concentrations of 7  10 , 8  
 10 , 1  10 , and 5  10 cfu / ml . bar charts showing ( c ) metabolite levels of taurine , ethanol , 
 and creatine and ( d ) blood leukocyte counts from serum of p. aeruginosa infected mice as a function of different bacterial 
 concentrations . for e. coli , s. aureus , and s. pneumoniae infected mice the similarities 
 between the 
 in vivo experiments and the bacterial culture footprint experiments 
 were less distinctive . for e. coli and s. 
 aureus only ethanol and acetone were found as secreted metabolites , 
 while for s. pneumoniae , none of the increased serum 
 and footprint compounds overlapped . fingerprint analysis by 
 pls - da ( three components , r2 = 0.98 , q2 
 = 0.98 ) showed clear separation between all four bacterial strains 
 with differences in amino acid levels , energy metabolites as well 
 as organic acids . however the metabolites found intracellularly in 
 the bacterial cultures did not correlate with any metabolites found 
 in the serum of the infected mice . in order to distinguish between bacteria and host responses 
 we injected mice with lps or malp2 to activate different tlrs . the 
 advantage of using lps over intact bacteria is that it should elicit 
 a host response for a gram - negative organism but does not contribute 
 bacterial metabolites . separate pls - da models were constructed comparing 
 infected , noninfected and either lps or malp2 treated mice . the corresponding 
 scores plots showed well separated clusters of differently treated 
 mice but also demonstrated similar trends for groups exposed to similar 
 cell constituents ( figure 5 ) . gram - negative 
 infected mice and animals having received lps clustered together and 
 were clearly separated from controls along the first component . however 
 the e. coli and p. aeruginosa induced 
 infections gave rise to an additional shift in the second principal 
 component , compared to lps treatment . in multivariate statistical 
 modeling , the first component explains the largest source of variance 
 in the data set , while subsequent components are orthogonal to each 
 other and explain lower levels of data variance . clearly , the tlr - mediated 
 response provides the major source of variance in the case of gram - negative 
 bacteria . very similar results were obtained for the comparison of 
 the s. pneumoniae infected mice and those treated 
 with the synthetic tlr2 agonist malp2 . in contrast , the s. 
 aureus infected and malp2 treated groups displayed significant 
 differences along the first component . compared to controls , s. aureus infected mice showed clear discrimination both 
 along the first and second component while the malp2 treated animals 
 differed from the controls only along the second component . while 
 activation by lps is obviously the most important virulence factor 
 for e. coli and p. aeruginosa infections , 
 for s. aureus it appears that most of the metabolic 
 changes are not related to a malp2-induced host response . similarities 
 between tlr2 and tlr4 agonists and gram - positive and 
 gram - negative bacterial infections . pls - da scores plot of serum h 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 controls 
 and ( 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 . the putative signaling receptors for lps and malp2 are tlr4 and 
 tlr2 , respectively . to identify specific metabolites which increase 
 in response to the activation of the tlr4 and tlr2 receptors , we studied 
 knockout mice . we first compared serum metabolite profiles of noninfected , e. coli infected and lps treated animals both in c57bl/6 
 wild - type ( two components , r2 = 0.76 , q2 = 0.60 ) and tlr4-deficient 
 mice ( 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 causing 
 the observed clustering . as shown in the previous section , in the 
 wild - type mouse both e. coli infected and lps treated 
 animals showed similar changes in their metabolite profiles compared 
 to controls , although they also differed from each other along the 
 second principal component . in order to distinguish between metabolites 
 which increased in response to an e. coli infection 
 or 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 concentrations 
 of creatine , hippurate , histidine , 2-hydroxybutyrate , taurine , and 
 tryptophan ( highlighted in green ) were significantly elevated in the 
 lps group . both disease initiators gave rise to a strong immune response 
 and severe disease symptoms ( figure 6 e and 
 f ) 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 disease 
 parameters were completely attenuated in the lps group and strongly 
 suppressed in infected mice identifying the tlr4 signaling pathway 
 as major host response for e. coli infections . in 
 the lps group , normal serum concentrations indeed the pls - da scores plot of the serum samples obtained 
 from tlr4 deficient mice revealed only two clusters without any distinction 
 between the control and lps group ( figure 6c ) . encouragingly , wild - type and tlr4 deficient mice respond in a 
 similar manner to e. coli as evidenced by the fact 
 that several of the induced metabolites are similar . 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 with 
 malp2 treated mice . in the loadings plot , we identified metabolites 
 in serum of wild - type mice which were specifically elevated by the s. aureus infection ( glycerol , creatine , acetone ; highlighted 
 red ) , compounds which were strongly elevated in response to malp2 
 treatment ( taurine , lactate , valine , isoleucine ; highlighted green ) , 
 and those found in increased concentration in both groups ( lysine , 
 isobutyrate ; highlighted blue ) . physiological response and the metabolites 
 and cytokines activated 
 by the tlr4 receptor and associated signaling pathway . ( a ) + ( b ) pls - da 
 modeling of metabolite concentrations identified in serum of c57bl/6 
 wild - type ( two components , r2 = 0.76 , q2 = 0.60 ) and in ( c ) + ( d ) 
 tlr4 deficient mice ( two components , r2 = 0.91 , q2 = 0.70 ) 24 h after 
 lps treatment and e. coli infection . ( a ) and ( c ) 
 represent scores plots from the pls - da analysis and ( b ) and ( d ) the 
 corresponding loadings plots . metabolites which were significantly 
 enhanced in response to both lps treatment and e. coli infection are highlighted in blue , those specific for lps or e. coli 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 , weight loss as 
 well as ( f ) cytokine and chemokine concentrations in the serum . physiological response and the metabolites and cytokines 
 activated 
 by the tlr2 receptor and associated signaling pathway . ( a ) + ( b ) pls - da 
 modeling of metabolite concentrations identified in serum of c57bl/6 
 wild - type ( two components , r2 = 0.77 , q2 = 0.65 ) and in ( c ) + ( d ) 
 tlr2 deficient mice ( two components , r2 = 99 , q2 = 97 ) 24 h after 
 malp2 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 significantly 
 enhanced 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 , weight 
 loss as well as ( f ) cytokine and chemokine concentrations in the serum . similar to the e.coli and lps 
 experiments , described 
 above , the class distinction between all three groups observed in 
 wild - type mice was eliminated in tlr2 knockout mice and only metabolite 
 profiles of mice with the s. aureus infection differed 
 from those of the controls . cytokine screening and blood cell analysis 
 ( figure 7 e and f ) confirmed that the tlr2-deficient 
 mice had a reduced response to malp2 . however most of the cytokine 
 levels remained elevated in s. aureus infected tlr2 
 knockout mice , indicating that other immune pathways are mainly activated . to further investigate the covariation between 
 metabolite levels and host response , pls regression analysis of all 
 four bacterial infections was performed using the physiological / immunological 
 characteristics as y variables and metabolite concentrations 
 as x variables ( three components , r2 = 0.72 , q2 = 
 0.52 ) . several statistically significant correlations between physiological 
 and immunological parameters and corresponding metabolite profiles 
 could be derived ( figure 8) . besides well - known 
 positive correlations between neutrophils and taurine and weight loss and ketone bodies , correlations were also found 
 between some cytokines and serum metabolites . for example , a positive 
 correlation was found between il6 and serum concentrations of acetone , 
 formate , creatine , and 2-hydroxybutyrate . similar positive relationships 
 were identified for tnfa , g - csf , and kc , while most of the cytokines 
 and chemokines were negatively correlated with choline , glucose , as 
 well as the tca cycle intermediates citrate and 2-oxoglutarate . correlation circle ( biplot , three components , r2 = 0.72 , q2 = 0.52 ) 
 of the four bacterial infections ( s. aureus , s. pneumoniae , e. coli , p. aeruginosa ) in c57bl/6 wild - type mice , their metabolites ( x - variables ) , as well as their physiological and immunological parameters 
 ( y - variables ) . in this work , we used a simple intraperitoneal 
 mouse model to study 
 bacterial infections . one of our main interests was the comparison 
 between the effects of gram - positive and gram - negative infections 
 on the physiological , immunological , and metabolite response of the 
 host . we selected for the study various bacterial strains of clinical 
 significance : the gram - positive strains s. aureus and s. pneumoniae and the gram - negative strains e. coli and p. aeruginosa . in previous 
 studies , the two gram - positive bacterial strains s. pneumoniae and s. aureus have been compared in a mouse model , and furthermore , different pneumonia infections 
 have been investigated in patients . to 
 the best of our knowledge , ours is the first study to use a metabolomics 
 approach to make a direct comparison between the effects of gram - positive 
 and gram - negative bacteria . in order to establish that all four pathogenic 
 bacteria induced a systemic infection with similar disease progression , 
 we measured lung cfus as well as several physiological and immunological 
 responses . because we were also interested in comparing the metabolic 
 response and the cytokine response to infection , we used mouse serum 
 as the biofluid of choice for our studies . in previous studies we 
 have shown that the metabolomics analysis of mouse serum by quantitative 
 proton nmr gives useful information about the onset of various diseases . to facilitate comparisons , all mice were harvested at 24 h after 
 infection and all parameters were measured at this time point . our results indicate that all four bacterially infected host mouse 
 models ( s. aureus , s. pneumoniae , p. aeruginosa , e. coli ) gave 
 rise to a distinct pathological phenotype based on the metabolite 
 profiles , suggesting that different cellular functions may be impaired 
 ( tables 1 and 2 , supporting information ) . the significantly enhanced serum levels of metabolites that are 
 related to fatty acid oxidation ( e.g. , the ketone bodies acetone and 
 3-hydroxybutyrate ) as well as increases in 2-hydroxybutyrate , isobutyrate , 
 and creatine , which were found in s. aureus infected 
 wild - type mice , are indicative of severe changes in hepatic cellular 
 functions . in contrast , perturbations in host tca cycle metabolites 
 were mainly 
 observed in s. pneumoniae infected mice . tca cycle 
 precursors and intermediates such as glucose , pyruvate , 2-oxoglutarate , 
 citrate , and fumarate , which usually decrease in times of sickness , 
 showed the highest serum levels in s. pneumoniae infected 
 mice compared to the other bacterial infections investigated in this 
 study . these metabolites were identified as main positive contributors 
 in the pls - da model ( table 2 , supporting information ) . unexpectedly high levels of 2-oxoglutarate and fumarate were also 
 found in the urine of pneumococcal pneumonia patients . overall , both gram - positive s. aureus and s. pneumoniae infections 
 gave rise to increased energy metabolites 
 causing clear distinction from the gram - negative bacterial infections 
 in the opls model . with a > 50 % negative culture in septic patients , 
 it seems imperative that an alternative approach to diagnosis is needed . moreover , the blood cultures currently used take several days to complete , 
 and a fast method would also be advantageous . perhaps metabolomics 
 can become a useful clinical tool to distinguish between gram - positive 
 and gram - negative infections to better inform the physician . although 
 encouraging , further studies are necessary to verify and extend these 
 initial observations . in mice infected with gram - negative bacteria , 
 we observed that 
 amino acid serum levels were strongly elevated and were partly derived 
 from the bacterial exometabolome , particularly in the case of p. aeruginosa infections . the secreted metabolites identified 
 in our bacterial culture experiments ( figure 4a ) were consistent with previous reports in the literature . however , bacterial metabolism strongly depends 
 on culture conditions , and the lack of agreement for metabolites found 
 in vivo and in cultures of s. aureus , s. 
 pneumonia , and e. coli may be caused by 
 the different composition of blood and culture media or the oxygenation 
 status . our study also demonstrated that metabolomics is a useful 
 tool 
 to investigate the effects of potential virulence factors and signaling 
 pathways in vivo . comparisons of the serum metabolites from mice treated 
 with the immune stimulators lps and malp2 and gram - negative and gram - positive 
 bacterial infections showed significant similarities ( figure 5 ) . for example , the metabolite profiles of serum 
 from mice infected with e. coli and of mice treated 
 with lps differed only in the metabolites such as valine , threonine , 
 alanine , acetone , and ethanol ( figure 6 ) . acetone 
 and ethanol were identified as bacterial metabolites involved in cellular 
 energy metabolism , while the amino acids seemed to be induced by other 
 virulence factors different from lps . investigation of e. coli infection and lps induced 
 host responses in both wild - type and tlr4 deficient mice clearly identified 
 lps as the main virulence factor . tlr4 was verified as the key receptor 
 in immune stimulation by gram - negative bacteria resulting in reduced 
 metabolic as well as physiological and immunological responses to 
 lps in tlr4 deficient mice . in e. coli infected knockout 
 mice in contrast , physiological and metabolic responses to the infection 
 remained observable while their immunological response was almost 
 completely attenuated . the same metabolites which were significantly 
 elevated in wild - type mice were also increased in tlr4 deficient animals . this indicates a strong correlation between physiological and metabolic 
 responses to an infection which seem to be independent from the activation 
 of the host immune response and in this case tlr4 specifically . in concordance with the e. coli / lps experiment , 
 the metabolic , physiological , and immunological responses to malp2 
 were attenuated in tlr2-deficient mice . the same metabolites were 
 elevated both in wild - type and tlr2 deficient mice in response to s. aureus infections . however similarities between s. aureus and malp2 were less obvious than for e. 
 coli and lps pointing out the complexity of an s. 
 aureus infection and its virulence factors . in fact , the 
 systemic chemokine / cytokine responses to s. aureus were hardly diminished in the tlr2 knockout mice , indicating that 
 other host defense signaling pathways are also involved . for s. aureus , a plethora of superantigens , which can induce 
 a strong immune response with activation of t cells at picomolar concentrations , 
 are described in the literature and may 
 explain the observed differences . finally , s. aureus also kills host cells inducing activation of the 
 inflammasome and the related sterile injury danger signal pathways . relationships between the different bacterial infections , their 
 metabolic , as well as their immunological and physiological responses 
 were further investigated in the biplot shown in figure 8 . these data highlight the statistical relationships between 
 the metabolic , cytokine , and physiological responses to infections . however , it is not clear at this stage whether there is a direct mechanistic 
 relationship between the metabolic and the cytokine responses . the 
 observed negative correlation between cytokines and chemokines and 
 the intermediates of energy metabolism such as glucose , citrate , and 
 2-oxoglutarate may simply be caused by their decreasing concentrations 
 in times of sickness . similar reasoning may also apply to the relationships 
 observed between increased ketone body , creatine and formate serum 
 levels and elevated general cytokines such as il-6 , tnf , and g - csf 
 as has already been discussed for cytokine and metabolic profiles 
 seen in protozoan infections . interestingly , 
 correlations between cytokine profiles and in vivo magnetic resonance 
 imaging and spectroscopy data have also been reported . other support for this notion comes from the correlation 
 found 
 between il-10 , mip-1a , and rantes and the amino acids asparagine , 
 phenylalanine , and histidine which have been identified as positive 
 contributors to the model in e. coli infected mice . however , especially for e. coli it should be noted 
 that cytokine and chemokine screening showed extremely high il-10 , 
 mip-1a , and rantes levels which may explain the observed covariation . our results suggest that different bacterial 
 strains give rise 
 to unique nmr - based serum profiles and that it might be possible to 
 use this approach in the future to distinguish between gram - positive 
 and gram - negative infections in a clinical setting . for some bacterial 
 strains , as is the case for p. aeruginosa , it might 
 be possible to directly identify the infecting organism because they 
 secrete specific metabolites into the host metabolome which can be 
 identified . the use of different knockout mice combined with the administration 
 of specific bacterial virulence factors allowed us to dissect bacterial 
 and host responses . our results illustrate that this is a powerful 
 approach that could be applied in future studies of host response 
 to other diseases or insults . finally , our quantitative approach allowed 
 us to make comparisons between the metabolic and physiological and 
 immunological responses to an infection which may lead to a better 
 understanding of the mechanisms of pathogenesis and immunoregulation .
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 1h 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 .