Transgenomic Metabolic Interactions in a Mouse Disease Model

Transgenomic Metabolic Interactions in a Mouse Disease Model: Interactions of Trichinella spiralis Infection with Dietary Lactobacillus paracasei Supplementation Francois-Pierre J. Martin,† Elena F. Verdu,‡ Yulan Wang,† Marc-Emmanuel Dumas,† Ivan K. S. Yap,† Olivier Cloarec,† Gabriela E. Bergonzelli,§ Irene Corthesy-Theulaz,§ Sunil Kochhar,| Elaine Holmes,† John C. Lindon,† Stephen M. Collins,‡ and Jeremy K. Nicholson*,† Biological Chemistry, Biomedical Sciences Division, Faculty of Natural Sciences, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ, United Kingdom, Intestinal Disease Research Program, McMaster University, Hamilton, Canada, and Departments of Nutrition and Health and Bioanalytical Sciences, Nestle´ Research Center, P.O. Box 44, Vers-chez-les-Blanc, CH-1000 Lausanne 26, Switzerland Received April 10, 2006

Irritable Bowel Syndrome (IBS) is a common multifactorial intestinal disorder for which the aetiology remains largely undefined. Here, we have used a Trichinella spiralis (T. spiralis)-induced model of postinfective IBS, and the effects of probiotic bacteria on gut dysfunction have been investigated using a metabonomic strategy. A total of 44 mice were divided into four groups: an uninfected control group and three T. spiralis-infected groups, one as infected control and the two other groups subsequently treated with either Lactobacillus paracasei (L. paracasei) NCC2461 in spent culture medium (SCM) or with L. paracasei-free SCM. Plasma, jejunal wall and longitudinal myenteric muscle samples were collected at day 21 post-infection. An NMR-based metabonomic approach characterized that the plasma metabolic profile of T. spiralis-infected mice showed an increased energy metabolism (lactate, citrate, alanine), fat mobilization (acetoacetate, 3-D-hydroxybutyrate, lipoproteins) and a disruption of amino acid metabolism due to increased protein breakdown, which were related to the intestinal hypercontractility. Increased levels of taurine, creatine and glycerophosphorylcholine in the jejunal muscles were associated with the muscular hypertrophy and disrupted jejunal functions. L. paracasei treatment normalized the muscular activity and the disturbed energy metabolism as evidenced by decreased glycogenesis and elevated lipid breakdown in comparison with untreated T. spiralis-infected mice. Changes in the levels of plasma metabolites (glutamine, lysine, methionine) that might relate to a modulation of immunological responses were also observed in the presence of the probiotic treatment. The work presented here suggests that probiotics may be beneficial in patients with IBS. Keywords: chemometrics • gut dysfunction • IBS • Lactobacillus paracasei • metabonomics • NMR • OPLS • Trichinella spiralis

Introduction Irritable Bowel Syndrome (IBS) is a common multifactorial intestinal disorder for which the aetiology remains largely undefined.1 Factors implicated in the onset and development of IBS include perturbation of gut microbiota precipitated by infection, diet, and genetic predisposition.2-4 Symptoms of IBS, such as abdominal pain, vomiting, and diarrhoea develop and persist commonly in patients in remission from inflammatory bowel diseases.2,3 The multiple mechanisms underlying these symptoms include altered motility and sensory perception fac* To whom correspondence should be addressed. Tel: +44 (0)20 7594 3195. Fax: +44 (0) 20 7594 3226. E-mail: [email protected]. † Imperial College London. ‡ McMaster University. § Department of Nutrition and Health, Nestle´ Research Center. | Department of Bioanalytical Sciences, Nestle´ Research Center. 10.1021/pr060157b CCC: $33.50

 2006 American Chemical Society

tors.1,2,5 It has been proposed that for some patients with IBS, a genetic predisposition may confer susceptibility to altered immune responses to inflammatory stimuli.5,6 The role of lowgrade inflammation in the pathogenesis of IBS is also supported by clinical and experimental data.3,7 Additionally, there is emerging evidence of abnormal microbiota composition8-11 but, it is not clear whether the altered microbiota have a role in IBS development or whether they are a consequence of the gut dysfunction. In particular, this abnormal microbial entourage is found in patients where constipation and diarrhoea are predominant in IBS, particularly where the numbers of Enterobacteria and Lactobacillus are low in comparison to healthy individuals.9 The model of Trichinella spiralis (T. spiralis) infection in NIH Swiss mice has a number of similarities to post-infective IBS.1 Journal of Proteome Research 2006, 5, 2185-2193

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research articles Between the 3rd and 7th day of infection, the presence of adult worms and larvae in the mucosa cause a predictable inflammatory response, increases in small bowel motility and smooth muscle growth, and mucus production modification12-16 which persist until eviction of the parasite. By 21 days post-infection, the adult worms leave the host and the acute inflammation normalizes, but functional alterations of the small intestine persist for at least a further 21 days.1,2,17 It has been shown that manipulation of host-microbiota interactions with probiotic supplementation offers a promising approach to evaluate therapeutic interventions.18 Verdu et al. demonstrated that not simply the presence, but also the type of microbiota can regulate the post-infective abnormalities.1 For example, treating T. spiralis infected mice with the probiotic Lactobacillus paracasei (L. paracasei) in its spent culture medium (SCM) and the SCM devoid of live bacteria attenuated gut muscle hyper-contractility and normalize the immune markers, suggesting amelioration of the inflammatory response.1 However, the other strains tested, Lactobacillus johnsonii, Bifidobacterium lactis, or Bifidobacterium longum, did not show normalizing effects on IBS.1 High resolution 1H nuclear magnetic resonance (NMR) spectroscopy of biofluids and tissues, coupled with multivariate statistical data analysis, is a well-established tool for the investigation of biological perturbations induced by physiological or pathological stimuli19 and has previously been applied to characterize the metabolic effects of parasite infections in animal models, e.g., Schistosoma mansoni in the mouse20 and Schistosoma japonicum in the hamster.21 This approach, known as metabonomics, provides the possibility of obtaining unique insights into in vivo metabolic processes at a system level.22 We have extensively reviewed the influences of gut microbe variations on mammalian biology and the complex combinational metabolic interactions involving more than one genome.19,23 Such interactions can be termed “microbialmammalian metabolic axis”. Perturbations of this axis (which has evolved over thousands of generations) may prove important in many aspects of animal and human disease.19,23 Here, a metabonomic strategy has been used to provide improved understanding of the underlying processes in a mouse model of post-infective IBS through analysis of the metabolic profiles of plasma, jejunal wall and muscle tissues. In addition, the effects of the probiotic L. paracasei and L. paracasei-free spent culture medium on the IBS mouse model were also evaluated. Improved knowledge of the metabolic signature of post-infective IBS and the mechanism of L. paracasei supplementation in normalizing gut abnormalities could lead to the development of therapies for treating IBS sufferers.18

Material and Methods: Bacterial Strains and Culture Conditions. L. paracasei NCC2461 was obtained from the Nestle´ Culture Collection (Lausanne, Switzerland) and grown under anaerobic conditions in Man-Rogosa-Sharpe (MRS) broth. After 48 h at 37 °C, the number of bacteria was estimated by measuring the optical density at 600 nm (1 OD600 ) 108 bacteria/mL). Bacterial cells were pelleted by centrifugation for 15 min at 5000 g at 4 °C, further resuspended at a concentration of 1010/mL in their spent culture medium (named L. paracasei) and kept in frozen aliquots until use. The spent culture medium was passed through a 0.2 µM filter to eliminate bacteria (SCM). Animal Handling Procedure and Host-Parasite Model. Animal studies were conducted as described previously.1 A total 2186

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of 44 female NIH Swiss mice (6-8 weeks old) were purchased from the National Cancer Institute (Bethesda, MD) and kept under specific pathogen-free conditions at the animal center of McMaster University. All animal studies were conducted according to the code of conduct of McMaster University Animal Care Committee and the Canadian Council on Animal Care. Uninfected mice (n ) 10) and T. spiralis infected mice (n ) 15) fed with MRS acted as control groups and 2 groups of T. spiralis infected mice were gavaged daily from day 10 to 21 after infection either with L. paracasei (n ) 9) or with SCM (n ) 10).1 At day 21 post-infection, the mice were sacrificed. Samples of plasma, jejunal wall and jejunal longitudinal myenteric muscle (LMM) were collected. The entire jejunum was dissected, rinsed in ice-cold sterile phosphate buffered saline. The mesentery was carefully removed. The muscle layer was carefully scraped off, snap-frozen and stored at -70 °C. Blood (400 µL) was collected before sacrifice into Eppendorf vials containing heparin (1:10 in sterile saline) using a sterile syringe. The plasma was obtained by centrifugation and frozen at -70 °C. Sample Preparation and 1H NMR Spectroscopic Analysis. Plasma samples were prepared by adding saline solution containing 10% D2O (serving as a spectrometer field frequency lock) into 100 µL of blood plasma to a total volume of 550 µL. Intact liver samples were bathed in 0.9% saline D2O solution. A portion of the tissue (∼15 mg) was inserted into a zirconium oxide (ZrO2) 4 mm outer diameter rotor, using an insert to make a spherical sample volume of 25 µL. All 1H NMR spectra were recorded on a Bruker DRX 600 NMR spectrometer (Rheinstetten, Germany) operating at 600.11 MHz for 1H observation. 1H NMR spectra of plasma were acquired with a Bruker 5 mm TXI triple resonance probe at 298 K. To reduce NMR spectral peak broadening caused by any residual dipolar couplings, chemical shift anisotropy, and microscopic inhomogeneities, 600 MHz 1H NMR spectra of intact tissues were acquired with a high-resolution magic-angle-spinning probe at a spin rate of 5000 Hz.24 Tissue samples were regulated at 283 K using cold N2 gas during the acquisition of the spectra to minimize any time-dependent biochemical degradation. Three 1H NMR spectra, a standard one-dimensional (1D) spectrum, a 1D Carr-Purcell-Meiboom-Gill (CPMG) spin echo spectrum and a diffusion-edited spectrum were obtained from each sample. Since the CPMG spin echo experiment gave the clearest signature of metabolic changes following intervention, with little extra information contained in the higher molecular weight components, only the results derived from the CPMG experiments are presented here. CPMG spin echo spectra were acquired using the pulse sequence [RD-90°-(t180°-t)n - acquire FID], with a spin-spin relaxation delay, 2nτ, of 160 ms for plasma and 200 ms for tissue.25 The 90° pulse length was 9.0-12 µs. A total of 128 transients were collected into 16K and 32K data points for tissue and plasma samples, respectively. The recycle delay (RD) was 2s. For assignment purposes, 2D COrrelation SpectroscopY (COSY)26 and TOtal Correlation SpectroscopY (TOCSY)27 NMR spectra were acquired on selected samples. In both cases, 48 transients per increment and 256 increments were collected into 2K data points. The spectral width in both dimensions was 10 ppm. The TOCSY NMR spectra were acquired by using the MLEV1728 spin-lock scheme for 1H-1H transfers with a spin-lock power of 6 kHz. COSY spectra were recorded with gradient selection. In both 2D NMR experiments, the water signal was

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Transgenomic Metabolic Interactions in a Mouse Disease Model

Figure 1. Typical 600 MHz 1H NMR CPMG spectrum of control plasma (A), and typical 1H MAS NMR CPMG spectra of jejunal wall (B) and LMM (C) from a control mouse. The spectra in the aromatic region (δ 5.2-8.5) were magnified 4 times compared to the aliphatic region (δ 0.7-4.5) and the plasma region (δ 5.2-5.4) was magnified twice. The key is given in Table 1.

irradiated with a weak pulse (∼50 Hz) during the recycle delay. These data were zero-filled to 2K data points in the evolution dimension. Before Fourier transformation, an unshifted sinebell and a shifted sine-bell apodization function were applied to the free induction decays of the COSY and TOCSY spectra, respectively. Further assignment of the metabolites was also accomplished with the use of Statistical TOtal Correlation SpectroscopY (STOCSY) on 1D spectra.29 Data Processing. 1H NMR spectra of plasma and 1H MAS NMR spectra of tissues were manually phased and baselinecorrected by using XwinNMR 3.5 (Bruker Biospin, Rheinstetten, Germany). The 1H NMR spectra were referenced to the chemical shift of the CH3 resonance of alanine at δ 1.466. The region containing the water resonance (δ 4.5-5.19) was removed and the spectra were converted into 22K data points over the range of δ 0.2-10.0 using Matlab (Version 7, The Mathworks inc, Natwick, MA).30 Normalization of the spectra to a constant sum was carried out prior to pattern recognition analyses. Multivariate Statistical Data Analysis. The multivariate pattern recognition techniques used in this study were based on the Principal Component Analysis (PCA) with mean centered data31 and the orthogonal-projection to latent structurediscriminant analysis (O-PLS-DA) approach with unit variance scaling.32 In the O-PLS-DA algorithm, the variation in X (here the NMR data) is decomposed in three parts: first, the variation in X (the data matrix) related to Y (the class matrix), and the two last parts contain the specific systemic variation in X and residual, respectively.32 This leads to a model with a minimal number of predictive components defined by the number of degrees of freedom between group variances. The O-PLS-DA coefficient (covariance) plots are presented using a back-scaling transformation in order to preserve the original spectral appearance, as described previously.30 This allows each data variable to be plotted with a color code which relates to the significance of class discrimination as calculated from the correlation matrix.

Results 1

H NMR Spectroscopy of Plasma and Intact Tissue Samples. Examples of typical 1H CPMG NMR spectra of plasma (A), jejunal wall (B) and LMM (C) samples obtained from an uninfected control mouse are shown in Figure 1. The assignment of the peaks to specific metabolites was achieved based on the literature,33-35 and confirmed by 2D COSY and TOCSY spectroscopy as well as STOCSY.29 The NMR spectra of both plasma and tissues contained a number of assignable amino acids, organic acids, membrane components and saturated and unsaturated fatty acids (Table 1). Lipoproteins, glycerol, Nacetyl-glycoproteins and glucose peaks were also assigned in plasma NMR spectra. Visual inspection of the 1H NMR spectra of plasma (not shown) revealed differences in overall composition between uninfected and T. spiralis infected animals. For instance, increases in the levels of lactate and alanine, but depletion in global lipoprotein content and glycerophosphorylcholine (GPC) were observed in plasma of infected mice. The metabolic profile of post-infection jejunal wall tissue showed lower levels of choline and creatine associated with an elevated amount of GPC. Finally, the jejunal LMM tissue revealed depletion in triglycerides and elevation of taurine and creatine in infected mice. However, these qualitative observations are all by visual inspection and inter-animal variation makes detailed data interpretation difficult. Hence, a more formal spectral comparison was carried out using O-PLS-DA. Metabolic Signature of T. spiralis Infection in Mice. O-PLSDA of NMR spectra of plasma and tissue samples was carried out by pair wise comparisons between the uninfected control group and infected control animals. Table 2 summarizes the characteristics of these models. A significant discrimination between the uninfected control and T. spiralis infected animals was found in the first PLS component from 1H NMR metabolic profiles of plasma and tissues, as reflected by the high value Journal of Proteome Research • Vol. 5, No. 9, 2006 2187

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Table 1. Table of Assignment of the Observed NMR Peaks of Plasma, Jejunal Wall, and LMMa key

moieties

δ 1H (ppm) and multiplicity

RCH, βCH, γCH3,δCH3 RCH, δCH3,δCH3 RCH, βCH, γCH3 CH, CH2, γCH3, RCH, βCH3 RCH, βCH3 RCH, γCH2, δCH2 RCH, γCH2, δCH2, CH2 CH3 CH3 RCH, βCH2, γCH2 RCH, βCH2, γCH2, δCH3 RCH, βCH2, γCH2 Nonequivalent CH2 N-CH3, CH2 NH2CH2, CH2OH N-(CH3)3, OCH2, NCH2 N(CH3)3, OCH2, NCH2 N-(CH3)3, OCH2, NCH2 N-CH2, S-CH2 CH2 CH3, (CH2)n, CH2-CdC, CH2-CdO, dC-CH2-Cd, -CHdCHCH3 -CH2-CH3 -(CH2)n -CHdCHCH, CH2, CH2 CH, CH 2,6-CH, 3,5-CH, 4-CH CH2, CH3 RCH, βCH2 RCH, βCH2 CH2 CH3 CH3, CH2 CH3 CH3

3.65(d), 1.95(m), 0.99(t), 1.02(d) 3.72(t), 0.91(d), 0.94(d) 3.6(d), 2.26(m), 0.98(d), 1.04(d) 4.16 (dt), 2.41 (dd), 1.20(d) 4.11(q), 1.32(d) 3.77(q), 1.47(d) 3.76(t), 1.63(m), 3.23(t) 3.77(t), 1.72(m), 1.47(m), 3.01(t) 1.91(s) 2.04 (s) 3.75(m), 2.08(m), 2.34(m) 3.78(m), 2.14(m), 2.6(dd), 2.13(s) 3.77(m), 2.15(m), 2.44(m) 2.55(d), 2.65 (d) 3.03(s), 3.92(s) 3.15 (t), 3.83 (t) 3.2(s), 4.05(t), 3.51(t) 3.22(s), 4.21(t), 3.61(t) 3.22(s), 4.32(t), 3.68(t) 3.26(t), 3.40(t) 3.55(s) 0.89(m), 1.27(m), 2.0(m), 2.3(m), 2.78 (m), 5.3(m) 0.65(s), 0.77(s) 0.87 (t), 1.29(m), 1.57(m) 5.3 (m) 3.91 (m), 3.64(m), 3.56(m) 7.16(m), 6.87(m) 7.40(m), 7.33(m), 7.35(m) 3.65(q), 1.18(t) 3.89(m), 2.79(m), 2.82 (m) 3.99(m), 2.86(m) 2.94(m) 5.24(d) 2.41(s) 2.29(s), 3.49(s) 1.18(d), 4.10 1.42(d), 4.14

metabolites

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

isoleucine leucine valine 3-D-hydroxybutyrate lactate alanine arginine lysine acetate N-acetyl-glycoproteins glutamate methionine glutamine citrate creatine ethanolamine choline phosphorylcholine GPC taurine glycine lipids in tissue

23 24 25 26 27 28 29 30 31 32 33 34 P1 P2

cholesterol in plasma LDL, VLDL in plasma unsaturated lipids in plasma glycerol tyrosine phenylalanine ethanol aspartic acid asparagine R-Glucose pyruvate acetoacetate P1 P2

a Key: s: singlet, d: doublet, t: triplet, q: quartet, m: multiplet, dd: doublet of doublets, GPC: Glycerophosphorylcholine, LDL: Low Density Lipoprotein, LMM: Longitudinal Myenteric Muscle, P1 and P2: unknown plasma metabolites, VLDL: Very Low Density Lipoprotein.

Table 2. O-PLS-DA Model Summary for the Different Discriminations among 1H NMR Spectra of Plasma, Jejunal Wall, and LMMa discrimination analyzed/ sample

untreated Infected mice vs uninfected mice

infected mice treated with L. paracasei vs untreated infected mice

infected mice treated with SCM vs untreated infected mice

plasma jejunal wall LMM

Q2 ) 89%, R2X ) 73% Q2 ) 60%, R2X ) 63% Q2 ) 49%, R2X ) 68%

Q2 ) 67%, R2X ) 40% Q2 ) 61.5%, R2X ) 45% Q2 < 0

Q2 ) 38%, R2X ) 19% Q2 < 0 Q2 < 0

a NB: O-PLS models were generated with 1 predictive component, and 2 orthogonal components to discriminate between 2 groups of mice. The R2X value shows how much of the variation in the data set X is explained by the model. The Q2 value represents the predictability of the models, and relates to its statistical validity. A negative value indicates that differences between groups are statistically nonsignificant.

of Q2 for each model (Table 2).30 Variables contributing to class discrimination are identified from the coefficient plots (Figure 2, Tables 3-5). In plasma, decreases in the levels of lipoproteins, isoleucine, acetate, glycerophosphorylcholine and an unknown compound (P1) was observed in infected mice when compared to the corresponding controls (Figure 2). These changes were associated with significant increases in the concentration of several amino acids including alanine, lysine, arginine, methionine, glycine, glutamate, and glutamine, as well as citrate, lactate and 3-D-hydroxybutyrate. In addition, elevation of choline, ethanolamine, phosphocholine, and glycerol, together with creatine and N-acetyl-glycoproteins was also observed in the plasma of T. spiralis infected mice (Table 3). The metabolites exerting the strongest leverage on the discrimination of jejunal tissues from uninfected and postinfected mice were elevated levels of GPC and decreased levels 2188


of ethanolamine, choline, acetate, glutamate, glycine, taurine, creatine, and ethanol in post-infected mice (Table 4). T. spiralis infected animals also presented elevated amount of GPC, choline and phosphocholine in jejunal LMM, as well as increased levels of creatine, taurine, and lactate when compared to the corresponding controls (Figure 2). The amount of triglycerides was globally decreased in LMM from infected mice (Table 5). Metabolic Signature of L. paracasei Effects in Infected Mice. PCA to NMR plasma spectra from animals infected with T. spiralis (red squares, Figure 3) showed distinct differences to plasma from uninfected mice. Infected animals treated with L. paracasei supplements in culture medium (black circles) or SCM (blue diamonds) showed metabolic profiles which were closer to the uninfected mice indicating partial normalization of the T. spiralis infection.

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Discussion

Figure 2. O-PLS-DA coefficient plots derived from 1H NMR CPMG spectra of plasma (A), 1H MAS NMR CPMG spectra of jejunal wall (B) and LMM (C) indicating discrimination between uninfected control mice (negative) and mice 21 days following T. spiralis infection (positive). The color code corresponds to the correlation coefficients of the variables. The key is given in Table 1.

Then, a statistical strategy based on O-PLS-DA was applied to further characterize the metabolic changes related to the dietary supplementation by the probiotic L. paracasei or its SCM on infected mice. The metabolic profiles of LMM tissues were identical to those of the infected control samples after both probiotic treatments as given by the statistics of the O-PLS-DA model (Table 2). In addition, no effects of the dietary supplement of SCM alone could be observed on the metabolic profile of the jejunal wall. However, there were significant differences between the plasma obtained from the untreated infected mice and from infected mice receiving L. paracasei supplements in culture medium or SCM alone (Table 2). The infected mice that received live L. paracasei supplementation showed increased levels of amino acids including lysine, glutamate, tyrosine, and methionine. Carbohydrate, fat and phospholipid levels were also modified with elevation of citrate, GPC, and lipoproteins in plasma when compared to the corresponding infected controls. In addition, there were reductions of lactate, choline and glutamine in the L. paracasei treated animals. The plasma from T. spiralis infected mice followed by SCM treatment alone contained higher concentrations of lipoproteins and citrate, but lower concentrations of creatine, lactate, choline, and an unknown metabolite, labeled P2, compared to the infected control mice (Table 3). Jejunal wall tissue obtained from infected mice fed with the L. paracasei showed marked increases in concentrations of amino acids, i.e., isoleucine, leucine, valine, alanine, arginine, lysine, glutamate, glutamine, glycine, asparate, asparagine, tyrosine, methionine, and phenlylalanine. Significant elevations in the levels of ethanolamine, choline, acetate, and a depletion of GPC were also observed in the jejunal wall of infected mice fed with the live bacteria (Table 4).

The aims of the present study were to investigate the metabolic signatures of the T. spiralis infection in a mouse model of post-infective IBS and to evaluate if L. paracasei supplementation, including live bacteria with the culture medium and SCM, could reverse the abnormalities present in the postinfective state. Muscle Hyperactivity. The investigation of the metabolite profiles of intact samples of small intestine tissues and plasma of T. spiralis infected mice suggested coordinated changes related to hyperactivity of intestinal muscle, as indicated by metabolites involved in energy metabolism, reflecting increased energy consumption. Thus, elevated levels of taurine and creatine were observed in LMM of infected mice (Figure 2). Creatine elevation was positively correlated with higher energy requirements resulting from increased ATP production and intake.36 There is also evidence that taurine modulates several calcium-dependent mechanisms including muscular contractility, excitability, and tissue osmolality.37-39 Elevation of a number of metabolites in plasma involved in energy metabolism was also consistent with the gut hyper-contractility found in infected mice1 (Figure 2). For example, elevated levels of lactate, citrate and alanine in plasma suggested extensive glycogenolysis and glycolysis in order to accommodate the extra energy demand required by hyper-contractility. This observation concurs with the stimulated carbohydrate metabolism found in tissues of T. spiralis infected mice.40 Moreover, increased activity of proteinase in muscle and enzymatic activity in plasma have also been shown during the first five weeks of T. spiralis infection, which may lead to the augmentation of amino acid concentrations observed in the target organs and blood in the current investigation (Figure 2, Table 3).41 In particular, the abundance of alanine and glutamine in plasma reflected both breakdown of proteins and interconversions of amino acids to produce energy. Additional energy supplies for enhanced muscular activity could be generated from mobilization of adipose tissue in infected animals. For example, liberation of fatty acids and glycerol from triacylglycerols stored in LMM and depletion of the blood plasma lipoproteins resulted in elevated plasma levels of 3-D-hydroxybutyrate and acetoacetate, which indicated a shift in energy metabolism toward ketone body formation. The systemic and tissue-specific metabolic flux is summarized in Figure 5 and showed the metabolic effects of T. spiralis infection and further modulation of probiotic supplementation. Metabonomics has been applied to study if a normalization of the metabolic profiles is occurring in parallel with the attenuation of the gut disorders observed previously.1 The effects of the treatments on uninfected mice were not investigated here. Application of PCA to NMR data from plasma and jejunal wall samples suggested a partial normalization of the plasma and jejunal wall metabolic profiles with probiotic treatments which is consistent with the work carried out by Verdu et al. who showed that dietary supplementation with both L. paracasei and SCM in infected mice moderately attenuated muscle hyper-contractility of intestinal muscle.1 This was mainly manifested by the normalization of carbohydrate metabolism as reflected by the observed reduction of lactate concentration in plasma (Figures 4 and 5 and Table 3). Nevertheless, incomplete normalization was noted since the level of citrate remained elevated in the infected animals that received probiotic supplementation, suggesting an increased breakdown of the lipids. Journal of Proteome Research • Vol. 5, No. 9, 2006 2189

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Table 3. Metabolic Effects of T. spiralis Infection on Plasma of Mice with and without Treatment with L. paracasei and SCMa

metabolite (keyb)

chemical shift

3-D-hydroxy-butyrate (4) acetate (9) acetoacetate (34) alanine (6) arginine (7) cholesterol (23) choline (17) citrate (14) creatine (15) ethanolamine (16) glutamate (11) glutamine (13) glycerol (26) glycine (21) GPC (19) isoleucine (1) lactate (5) LDL,VLDL (24) lysine (8) methionine (12) N-acetyl-glycoproteins (10) P1 P2 phosphocholine (18) tyrosine (27) unsaturated lipid (25)

4.16 (dt) 1.92 (s) 2.29 (s) 1.46 (d) 1.63 (m) 0.65 (m) 3.2 (s) 2.65 (d) 3.05 (s) 3.15 (t) 2.08 (m) 2.46 (m) 3.91 (m) 3.56 (s) 3.22 (s) 1.02 (d) 4.12 (d) 1.27 (m) 1.72 (m) 2.13 (s) 2.06 (s) 1.18 (d) 1.42 (d) 3.22 (s) 6.87 (dd) 5.3 (m)

infected vs uninfected correlation coeffs (rankingc)

0.85 (13) -0.77 (18) 0.85 (13) 0.89 (8) 0.69 (21) -0.88 (9) 0.87 (10) 0.84 (14) 0.65 (23) 0.92 (4) 0.80 (16) 0.78 (17) 0.95 (3) 0.71 (19) -0.97 (1) -0.86 (11) 0.66 (22) -0.90 (6) 0.90 (5) 0.86 (12) 0.90 (7) -0.83 (15)

infected treated with L. paracasei vs untreated infected correlation coeffs (rankingc)

0.83 (3) -0.72 (7) 0.80 (4)

infected treated with SCM vs untreated infected correlation coeffs (rankingc)

-0.72 (4) 0.87 (1) -0.62 (7)

0.60 11) -0.74 (6) 0.62 (11) -0.83 (2) 0.75 (5) 0.70 (8) 0.86 (1)

-0.83 (2) 0.65 (5)

-0.78 (3)

0.70 (20) -0.95 (2)

0.62 (10) 0.69 (9)

0.63 (6)

a Key: s: singlet, d: doublet, t: triplet, q: quartet, m: multiplet, dd: doublet of doublets, GPC: Glycerophosphorylcholine, LDL: Low Density Lipoprotein, P1 and P2: unknown plasma metabolites, VLDL: Very Low Density Lipoprotein. b Key numbers correspond to those shown on the 600 MHz spectra in the figures. c The ranking is computed among the selected variables for each model according to their O-PLS weights.

Inflammation and Hypertrophy. Attenuation of muscle hyper-contractility with supplementation of L. paracasei has previously been reported by Verdu et al. who suggested a probiotically induced modulation of the immune response caused by the infection.1 The role of low-grade inflammation in the pathogenesis of IBS is well established.3,7 In response to cytokine secretion at the site of inflammation, mammalian liver increases production and secretion of a series of plasma glycoproteins.42 In the present study, elevated levels of plasma N-acetyl-glycoproteins were observed in infected mice and could be correlated with late markers of the acute-phase protein response to parasite infection at day 14, as has already been described.42 The inflammation induced by the T. spiralis infection could also be observed by the decreased glycine level in jejunal tissue (Figures 2 and 5). The intestinal protective effect of glycine against gut disorders has been reported in the case of reinduction of colitis43 and reduction of glycine has been related to local inflammatory response and organ damage.44 In addition, the importance of lysine in modulation of diarrhoea-type intestinal pathologies in small intestine contraction and anxiety of rats has been demonstrated previously,45 and this is consistent with the increased lysine level in blood flow (Figures 2 and 5). It has been well documented that inflammation causes hyperplasia and hypertrophy in smooth muscle of rat intestine and thickening of the smooth muscle layers of human intestine.16 The inflammation resulting from the T. spiralis infection has been shown to lead to hypertrophy of epithelial cells, significant decreases in gut villi height and gut permeability, increases in crypt depth, and loss of gut epithelial tight junctions.2 Changes in the metabolic signature of plasma and tissues observed here were consistent with these findings. For 2190


instance, a perturbation of the phospholipid metabolism was observed with increased levels of ethanolamine, choline, and glycerol in plasma and decreased levels of GPC in infected mice (Figures 2 and 5). These observations concomitant to increased GPC in both jejunal wall and LMM suggested elevation of lipid synthesis related to cellular overgrowth.16 Furthermore, a loss of glutamine in the jejunal wall was observed, which may also indicate hypertrophy, since glutamine is an important fuel for rapid dividing cells, crucial for intestinal integrity.46,47 The plasma metabolic profile of mice treated with L. paracasei but not the SCM supplement showed increases in plasma concentrations of methionine, alanine, glutamate, and a decrease in glutamine. This metabolic signature is similar to that observed in conditions of stress and trauma under the influence of counter-regulatory hormones.48 Lower plasma levels of glutamine could be related to physiological changes by contributing to the immuno-suppression that accompanies catabolic stress.49 These results are consistent with the normalization of the late immune markers by L. paracasei and SCM supplements and with reports on different species of Lactobacillus exerting modulations of immunologic responses1 and expression of cytokines.50 In addition, probiotic supplementation might modulate lymphocyte proliferation which is suggested by the increase in methionine levels, a precursor of cysteine.51 The observed decreased jejunal wall GPC level related to a modification of dietary phospholipids use52 can be correlated with the role of Lactobacillus in restoring the structure of tight junctions, the selective permeability of the gut and hence cell membrane composition.53 Disruption of Gut Microbiota. Reduced levels of jejunal wall acetate, choline and ethanol and blood plasma acetate were observed in T. spiralis infected mice. These metabolites are


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Table 4. Metabolic Effects of T. spiralis Infection on Jejunal Wall of Mice with and without Treatment with L. paracaseia,d

metabolite (keyb)

chemical shift

acetate (9) alanine (6) arginine (7) asparagine (31) aspartic acid (30) choline (17) creatine (15) ethanol (29) ethanolamine (16) glutamate (11) glutamine (13) glycine (21) GPC (19) isoleucine (1) leucine (2) lysine (8) methionine (12) phenylalanine (28) taurine (20) tyrosine (27) valine (3)

1.92 (s) 1.46 (d) 1.63 (m) 2.94 (m) 2.79 (m) 3.2 (s) 3.05 (s) 1.18 (t) 3.15 (t) 2.08 (m) 2.46 (m) 3.56 (s) 3.22 (s) 1.02 (d) 0.96 (t) 1.72 (m) 2.13 (s) 7.40 (m) 3.44 (t) 6.87 (dd) 1.04 (d)


-0.83 (2)

-0.58 (8) -0.74 (5) -0.74 (6) -0.75 (4) -0.72 (7) -0.83 (3) 0.93 (1)

-0.59 (9)

infected treated with L. paracasei vs untreated infected correlation coeffs (rankingc)

0.65 (11) 0.72 (2) 0.61 (13) 0.65 (9) 0.67 (8) 0.53 (18) 0.60 (16) 0.65 (10) 0.63 (12) 0.69 (3) -0.54 (17) 0.68 (7) 0.60 (15) 0.61 (14) 0.75 (1) 0.68 (6)

Figure 3. PCA scores plot (PC1 vs PC2) derived from 1H NMR CPMG spin echo spectra of plasma from mice uninfected (2), post T. spiralis-infected (9), and post-infected mice treated either with L. paracasei (b) or with SCM ([). The model was calculated with 4 principal pomponents and mean centered data, R2X ) 89%, Q2 ) 78%. PC1 and PC2 explained respectively 63% and 11% of the variance.

0.69 (5) 0.69 (4)

a Key: s: singlet, d: doublet, t: triplet, q: quartet, m: multiplet, dd: doublet of doublets, GPC: Glycerophosphorylcholine. b Key numbers correspond to those shown on the 600 MHz spectra in the figures. c The ranking is computed among the selected variables for each model according to their O-PLS weights. d No effects were observed in jejunal wall NMR spectra from infected mice with SCM treatment.

Table 5. Metabolic Effects of T. Spiralis Infection on LMM of Untreated Micea,d

metabolite (keyb)

chemical shift & multiplicity


choline (17) creatine (15) GPC (19) lactate (5) lipid (22) phosphocholine (18) taurine (20)

3.2 (s) 3.05 (s) 3.22 (s) 4.12 (d) 0.89 (m) 3.22 (s) 3.44 (t)

0.67 (3) 0.70 (2) 0.84 (1) 0.61 (5) -0.57 (6) 0.39 (7) 0.63 (4)

a Key: s: singlet, d: doublet, t: triplet, q: quartet, m: multiplet, dd: doublet of doublets, GPC: Glycerophosphorylcholine. b Key numbers correspond to those shown on the 600 MHz spectra in the figures. c The ranking is computed among the selected variables for each model according to their O-PLS weights. d No effects were observed in LMM NMR spectra from infected mice with either L. paracasei or SCM treatments.

known to be metabolized by the gut microbiota.54,55 The changes of these metabolites are consistent with changes in microbiota metabolism in response to infection and may also reflect the quantitative changes in the measurable microbiota already described in this model,1 i.e., T. spiralis infection decreased the total Enterococcus counts in the small intestine and tended to decrease Lactobacillus. Variations in microbiotal colonization have been previously reported in patients with IBS.8,56 Alteration of gut physiology, i.e., malabsorption of nutrients20 and gut motility,1 were considered to be consequences of gut microflora disruption with abdominal infection.57 Normalization of these microbiota-related co-metabolites was evident in both plasma and jejunal wall after mice were treated with live L. paracasei (Figures 4 and 3, Tables 3 and 4).

Figure 4. O-PLS-DA coefficient plots derived from 1H NMR CPMG spectra of plasma (A, B), and 1H MAS NMR CPMG spectra of jejunal wall (C), illustrating the discrimination between untreated infected mice (negative) and infected mice (positive) treated either with L. paracasei in culture medium (A, C) or with SCM (B). The color code corresponds to the correlation coefficients of the variables. The key is given in Table 1.

These results were consistent with the probiotic-induced partial normalization of measurable microbiota altered by T. spiralis infection. Thus, we postulated that L. paracasei might modulate microbiota and gut metabolism to normalize absorption of nutrients in infected animals. Journal of Proteome Research • Vol. 5, No. 9, 2006 2191

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Figure 5. Major fuel and amino acid flows between organs and amino acid interconversions in LMM. Elevation (v) or depletion (V) in metabolites as observed in infected mice; normalization (b) or elevation (b) as observed in infected mice treated with probiotic are indicated. Taurine and acetylcholine function in muscle activity is described by f. Keys: LMM, Longitudinal Myenteric Muscle; GPC, Glycerophosphorylcholine; HDL HighDensity Lipoprotein; VLDL, Very Low-Density Lipoprotein.

Comparison of the Effects of Live L. paracasei and SCM Alone. Even though L. paracasei free-SCM efficiently normalized gut contractility, it induced limited effects on plasma and jejunal wall profiles when compared to live L. paracasei in SCM.1 In particular, no effects were observed on microbiota related compounds in agreement with the previous finding that SCM had no measurable effects on small intestine microbiota.1 The results suggested that an altered intestinal microbiota or a misbalance of the microbiota metabolism in the post-infective state may be a consequence of enteric infection and that they are not directly involved in the maintenance of muscle hypercontractility. Furthermore, these results also indicated that L. paracasei and the metabolites present in its SCM may regulate gut contractility by additional host-bacterial interactions that do not require the normalization of gut microbiota or their metabolism. Indeed, recent studies on commensal microbiota or intervention in germ-free mice with B. thetaiotaomicron have described the potential of gut microflora to modulate directly host metabolism.58,59 LMM tissues, with respect to both treatments, were shown to be metabolically identical to control infected samples despite reported modulation of protein and gene expression related to immune responses.1 Metabonomic analysis suggested that even if treatments induced a normalization of muscular activity the changes in muscular morphology and disturbed metabolic profiles, a consequence of hypertrophy and hyperplasia, were not resolved.

Conclusions Both systemic and tissue-specific metabolic changes associated with the T. spiralis model of post-infective IBS were detected. The metabolic profiling approach showed changes that were consistent with an increase in energy requirement 2192


Martin et al.

due to muscular hyper-contractility, the hypertrophy of intestinal muscles, inflammation and the gut microflora disturbance. Additionally, it has been shown that partial regression of the T. spiralis-induced effects could be achieved by intervention with a L. paracasei probiotic. The probiotic treatment moved the energy metabolism toward normality, reduced the gut microbiota disturbances and might contribute to normalization of the late inflammatory markers. Hence, supplementation with specific probiotics appeared to offer considerable promise in the treatment of post-infective IBS by modulating host metabolism and host-microbiota interactions. This work demonstrates the potential of metabolic profiling for disease and therapeutic surveillance and forms a framework for surveying modulation of mammalian-microbiota interactions and studying mechanisms of the “microbe-mammalian metabolic axis” interactions related to the processes of host diseases. Abbreviations. COSY, correlated spectroscopy; CPMG, CarrPurcell-Meiboom-Gill; GPC, glycerophosphorylcholine; IBS, irritable bowel syndrome; LMM, longitudinal myenteric muscle; NMR, nuclear magnetic resonance; MAS, magic angle spinning; MRS, Man-Rogosa-Sharpe; O-PLS-DA, orthogonal projection to latent structure discriminant analysis; OSC, orthogonal signal correction; PCA, principal component analysis; PI-IBS, postinfective IBS; PLS-DA, projection to latent structure discriminant analysis; SCM, spent culture medium; STOCSY, statistical correlation spectroscopy; TOCSY, total correlation spectroscopy

Acknowledgment. The authors acknowledge Denis Breuille´ and Norbert Sprenger for helpful discussions and advice. This work received financial support from Nestle´ to F.P.M., Y.W., and E.V., from the Canadian Institutes of Health Research to S.M.C. to E.V., from the UK Department for Education and Skills and Pfizer to I.K.S.Y.; and from the Biological Atlas of Insulin Resistance Consortium under the Wellcome Trust Functional Genomics Initiative grant (066786) to M.E.D. and O.C. References (1) Verdu, E. F.; Bercik, P.; Bergonzelli, G. E.; Huang, X. X.; Blennerhasset, P.; Rochat, F.; Fiaux, M.; Mansourian, R.; CorthesyTheulaz, I.; Collins, S. M. Gastroenterology 2004, 127, 826-837. (2) Barbara, G.; Vallance, B. A.; Collins, S. M. Gastroenterology 1997, 113, 1224-1232. (3) Barbara, G.; De, G. R.; Stanghellini, V.; Cremon, C.; Corinaldesi, R. Gut 2002, 51 Suppl 1, i41-i44. (4) Rodriguez, L. A.; Ruigomez, A. BMJ 1999, 318, 565-566. (5) Shanahan, F. Gastroenterology 2001, 120, 622-635. (6) Bickston, S. J.; Comerford, L. W.; Cominelli, F. Curr. Gastroenterol. Rep. 2003, 5, 518-523. (7) Bercik, P.; Verdu, E. F.; Collins, S. M. Gastroenterol. Clin. North Am. 2005, 34, 235-vii. (8) Madden, J. A.; Hunter, J. O. Br. J. Nutr. 2002, 88 Suppl 1, S67S72. (9) Malinen, E.; Rinttila, T.; Kajander, K.; Matto, J.; Kassinen, A.; Krogius, L.; Saarela, M.; Korpela, R.; Palva, A. Am. J. Gastroenterol. 2005, 100, 373-382. (10) Si, J. M.; Yu, Y. C.; Fan, Y. J.; Chen, S. J. World, J. Gastroenterol. 2004, 10, 1802-1805. (11) Matto, J.; Maunuksela, L.; Kajander, K.; Palva, A.; Korpela, R.; Kassinen, A.; Saarela, M. FEMS Immunol. Med. Microbiol. 2005, 43, 213-222. (12) Collins, S. M.; Blennerhassett, P. A.; Blennerhassett, M. G.; Vermillion, D. L. Am. J. Physiol. 1989, 257, G898-G903. (13) Vermillion, D. L.; Collins, S. M. Am. J. Physiol. 1988, 254, G124G129. (14) Marzio, L.; Blennerhassett, P.; Chiverton, S.; Vermillion, D. L.; Langer, J.; Collins, S. M. Am. J. Physiol. 1990, 259, G306-G313. (15) Collins, S. M.; McHugh, K.; Jacobson, K.; Khan, I.; Riddell, R.; Murase, K.; Weingarten, H. P. Gastroenterology 1996, 111, 15091515.

research articles

Transgenomic Metabolic Interactions in a Mouse Disease Model (16) Blennerhassett, M. G.; Vignjevic, P.; Vermillion, D. L.; Collins, S. M. Am. J. Physiol. 1992, 262, G1041-G1046. (17) Bercik, P.; Wang, L.; Verdu, E. F.; Mao, Y. K.; Blennerhassett, P.; Khan, W. I.; Kean, I.; Tougas, G.; Collins, S. M. Gastroenterology 2004, 127, 179-187. (18) Sartor, R. B. Gastroenterology 2004, 126, 1620-1633. (19) Nicholson, J. K.; Holmes, E.; Wilson, I. D. Nat. Rev. Microbiol. 2005, 3, 431-438. (20) Wang, Y.; Holmes, E.; Nicholson, J. K.; Cloarec, O.; Chollet, J.; Tanner, M.; Singer, B. H.; Utzinger, J. Proc. Natl. Acad. Sci., U.S.A 2004, 101, 12676-12681. (21) Wang, Y.; Utzinger, J.; Xiao, S. H.; Xue, J.; Nicholson, J. K.; Tanner, M.; Singer, B. H.; Holmes, E. Mol. Biochem. Parasitol. 2006, 146, 1-9. (22) Nicholson, J. K.; Wilson, I. D. Prog. Nucl. Magn. Reson. Spectrosc. 1989, 21, 449-501. (23) Nicholson, J. K.; Holmes, E.; Lindon, J. C.; Wilson, I. D. Nat. Biotechnol. 2004, 22, 1268-1274. (24) Waters, N. J.; Garrod, S.; Farrant, R. D.; Haselden, J. N.; Connor, S. C.; Connelly, J.; Lindon, J. C.; Holmes, E.; Nicholson, J. K. Anal. Biochem. 2000, 282, 16-23. (25) Stejskal, E. O.; Tanner, J. E. J. Chem. Phys. 1965, 42, 288-292. (26) Nagayama, K. J. Magn. Reson. 1980, 40, 321-334. (27) Spraul, M.; Nicholson, J. K.; Lynch, M. J.; Lindon, J. C. J Pharm. Biomed. Anal. 1994, 12, 613-618. (28) Bax, A.; Davis, D. J. Magn. Reson. 1985, 65, 355-360. (29) Cloarec, O.; Dumas, M. E.; Craig, A.; Barton, R. H.; Trygg, J.; Hudson, J.; Blancher, C.; Gauguier, D.; Lindon, J. C.; Holmes, E.; Nicholson, J. Anal. Chem. 2005, 77, 1282-1289. (30) Cloarec, O.; Dumas, M. E.; Trygg, J.; Craig, A.; Barton, R. H.; Lindon, J. C.; Nicholson, J. K.; Holmes, E. Anal. Chem. 2005, 77, 517-526. (31) Wold, S. Chemom. Intell. Lab. Syst. 1987, 2, 37-52. (32) Trygg, J.; Wold, S. J. Chemom. 2003, 17, 53-64. (33) Nicholson, J. K.; Foxall, P. J.; Spraul, M.; Farrant, R. D.; Lindon, J. C. Anal. Chem. 1995, 67, 793-811. (34) Tugnoli, V.; Mucci, A.; Schenetti, L.; Calabrese, C.; Di, F. G.; Rossi, M. C.; Tosi, M. R. Int. J. Mol. Med. 2004, 14, 1065-1071. (35) Wang, Y.; Tang, H.; Holmes, E.; Lindon, J. C.; Turini, M. E.; Sprenger, N.; Bergonzelli, G.; Fay, L. B.; Kochhar, S.; Nicholson, J. K. J. Proteome Res. 2005, 4, 1324-1329. (36) de Jonge, W. J.; Marescau, B.; D’Hooge, R.; de Deyn, P. P.; Hallemeesch, M. M.; Deutz, N. E.; Ruijter, J. M.; Lamers, W. H. J. Nutr. 2001, 131, 2732-2740. (37) Huxtable, R. J. Prog. Clin. Biol. Res. 1990, 351, 157-161.

(38) Schanne, O. F.; Dumaine, R. J. Pharmacol. Exp. Ther. 1992, 263, 1233-1240. (39) Huxtable, R. J. Physiol Rev. 1992, 72, 101-163. (40) Przyjalkowski, Z.; Malinowska, A. Bull. Acad. Pol. Sci. Biol. 1978, 26, 173-179. (41) Hadas, E.; Derda, M.; Boczon, K.; Wandurska-Nowak, E. Wiad. Parazytol. 1994, 40, 329-335. (42) Stadnyk, A.; Gauldie, J. Immunol. Today 1991, 12, A7-12. (43) Tsune, I.; Ikejima, K.; Hirose, M.; Yoshikawa, M.; Enomoto, N.; Takei, Y.; Sato, N. Gastroenterology 2003, 125, 775-785. (44) Grotz, M. R.; Pape, H. C.; van, G. M.; Stalp, M.; Rohde, F.; Bock, D.; Krettek, C. Shock 2001, 16, 116-121. (45) Smriga, M.; Torii, K. Proc. Natl. Acad. Sci. U. S. A 2003, 100, 15370-15375. (46) Labow, B. I.; Souba, W. W. World, J. Surg. 2000, 24, 1503-1513. (47) Wu, G.; Meier, S. A.; Knabe, D. A. J. Nutr. 1996, 126, 2578-2584. (48) Wernerman, J.; Larsson, J.; Nordenstrom, J. Lakartidningen 1988, 85, 4215-4216. (49) Lacey, J. M.; Wilmore, D. W. Nutr. Rev. 1990, 48, 297-309. (50) Christensen, H. R.; Frokiaer, H.; Pestka, J. J. J. Immunol. 2002, 168, 171-178. (51) Chuang, J. C.; Yu, C. L.; Wang, S. R. Clin. Exp. Immunol. 1990, 81, 173-176. (52) Parthasarathy, S.; Subbaiah, P. V.; Ganguly, J. Biochem. J. 1974, 140, 503-508. (53) Qin, H. L.; Shen, T. Y.; Gao, Z. G.; Fan, X. B.; Hang, X. M.; Jiang, Y. Q.; Zhang, H. Z. World J. Gastroenterol. 2005, 11, 2591-2596. (54) al-Waiz, M.; Mikov, M.; Mitchell, S. C.; Smith, R. L. Metabolism 1992, 41, 135-136. (55) Tannock, G. W. Appl. Environ. Microbiol. 2004, 70, 3189-3194. (56) Fujita, K.; Kaku, M.; Yanagase, Y.; Ezaki, T.; Furuse, K.; Ozawa, A.; Saidi, S. M.; Sang, W. K.; Waiyaki, P. G. Ann. Trop. Paediatr. 1990, 10, 339-345. (57) Liu, C.; Russell, R. M.; Smith, D. E.; Bronson, R. T.; Milbury, P. E.; Furukawa, S.; Wang, X. D.; Blumberg, J. B. Int. J. Vitam. Nutr. Res. (N.Y.) 2004, 74, 74-85. (58) Xu, J.; Bjursell, M. K.; Himrod, J.; Deng, S.; Carmichael, L. K.; Chaing, H. C.; Hooper, L. V.; Gordon, J. I. Science 2003, 299, 20742076. (59) Backhed, F.; Ding, H.; Wang, T.; Hooper, L. V.; Koh, G. Y.; Nagy, A.; Semenkovich, C. F.; Gordon, J. I. Proc. Natl. Acad. Sci. U. S. A 2004, 101, 15718-15723.

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