Metabolic Assessment of Gradual Development of Moderate

Metabolic Assessment of Gradual Development of Moderate Experimental Colitis in IL-10 Deficient Mice Francois-Pierre J. Martin,*,† Serge Rezzi,† Ivan Montoliu,† David Philippe,† Lionel Tornier,† Anja Messlik,| Gabriele Ho ¨ lzlwimmer,‡ Pia Baur,| Leticia Quintanilla-Fend,‡ Gunnar Loh,§ Michael Blaut,§ Stephanie Blum,† Sunil Kochhar,† and Dirk Haller| Nestle´ Research Center, P.O. Box 44, Vers-chez-les-Blanc, CH-1000 Lausanne 26, Switzerland, Institut fu ¨r Pathologie, Ingolsta¨dter Landstrasse 1, 85764 Neuherberg, Germany, German Institute of Human Nutrition Potsdam-Rehbruecke, Department of Gastrointestinal Microbiology, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany, and Technische Universita¨t Mu ¨ nchen, Chair for Biofunctionality, ZIEL-Research Centre for Nutrition and Food Science, Am Forum 5, 85350 Freising-Weihenstephan, Germany Received November 20, 2008

Evidence has linked genetic predisposition and environmental exposures to the worldwide pandemic of inflammatory bowel diseases (IBD), but underlying biochemical events remain largely undefined. Here, we studied the gradual development of colitis in Interleukin 10 deficient mice using a combination of (i) histopathological analysis of intestinal sections, (ii) metabolic profiling of blood plasma, and (iii) measurement of plasma inflammatory biomarkers. Data integration using chemometric tools, including Independent Component Analysis, provided a new strategy for measuring and mapping the metabolic effects associated with the development of intestinal inflammation at the age of 1, 8, 16, and 24 weeks. Chronic inflammation appeared at 8 weeks and onward, and was associated with altered cecum and colon morphologies and increased inflammatory cell infiltration into the mucosa and the submucosa. Blood plasma profiles provided additional evidence of loss of energy homeostasis, impaired metabolism of lipoproteins and glycosylated proteins. In particular, IL-10 -/- mice were characterized by decreased levels of VLDL and increased concentrations of LDL and polyunsaturated fatty acids, which are related to the etiology of IBD. Moreover, higher levels of lactate, pyruvate, citrate and lowered glucose suggested increased fatty acid oxidation and glycolysis, while higher levels of free amino acids reflected muscle atrophy, breakdown of proteins and interconversions of amino acids to produce energy. These integrated system investigations demonstrate the potential of metabonomics for investigating the mechanistic basis of IBD, and it will provide novel avenues for management of IBD. Keywords: Chemometrics • Experimental colitis • IL-10 deficient mice • Gut dysfunction • IBD • Metabonomics • NMR

Introduction The incidence and prevalence of inflammatory bowel diseases (IBD) continue to rise worldwide and are the cause of much morbidity and disability, placing a considerable burden on health care systems.1,2 As many as 1.4 million persons in the United States and 2.2 million persons in Europe suffer from these diseases; the two main idiopathic pathologies being Crohn’s disease and ulcerative colitis.3 IBD are common multifactorial intestinal disorders leading to chronic inflammation, destruction of intestinal mucosa, and the manifestation of clinical symptoms (abdominal pain, vomiting, diarrhea and * Correspondence should be addressed to Francois-Pierre J. Martin, Nestle´ Research Center, BioAnalytical Sciences, Vers-chez-les-Blanc, CH-1000 Lausanne 26, Switzerland. E-mail: [email protected]. Tel: + 41 (0) 21 785 8771. Fax: +41 (0) 21 785 9486. † Nestle´ Research Center. | Technische Universita¨t Mu ¨ nchen. ‡ Institut fu ¨ r Pathologie. § German Institute of Human Nutrition Potsdam-Rehbruecke.

2376 Journal of Proteome Research 2009, 8, 2376–2387 Published on Web 03/26/2009

weight loss).4 IBD are generally determined by a deregulation of the mucosal immune response toward luminal gut bacteria antigens and by autoimmune events, such as elevated production of proinflammatory cytokines and increased activation of immune cells.4,5 The etiology of IBD remains largely undefined, but both genetic predisposition and environmental exposure are thought to contribute to the initiation of the pathogenesis.6,7 Epidemiological studies have revealed that the strongest environmental factors associated with the pathologies are cigarette smoking (for Crohn’s disease) and appendectomy, and that IBD incidence varies with age, time, and geographical region.2,4 Moreover, the involvement of gut microbiota in the chronic mucosal immune activation underlying the IBD pathogenesis have been widely documented, in particular as a key component in the duration and the prevention/treatment of chronic intestinal inflammation.8,9 To understand the pathogenesis of IBD, several animal models based on genetic modification have been developed, such as Interleukin 10 knock out (IL-10 -/-) mice. Notably, 10.1021/pr801006e CCC: $40.75

 2009 American Chemical Society

Metabolic Assessment of Moderate Experimental Colitis

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taining 10 µL of heparin (for metabonomics) and EDTA (for enzymatic assays). Animals were killed by cervical dislocation immediately after the blood was collected. The plasma was obtained by centrifugation and frozen at -80 °C prior to analysis. Measures of Blood Plasma Inflammatory Markers. Protein levels for sTNFR2 and serum amyloid A (SAA) of WT and IL-10 -/- mice at the age of 1, 8, 16, and 24 were measured in plasma by enzyme-linked immunosorbent assay (ELISA) following the manufacturer’s instructions (respectively, R&D Systems, England, and Biosource). All samples were measured in duplicates and the concentration calculations were derived from appropriate standard curves using a 4-PL algorithm. Data were analyzed by means ( SEM or SD, and the Student’s t test (unpaired). Probability values of less than 5% were considered as significant.

IL-10 is an essential immunoregulator in the intestinal tract that physiologically limits and down-regulates inflammation by affecting the activation, the growth and the differentiation of many hemopoietic cells by pro-inflammatory cytokines.11 In addition, several studies demonstrated that colitis developed in IL-10 -/- mice can be linked to the absence of the general suppressive effect of IL-10 on the production of pro-inflammatory mediators by macrophages and Th 1 like T-cells resulting in an uncontrolled pathogenic Th 1 response.10 IL10 knockout mice develop chronic colonic inflammation that mimics human IBD, which results from an unopposed immune and inflammatory response to the normal intestinal microbiota.10 These inflammatory processes are markedly associated with diarrhea, increased protein catabolism and loss of lean mass and appetite.12 Recent studies in inflamed IL-10 -/- mice and IBD patients demonstrated that the initiation of endoplasmic reticulum-mediated stress responses in intestinal epithelial cells may contribute to the pathogenesis of chronic intestinal inflammation.13 In particular, these studies brought evidence that chronic intestinal inflammation represents an energydeficiency disease with alterations in the oxidative metabolism of epithelial cells.13 Advancing knowledge regarding the mechanisms of IBD has led to the development of different therapeutic solutions based on surgery,14 cannabinoids,15 immunosuppression (Remicade, Mesalamine, etc),16,17 and alternatively probiotic supplementation.18 However, the IBD diagnosis is only possible at an advanced disease state when symptoms are detectable by radiology, endoscopy with biopsy of pathological lesions.19 There is therefore a need to develop new strategies for early prognosis and treatment of IBD. Application of the wellestablished metabonomic approach20 could help to define early biomarkers of pathogenesis to be used for disease surveillance. Metabonomics indeed provides an efficient way to diagnose toxicological and pathophysiological states,21,22 assess metabolic response to environmental factors,23 characterize metabolic phenotypes of mammals including host and gut microbiome metabolic interactions,24 and the metabolic effects induced by nutritional interventions.25,26 In extension to our previous investigations in the development of chronic inflammation in a model of IL-10 deficient mice,13,27 we have monitored the metabolic changes associated with the gradual development of colitis. For this purpose, we have applied a metabonomic approach based on the analysis of blood plasma by high resolution proton nuclear magnetic resonance (1H NMR) spectroscopy, in combination with immunological and histological measurements. We here describe the metabolic variations induced by gradual colitis and discuss their association with changes in energy homeostasis and altered lipoprotein cycle.

Material and Methods Animal Handling Procedure. All animal studies were carried out under appropriate national guidelines according to the code of conduct of Animal Care Committee (approval no. 32-2347/ 4+63). A total of 20 wild-type (WT) 129S6/SvEvTac mice and 20 IL-10 gene deficient (IL-10 -/-) 129(B6)-Il10tm1Cgn/J mice were housed at constant room temperature (22 °C ( 10%), air humidity (55% ( 10%), and a light/dark cycle of 12 h. Water and feed (ssniff Spezialdia¨ten GmbH, Germany) were given ad libitum. Samples were taken from 1, 8, 16, and 24 week old mice under isoflurane anesthesia. Blood from the retro-orbital plexus (200-500 µL) was collected into Eppendorf vials con-

Histological Characterization of the Cecal and Colonic Inflammatory State. Sections of the distal ileum, cecal tip and distal colon were fixed in 10% neutral buffered formalin (Sigma Aldrich). The fixed tissue was embedded in paraffin, sectioned at 4 µm, and hematoxylin- and eosin-stained. Histology scoring was performed by blindly assessing the degree of lamina propria mononuclear cell infiltration, crypt hyperplasia, goblet cell depletion and architectural distortion, resulting in a score from 0 (not inflamed) to 12 (inflamed), as previously described.28 Briefly, according to Katakura method, the total scoring range is between 0 and 12. The information on epithelial damage is coded with a score given between 0 and 6 (0 ) normal; 1 ) hyperproliferation, irregular crypts, and goblet cell loss; 2 ) mild to moderate crypt loss (10-50%); 3 ) severe crypt loss (50-90%); 4 ) complete crypt loss, surface epithelium intact; 5 ) small- to medium-sized ulcer (10 crypt width)). The information on the tissue infiltration with inflammatory cells is coded separately for the mucosa with a score between 0 and 3 (0 ) normal; 1 ) mild; 2 ) modest; 3 ) severe), the submucosa with a score between 0 and 2 (0 ) normal, 1 ) mild to modest; 2 ) severe) and the muscle/serosa with a score between 0 and 1 (0 ) normal; 1 ) moderate to severe). Blood Sample Preparation and 1H NMR Spectroscopic Analysis. Plasma and urine metabolic profiles were measured at 298 K on a Bruker Avance II 600 MHz spectrometer equipped with a 5 mm inverse probe (Bruker Biospin, Rheinstetten, Germany). All samples were randomly measured using an automatic sample changer. Plasma samples (320 µL) were introduced into a 4 mm NMR tube with 40 µL of deuterium oxide (D2O) as locking substance. For samples obtained from 1 week old mice, 100 µL of blood plasma was mixed with 260 µL of D2O solution. Three types of 1H NMR spectra were measured for each blood plasma samples using a standard onedimensional pulse sequence (D1-90°-t1-90°-tm-90°-free induction decay [FID]) with water suppression,29 a Carr-PurcellMeiboom-Gill (CPMG, D1-90°-(τ-180°-τ-)n-FID) spin-echo sequence with water suppression30 and a diffusion-edited (D190°-G1-180°-G1-90°-G2-∆-90°-G1-180°-G1-90°-G2-τ-90°-FID) sequence.31 The standard spectra were acquired with a relaxation delay D1 of 2.5 s during which the water resonance is selectively irradiated, and a fixed interval t1 of 3 µs. The water resonance is irradiated for a second time during the mixing time tm of 100 ms. CPMG spin-echo spectra were measured using a spin-echo loop time (2nτ) of 19.2 ms and a relaxation delay of 2.5 s. Diffusion-editing spectra were obtained using a relaxation delay D1 of 1 s, pulsed field gradients G1 and G2 set Journal of Proteome Research • Vol. 8, No. 5, 2009 2377

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at 46.8 G · cm , and a diffusion delay ∆ of 120 ms during which the molecules are allowed to diffuse. For each sample, 32 free induction decays (FIDs) were collected into 65 K data points using a spectral width of 12 KHz. The FIDs were multiplied by an exponential weighting function corresponding to a line broadening of 0.3 Hz before Fourier transformation for standard and CPMG data sets. A line broadening of 1 Hz was applied to FIDs of diffusion-edited data sets. The acquired NMR spectra were corrected for phase and baseline distortions and referenced to the chemical shift of R-glucose at δ 5.236 using the TOPSPIN (version 2.1, Bruker Biospin, Rheinstetten, Germany) software package. Statistical Analysis of 1H NMR Spectroscopic Data. The spectra were converted into 22K data points over the range of δ 0.2-10.0 using an in-house developed MATLAB (The MathWorks, Inc., Natick, MA) routine excluding the water residue signal between δ 4.68-5.10. The spectra were normalized to a constant total sum of all intensities within the specified range prior to multivariate data analyses. Chemometric analysis was performed using the SIMCA-P+ (version 11.5, Umetrics AB, Umeå, Sweden) software package and in-house developed MATLAB (The MathWorks, Inc., Natick, MA) routines on unit-variance scaled NMR variables (i.e., each variable divided by its standard deviation). Initial data analyses were conducted using a nonsupervised method, Principal Component Analysis (PCA,32), in order to assess metabolic similarities between samples. Data were visualized by means of principal component scores, where each point represents an individual metabolic profile. NMR variables, for example, metabolic concentrations, responsible for the differences between samples in the scores plot can be extracted from the corresponding loadings plot, where each coordinate represents a single NMR signal. With the aim of depicting time-dependent profiles in a meaningful way, another nonsupervised method based on Independent Component Analysis (ICA,33) was applied to NMR spectra. This nonsupervised method considers each signal as a combination of independent sources that can be solved by a linear model. In this case, the model is built applying a criterion of maximization of the statistical independence between the sources. Therefore, after solving the model, a set of independent components and mixing matrix values is obtained. The first provides the independent source signals, thus, giving information of the regions of the global signal that are behaving independently. The mixing matrix contains information about the contribution of each sample to the general model. In addition, Projection to Latent Structure (PLS,34) and the Orthogonal Projection to Latent Structure (O-PLS,35) methods were applied to maximize the discrimination between sample groups focusing on differences according to time-dependent metabolic variations with gradual development of colitis. O-PLS discriminant analysis (O-PLS-DA) provides a way to filter out metabolic information (NMR spectral data) that is not correlated to the predefined classes. Influential variables that are therefore correlated to the group separation are identified using the variable coefficients according to a previously published method.36 The weight of a variable in the discrimination is given by the square of its correlation coefficient (r2), which is color coded from zero in blue to high values (0.7-0.9) in red. The robustness of statistical models was assessed with the standard 7-fold cross validation method (repeatedly leaving out a seventh of the animal subjects and predicting them back into the model). To test the validity of the model against overfitting, 2378

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the goodness of fit (RX ) and predictability (QY2) values of O-PLS-DA models were computed and reported in Table 2. Here, the test for the significance of the Pearson productmoment correlation coefficient was used to calculate the cutoff value of the correlation coefficients at the level of p < 0.05 (r ) 0.58 for n ) 10 and r ) 0.27 for n ) 40). In addition, for each influential metabolite the Mann-Whitney U test was performed on relative concentrations obtained by integration of normalized area of representative signals (Table 3). Heatmap Representation of the Metabolic Correlation. A statistical correlation analysis was applied to plasma inflammation biomarkers, histological inflammatory state and blood plasma metabolites in order to establish possible functional relationships. Pearson’s correlation coefficients were computed between normalized intensities of spectral peaks found to be significantly different between WT and IL-10 -/- mice, plasma concentrations in SAA and sTNFRII, and inflammatory state of the cecum from the same mice. Heatmaps were used to display the correlation matrices, and a cutoff value of 0.6 was applied to the absolute value of the coefficient |r| so that the map only represents the correlations between two entities above the cutoff. The value and the sign of the correlation were then color-coded (gradient of red colors for positive values, gradient of blue colors for negative values). The presence of colored pixels between specific metabolites reveals a correlation (above the cutoff) between these molecules that may reflect a functional association.

Results Histopathology of Distal Colon and Cecum. Histology of colon and cecum intestinal segments showed inflammatory changes in all IL-10 -/- mice at the age of 8, 16, and 24 weeks, whereas no alteration of the cecal tissues was observed at 1 week of age when compared to WT mice (Figure 1). Notably, the grade of inflammation increased with the age and was more prominent in the cecum compared to the colon of mice at the age of 8 and 16 weeks, and similar at the age of 24 weeks. Characterization of the histological sections revealed that the colon and the cecum exhibited inflammatory cell infiltration (lymphocytes and polymorphonuclear cell (PMN)) into the mucosa and the submucosa (Figure 1). However, the changes were in a mild range, and the epithelial damage was limited to hyperproliferation and irregular crypts. Moreover, the participation of PMN infiltration is lower in younger mice when compared to older animals. Gradual Colitis and Inflammation Markers. Concentrations of serum amyloid A (SAA) and soluble tumor necrosis factor II (sTNRFII) were measured in blood of WT and IL-10 -/- mice at the age of 1, 8, 16, and 24 weeks. Expression of sTNFRII was increased in IL-10 -/- mice from the age of 8 weeks onward when compared to WT animals, whereas no significant differences were observed in 1 week old animals (Figure 1C). Interestingly, blood levels of sTNFRII also increased from the age of 1 week to 8 weeks in both WT and IL-10 -/- mice, and reached a value that remained unchanged until the age of 24 weeks. Expression of SAA showed an upward trend in IL-10 -/- mice compared to WT animals at the age of 8, 16, and 24 weeks, and the changes were more marked at 16 and 24 weeks, yet not significant. Moreover, no age-dependent changes were observed in WT mice. Analysis of 1H NMR Spectroscopic Profiles of Blood Plasma. Examples of typical 1H CPMG and diffusion-edited NMR spectra of plasma samples obtained from WT and IL-10


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Figure 1. Histological, morphological and inflammatory changes during colitis. (A) Histological characterization of the cecal and colonic inflammatory state. (B) Animal weights. (C) Histopathological results for the cecum obtained from Il-10 -/- and wild type mice. Histological examination was performed on intestinal sections of 4 µm embedded in paraffin, stained with hematoxilin/eosin and magnified 200 times. (D) Time dependent changes in blood concentrations of Serum Amyloid A (SAA) and soluble tumor necrosis factor receptor II (sTNFRII) in WT and IL-10 -/- mice. Key: The values for the IL-10 -/- animals were compared at each time point with the wild type mice. The single (*) and double (**) asterisks designate significant difference at 95% and 99% confidence level, respectively. Observations obtained by histological analysis are coded using the Katakura score system.

-/-mice are shown in Figure 2. CPMG NMR data provide a clear profile of low molecular weight components through reduction of spectral contribution of broad signals from large molecules. In addition, diffusion-edited NMR spectra provide a complementary profile of the protein and lipid resonances, and the signals of low molecular weight metabolites are severely attenuated. The assignment of the peaks to specific metabolites was achieved based on the literature,37,38 and confirmed by 2D 1H NMR spectroscopy. Further assignment of the metabolites was also accomplished with the use of Statistical TOtal

Correlation SpectroscopY (STOCSY) on 1D spectra.39 The 1H NMR plasma profile exhibit a broad set of resonances arising from lipoproteins and lipids together with many sharper peaks arising from major low molecular weight molecules, such as glucose, and ketone bodies (3-D-hydroxybutyrate and acetoacetate), amino acids, and organic acids such as acetate, lactate, succinate, pyruvate and citrate (Table 1). Visual inspection of the 1H NMR spectra of plasma revealed differences in overall composition between WT and IL-10 -/mice, as illustrated in Figure 2. For instance, IL-10 -/- mice Journal of Proteome Research • Vol. 8, No. 5, 2009 2379

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Figure 2. NMR-based metabolic profiling of blood plasma. Typical 600 MHz proton nuclear magnetic resonance (1H NMR) Carr-Purcell-Meiboom-Gill (CPMG) (A and B) and diffusion-edited (C and D) spectra of blood plasma from IL-10 -/- (A and C) and wild type mice (B and D) at 24 weeks of age. The spectra in the aromatic region (δ 5.2-8.5) were magnified compared to the aliphatic region (δ 0.7-4.5). Key: Arg, arginine; Asn, asparagine; Asp, aspartate; GPC, glycerophosphocholine; HDL, high density lipoproteins; LDL, low density lipoproteins; Lys, lysine; PC, phosphocholine; VLDL, very low density lipoproteins.

showed a marked reduction of global levels of lipoprotein and lipids, lower levels of choline, phosphocholine (PC) and glycerophosphocholine (GPC). These changes were associated with an increase of lactate, alanine and glutamine. However, to establish a global overview of the gradual development of colitis, a more formal comparison of the metabolic profiles was carried out using multivariate data analysis. Time-Dependent Metabolic Changes Associated with Gradual Development of Colitis. Initial data analyses were conducted using PCA to assess metabolic similarities between samples through modeling the main sources of variations in the metabolite profiles. PCA was initially performed on the CPMG metabolic profiles obtained from mice aged 1, 8, 16, and 24 weeks (data not shown). The distribution of the biochemical profiles along the two first principal components by means of scores plot revealed a statistically significant separation of the samples obtained from 1 week old animals. This effect is directly related to the smaller volume of collected blood plasma and consequently to a dilution factor. Therefore, the samples obtained from young mice were analyzed separately to avoid introducing any biases during chemometric analysis. PCA of 1 H CPMG NMR spectra of blood samples collected at 8, 16, and 24 weeks was generated using 4 principal components explaining 20, 9.5, 8 and 7% of the total variance, respectively. The PCA scores plot revealed a comapping of samples according to their genetic background along PC2 and PC3 (WT and IL-10 -/-) (Figure 3A), while PC1 described high interindividual variability. Analysis of the distribution of the samples showed that metabolic differences became more significant over time, and the greatest ones were observed at the age of 24 weeks (Figure 3A). PCA was also applied to diffusion-edited 2380


metabolic profiles using a total of 4 components, which explained 43, 24, 12 and 7% of the total variance. Intriguingly, metabolic variations modeled along PC 1 and PC2 revealed high interindividual variations in IL-10 -/- animals (data not shown), while along PC 3 and PC 4, a clear time-dependent separation of WT and IL-10 -/- animals was observed (Figure 3B). To identify time-dependent profiles, another unsupervised method based on ICA was employed. At the difference of supervised methods, such as O-PLS-DA, the information contained in NMR-based metabolic profiles was differentiated without the contribution of an external supervision. ICA was performed on 1H CPMG NMR metabolic profiles obtained from mice aged 8, 16, and 24 weeks. The model was generated using five Independent Components (IC) and excluding the spectral regions corresponding to lactate (δ 1.31-1.34) and citrate (δ 2.50-2.72) due to positional peak shift. These ICs explained 85.73% of the total sum of squares variance of the data. The mixing matrix results obtained for IC 4 (11.59% of the total variance) showed two different trends ascribable to the genetic background and the age of the animals (Figure 3C). Notably, control animals were characterized by a linear relationship between the metabolic profiles and the time, whereas IL-10 -/- animals showed nonlinear time-dependent changes. Moreover, the mixing matrix score plots revealed different interindividual variability within each subset, and the variations were more important in IL-10 -/- animals and increasing with age. The inspection of the IC profile (Figure 3D) revealed that aging and development of colitis were associated with increased levels of phospholipids rich lipoprotein particles, glycosylated

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Table 1. Table of Assignment of the Observed NMR Peaks of Blood Plasma metabolites

moieties

δ1H (ppm) and multiplicity

3-D-Hydroxybutyrate Acetate Acetoacetate Alanine Allantoin Arginine Asparagine Aspartic acid Choline Citrate Creatine Ethanolamine Fumarate Glutamate Glutamine Glycerol in plasma lipids Glycerophosphocholine (GPC) Glycine Histidine Isoleucine Lactate Leucine Lipids Lipoproteins Lysine Methionine N-acetyl-glyco-proteins Phenylalanine Phosphocholine (PC) Pyruvate Taurine Trimethylamine (TMA) Tyrosine Urea Valine R-Glucose β-Glucose

CH, CH2, γCH3 CH3 CH3, CH2 RCH, βCH3 CH RCH, γCH2, δCH2 RCH, βCH2 RCH, βCH2 N-(CH3)3, OCH2, NCH2 Non-equivalent CH2 N-CH3, CH2 NH2CH2, CH2OH CH RCH, βCH2, γCH2 RCH, βCH2, γCH2 CH, CH2, CH2 N-(CH3)3, OCH2, NCH2 CH2 4-CH, 2-CH RCH, βCH, γCH3, δCH3 RCH, βCH3 RCH, δCH3, δCH3 CH3, (CH2)n, CH2-CdC, CH2-CdO, )C-CH2-C), -CH)CH-CH2-CH3 -(CH2)n RCH, γCH2, δCH2, εCH2 RCH, βCH2, γCH2, δCH3 CH3 2,6-CH, 3,5-CH, 4-CH N(CH3)3, OCH2, NCH2 CH3 N-CH2, S-CH2 CH3 CH, CH NH2 RCH, βCH, γCH3 C1H, C2H, C3H C4H, C5H, 1/2 CH2-C6 C1H, C2H, C3H C4H, C5H, 1/2 CH2-C6

4.16(dt), 2.41(dd), 1.20(d) 1.91(s) 2.29(s), 3.49(s) 3.77(q), 1.47(d) 5.41(s) 3.76(t), 1.63(m), 3.23(t) 3.99(m), 2.86(m) 2.94(m) 3.89(m), 2.79(m), 2.82 (m) 3.2(s), 4.05(t), 3.51(t) 2.55(d), 2.65 (d) 3.03(s), 3.92(s) 3.15 (t), 3.83 (t) 6.53(s) 3.75(m), 2.08(m), 2.34(m) 3.77(m), 2.15(m), 2.44(m) 3.91 (m), 3.64(m), 3.56(m) 3.22(s), 4.32(t), 3.68(t) 3.55(s) 7.02(s), 7.73(s), 3.65(d), 1.95(m), 0.99(t), 1.02(d) 4.11(q), 1.32(d) 3.72(t), 0.91(d), 0.94(d) 0.89(m), 1.27(m), 2.0m), 2.3(m), 2.78 (m), 5.3(m) 0.87 (t), 1.29(m), 1.57(m) 3.77(t), 1.72(m), 1.47(m), 3.01(t) 3.78(m), 2.14(m), 2.6(dd), 2.13(s) 2.04 (s) 7.40(m), 7.33(m), 7.35(m) 3.22(s), 4.21(t), 3.61(t) 2.41(s) 3.26(t), 3.40(t) 2.87(s) 7.16(m), 6.87(m) 5.90(s) 3.6(d), 2.26(m), 0.98(d), 1.04(d) 5.25 (d), 3.56(m), 3.71(m), 3.41(m), 3.84(m), 3.74(m) 4.65 (d), 3.25 (t), 3.50(m), 3.48(m), 3.48(m), 3.88(m)

a

Key: s, singlet; d, doublet; t, triplet; q, quartet; m, multiplet; dd, doublet of doublets.

proteins, polyunsaturated fatty acids, pyruvate and alanine, and reduced levels of triglycerides rich lipoproteins and glutamine. Characterization of Differential Metabolite Patterns Associated with Colitis. The O-PLS-DA method was then applied to maximize the separation of groups obtained by pairwise comparison of 1H CPMG and diffusion-edited spectra of blood plasma obtained from WT and IL-10 -/- mouse samples. The metabolic profiles could be clearly clustered at the different ages as observed through the high and positive value of the model predictability Q2Y (Table 2). Analysis of the corresponding coefficient plots allows identification of influential metabolites discriminating the groups of blood plasma samples. The direction of the signals in the coefficient plots relative to zero indicates positive or negative covariance with the class of interest (Figure 4). In addition, the color code is representative of the discriminant power which is given as the square value of the correlation coefficient. Representative signals of these metabolites were integrated and are reported in Table 3. At the age of 1 week, IL-10 -/- mice were characterized by higher levels of citrate, glutamine, fumarate and lower levels of glucose and dimethylglycine (DMG) when compared to controls. These changes were associated with variations in lipoprotein composition characterized by increased levels of polyunsaturated lipids, high and low density lipoprotein (HDL and LDL) and decreased very low density lipoprotein (VLDL) concentrations when compared to WT mice.

At the age of 8 weeks, IL-10 -/- mice showed increased levels of alanine, arginine, glutamine, lactate, pyruvate, succinate, fumarate, creatine, DMG, choline in phospholipids, glycerophosphocholine (GPC), and glycoproteins when compared to controls. In addition, IL-10 -/- mice had compositional changes in unsaturated fatty acids, higher levels of certain polyunsaturated lipids and decreased levels of VLDL when compared to control mice. Sixteen week-old IL-10 -/- animals showed higher levels of isoleucine, citrate, pyruvate, lactate, phenylalanine, choline in phospholipids, glycoproteins, and lower levels of glutamine, leucine, tyrosine, methionine, creatine, DMG, TMA, and glucose when compared to 16 week-old controls. These changes were associated with compositional variations of polyunsaturated fatty acids, increase of HDL, LDL and certain unsaturated fatty acids, and decreased levels of VLDL when compared to WT animals. At the age of 24 weeks, IL-10 -/- mice had increased levels of alanine, arginine, isoleucine, phenylalanine, and a decrease of TMA when compared to WT mice. This differential metabolite pattern was associated with an upward trend in HDL, LDL, higher levels of certain polyunsaturated fatty acids, and lowered concentrations of VLDL when compared to controls. At the difference of ICA profiles, O-PLS-DA coefficients plots describe relative changes (positive and negative) of metabolite concentrations discriminating the groups of animals. Application of ICA generated numerical spectroscopic constructs Journal of Proteome Research • Vol. 8, No. 5, 2009 2381

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Figure 3. Blood plasma metabolic changes associated with colitis development. Principal component analysis (PCA) scores plot derived from proton nuclear magnetic resonance (1H NMR) Carr-Purcell-Meiboom-Gill (CPMG) (A), and diffusion-edited (B) spectra of plasma indicating discrimination between wild type (WT, black) and IL-10 -/- (red) mice. Independent component analysis (ICA) mixing matrix (C) and Independent component results (D) derived from 1H NMR CPMG spectra of plasma. The X-axis displayed in the mixing matrix corresponds to individual animals from each group at the different ages. The red line displayed in the mixing matrix corresponds to the median obtained for a group of animals at the time point indicated. Key: Ala, alanine; Gln, glutamine; PUFAs, polyunsaturated fatty acids; UFAs, unsaturated fatty acids.

summarizing metabolite presence and proportion that contribute to describe time-dependent differences between controls and IL-10 -/- animals. Interestingly, the major metabolic changes associated with the development of inflammation obtained by O-PLS-DA were strongly similar with the ones modeled using ICA. Such observations highlight the potential of using ICA to provide a global picture of time-dependent metabolic events that can be completed with a more detailed investigation of the profiles using supervised methodologies. Correlation Analysis of Metabolite Functional Relationships. A correlation analysis was conducted to identify any latent metabolic links between plasma inflammation biomarkers, histological inflammatory state and blood plasma metabolites (Figure 5). In addition to summarizing major metabolic changes in relation to genetic background, such as compositional changes in blood plasma lipids and lipoproteins, the heatmap highlighted strong positive correlations between the degree of inflammation of the cecal and colonic tissues, the plasma concentrations of sTNFRII and the levels of glycoproteins. Moreover, the SAA concentration was strongly 2382


correlated only with colonic inflammation, while sTNFRII variations were strongly correlated with lactate levels. Interestingly, blood plasma levels of glycoproteins were positively correlated with specific signals of polyunsaturated and unsaturated lipids.

Discussion In the present study, we have characterized the metabolism associated with gradual development of colitis using NMRbased metabolic profiles of blood plasma of WT and IL-10 -/mice combined with measures of blood plasma inflammatory biomarkers and histopathology of cecum and colon. The metabonomic approach provides a holistic inspection of metabolic homeostatic processes in relation to both genetic background and development of inflammation. Histopathology, Inflammatory Biomarkers and Blood Plasma Metabolic Profile Describe the Gradual Development of Inflammation. IL-10-deficient mice are growth retarded, anemic and spontaneously develop chronic intestinal

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Metabolic Assessment of Moderate Experimental Colitis 40

disease in humans and the tissue destructive processes induce a systemic reaction, the acute phase response, which induces multiple physiologic adaptations, including the hepatic synthesis of acute phase proteins, including C-reactive protein (CRP) and SAA.41 Moreover CRP and SAA are clearly associated with colitis development in inflammatory bowel disease patients42,43 and in model of colitis genetically induced, such as IL-10-/- mice or IL-2-/- mice.44,45 Such changes can be monitored at the system level via measurement of circulating levels of the inflammatory mediators, e.g. sTNFRII, which stimulates the acute phase reaction leading first to an increase of acute phase proteins e.g. SAA as observed in our present study. Indeed, a strong increase of sTNFRII was observed after 8 weeks, although no difference was observed for SAA levels at the same time point (Figure 5). At 16 weeks, the SAA level was increased while a stabilization of sTNFRII occurred (Figure 5). These increased concentrations of SAA and sTNFRII significantly correlated with the histological development of inflammation from the age of 8 weeks onward (Figure 5), as previously reported for acute colitis.46,47

Figure 4. Chemometric analysis of plasma metabolic profiles. Orthogonal projection to latent structure discriminant analysis (O-PLS-DA) scores and coefficient plots derived from proton nuclearmagneticresonance(1HNMR)Carr-Purcell-Meiboom-Gill (CPMG) (A) and diffusion-edited (B) spectra of blood plasma illustrating the discrimination between wild type (WT, black) and IL-10 -/- (red) mice at the age of 16 weeks. The color code corresponds to the correlation coefficients of the variables. Key: Ala, alanine; Arg, arginine; DMG, dimethylglycine; Gln, glutamine; HDL, high density lipoproteins; LDL, low density lipoproteins; Leu, Leucine; Lys, Lysine; TMA, trimethylamine; Val, valine; VLDL, very low density lipoproteins.

inflammation due to a loss of tolerance toward the gut microbiota.10 The involved immune and inflammatory processes are characterized by an enhanced Th1 response in the early course of disease, and high Th2 cytokines release at a later stage.10 Here, IL-10 -/- mice developed moderate bowel inflammation from the age of 8 weeks on to reach a maximum at the age of 24 weeks accompanied by important physiological changes in cecum and colon. These intestinal tissues were characterized by increased inflammatory cell infiltration into the mucosa and the submucosa, development of irregular crypts, abscesses and hyperplasia (Figure 1), as previously reported.10 In particular, mucosal inflammation was associated with cellular infiltration of lymphocytes and PMN. The participation of the PMN was higher in older IL-10 -/- mice, which suggested more active lesions in these animals. It has been previously reported that macrophages and neutrophils are progressively recruited into the lamina propria by secretion of pro-inflammatory cytokines and chemokines during the inflammatory response.10 This cellular infiltration is characteristic of active IBD and eventually leads to tissue damage, as observed here with the development of crypt abscesses at the age of 24 weeks. The production of cytokines in immune cells as IL-6 and sTNRII, known to be increased in cases of inflammatory bowel

Acute and chronic inflammations are generally associated with a profound change in the apolipoprotein composition of HDL, and the SAA protein family is the major component.48,49 This structural relationship has suggested that acute phase SAA may play a role in lipoprotein functions in inflammation.48,49 Notably, increased plasma concentration of LDLs and/or remnants of triglyceride-rich lipoproteins are two major factors initiating inflammatory disorders via modification by oxidation or by aggregation.50 Interestingly, the correlation analysis between the time-dependent increase in the blood plasma levels of the inflammatory markers (SAA and sTNFRII) against the full diffusion-edited metabolic profiles revealed unique biochemical relationships (Figure 5, Supplementary Figure 1 in Supporting Information). In particular, the 1H NMR metabolic profiles obtained from blood plasma show a marked correlation between total glycosylated protein and SAA and sTNFRII concentrations over time, which may reflect the overall inflammatory-related shift in protein metabolism. Interestingly, this metabolic feature was also observed in a mouse model of chemically induced colitis (Supplementary Figure 2 in Supporting Information). The sTNFRII concentrations were also inversely correlated with VLDL and correlated with LDL and polyunsaturated fatty acids. Notably, the relative decrease of the level of VLDL particles in IL-10 -/- mice can be associated with an increased activity of lipoprotein lipase in the capillary beds in adipose tissue and skeletal muscles to remove triglycerides. Inflammatory processes were indeed characterized by increased blood levels of secretory phospholipase A2, which promotes hydrolysis of phospholipids in LDL and VLDL and generates PUFAs that can be oxidized and promote inflammation.51 Application of lipidomics to blood plasma samples indicated that IL-10 -/mice showed higher levels of n-6 PUFA arachidonic acid, key precursor of inflammatory prostaglandin and leukotriene,52 and lower levels of triglycerides when compared to controls at 16 and 24 weeks (Supplementary Figure 3 in Supporting Information). This lipoprotein metabolic signature of inflammatory processes is strongly associated with changes in phospholipid metabolism, such as increased levels of choline in phospholipids and GPC, which promote the formation of foam cells and inflammation.51 This modification of the phospholipid metabolism is the result of an alteration of the plasma phospholipid lipid transfer protein activity, a metabolic feature previJournal of Proteome Research • Vol. 8, No. 5, 2009 2383

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Martin et al.

Figure 5. Integration of metabolic correlations. The heatmaps were derived from correlations between plasma inflammation biomarkers, histological inflammatory state and blood plasma metabolites found to be significantly different between wild type (WT) and IL-10 -/mice. The cutoff value of 0.6 was applied to the absolute value of the coefficient |r| for displaying the correlations between metabolites. Correlation values are displayed as a color-coded heatmap according to correlation value (gradient of red colors for positive values, gradient of blue colors for negative values). Key: DMG, dimethylglycine; GPC, glycerophosphocholine; HDL, high density lipoproteins; Lipo, lipoproteins; PUFAs, polyunsaturated fatty acids; SAA, Serum Amyloid A; TMA, trimethylamine; TNFR, soluble tumor necrosis factor receptor II; Unsaturated, unsaturated fatty acids. Table 2. O-PLS-DA Model Summary for the Different Discriminations among 1H NMR Spectra of Plasmaa discrimination analyzed\sample

WT vs IL-10 -/mice at 1 week

WT vs IL-10 -/mice at 8 weeks



CPMG spectra Diffusion-edited spectra

Q2Y ) 74%, R2X ) 53% Q2Y ) 75%, R2X ) 60%

Q2Y ) 74%, R2X ) 46% Q2Y ) 58%, R2X ) 74%

Q2Y ) 76%, R2X ) 36% Q2Y ) 75%, R2X ) 74%

Q2Y ) 59%, R2X ) 45% Q2Y ) 27%, R2X ) 69%

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.

ously associated with plasma levels of SAA, CRP, glucose and Apo-I-HDL particles,51 as presented here. These metabolic features were previously reported for acute colitis,12 and they are here observed at 1 week of age prior to changes in body weight, inflammatory biomarkers and epithelial damages. Such an observation suggests that silencing the IL-10 gene results in an alteration of the lipoprotein cycle that may increase the inflammatory susceptibility of these animals. Inflammatory-Related Changes in Energy Metabolism. Since plasma lipoproteins transport lipids, the interaction of acute phase proteins with plasma lipoproteins may have important metabolic consequences during acute illness when 2384


host dependence on lipid for fuel is increased. Although the body weights of moderately inflamed mice did not significantly differ from their wild-type controls, a consistent disruption of normal energy homeostasis in IL-10 -/- mice was evidenced from the age of 8 weeks on with coordinated changes in the plasma concentrations of lipoproteins and metabolites involved in energy metabolism. For example, elevated levels of lactate, citrate and pyruvate and decreased plasma glucose level in the IL-10-deficient mice brought additional evidence of increased fatty acid oxidation and suggested extensive glycolysis to accommodate a higher energy demand or decreased energy availability due to decreased nutrient absorption through the

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Metabolic Assessment of Moderate Experimental Colitis Table 3. Blood Plasma Metabolite Differences between WT and IL-10 -/- Mice

a

animal model/ metabolite (chemical shift)

Alanine δ 1.48 Arginine δ 1.68 Choline in phospholipids δ 3.20-3.23§ Citrate δ 2.55 Creatine δ 3.03 Dimethylglycine δ 2.93 Fumarate δ 6.51 Glucose δ 4.64 Glutamine δ 2.45 Glycerophosphocholine δ 4.35 Glycoproteins δ 4.16 Isoleucine δ 0.92 Lactate δ 4.11 Leucine δ 1.01 Lipoproteins mainly HDL/LDL δ 0.81-0.88§ Lipoproteins δ 0.88§ Lipoproteins δ 1.18-1.25§ Lipoproteins δ 1.25-1.27§ Lipoproteins δ 1.27-1.33§ Lipoproteins mainly VLDL δ 1.56§ Lysine δ 1.72 Methionine δ 2.14 Phenylalanine δ 7.44 Polyunsaturated fatty acids δ 2.73§ Polyunsaturated fatty acids δ 2.76§ Polyunsaturated fatty acids δ 2.78§ Pyruvate δ 2.41 Succinate δ 2.37 Trimethylamine δ 2.87 Tyrosine δ 6.9 Unsaturated fatty acids δ 5.25§

WT at 1 week

IL-10 -/ - at 1 week

WT at 8 week

IL-10 -/ -at 8 week

0.59 ( 0.06 1.0 ( 0.19 2.32 ( 0.18

0.54 ( 0.11 0.97 ( 0.25 2.38 ( 0.19

0.54 ( 0.05 0.86 ( 0.08 2.22 ( 0.08

0.73 ( 0.1** 1.11 ( 0.17* 2.37 ( 0.1*

0.61 ( 0.09 1.06 ( 0.13 2.29 ( 0.13

0.59 ( 0.07 1.12 ( 0.14 2.44 ( 0.12*

0.55 ( 0.04 1.01 ( 0.12 2.31 ( 0.12

0.70 ( 0.12* 1.15 ( 0.07* 2.41 ( 0.17

4.46 ( 1.06 0.23 ( 0.02 0.11 ( 0.01

4.52 ( 0.88 0.29 ( 0.04* 0.14 ( 0.02*

3.44 ( 1.1 0.28 ( 0.04 0.15 ( 0.03

4.37 ( 0.69* 0.23 ( 0.01* 0.09 ( 0.01*

4.18 ( 1.18 0.26 ( 0.03 0.14 ( 0.03

4.56 ( 1.16 0.27 ( 0.05 0.11 ( 0.02

10.78 ( 2.26 17.87 ( 8.02* 0.46 ( 0.09 0.42 ( 0.11 0.14 ( 0.03 0.09 ( 0.03*

IL-10 -/ IL-10 -/ WT at 16 week - at 16 week WT at 24 week - at 24 week

0.009 ( 0.007 0.001 ( 0.001* 0.007 ( 0.001 0.01 ( 0.003* 0.007 ( 0.003 0.006 ( 0.001 0.006 ( 0.002 0.009 ( 0.005 0.67 ( 0.05 0.47 ( 0.14* 1.06 ( 0.14 1.06 ( 0.1 1.21 ( 0.1 0.94 ( 0.03* 1.09 ( 0.11 0.98 ( 0.11 0.42 ( 0.06 0.53 ( 0.05* 0.31 ( 0.04 0.40 ( 0.06* 0.41 ( 0.1 0.32 ( 0.02* 0.38 ( 0.05 0.41 ( 0.11 0.57 ( 0.05

0.52 ( 0.15

6.87 ( 0.59 6.85 ( 0.62 0.54 ( 0.06 0.45 ( 0.12 1.54 ( 0.09 1.38 ( 0.23 0.21 ( 0.04 0.21 ( 0.06 43.23 ( 3.21 52.34 ( 3.94*

0.44 ( 0.1

0.58 ( 0.11*

8.54 ( 0.16 0.51 ( 0.05 2.23 ( 0.37 0.19 ( 0.03 44.4 ( 2.45

9.11 ( 0.23* 0.54 ( 0.04 3.3 ( 0.67* 0.17 ( 0.02 47.68 ( 2.2

46.46 ( 5.24 40.35 ( 0.87* 35.25 ( 1.38 31.96 ( 1.17* 57.17 ( 3.23 63.68 ( 6.43*

56.75 ( 3.73

43.45 ( 7.15

43.69 ( 4.67 34.31 ( 3.03*

38.5 ( 2.58

60.94 ( 2.83

126.44 ( 33.19 77.99 ( 12.06* 69.55 ( 3.91 55.41 ( 4.76* 22.47 ( 1.89 18.7 ( 2.33* 20.36 ( 0.28 18.55 ( 0.78*

0.57 ( 0.14

0.54 ( 0.07

0.57 ( 0.12

0.52 ( 0.09

9.0 ( 0.39 10.99 ( 1.5* 9.43 ( 0.42 10.15 ( 0.84 0.55 ( 0.03 0.64 ( 0.05* 0.54 ( 0.05 0.62 ( 0.04* 2.8 ( 0.41 3.33 ( 0.44* 2.74 ( 0.36 3.31 ( 0.82 0.20 ( 0.02 0.15 ( 0.02* 0.18 ( 0.02 0.16 ( 0.01 43.99 ( 2.4 51.37 ( 3.59* 46.39 ( 1.71 49.03 ( 3.52 33.46 ( 2.2

32.01 ( 0.61

32.96 ( 1.1

54.8 ( 4.64 64.67 ( 4.77* 58.89 ( 3.37 36.89 ( 8.2

34.31 ( 2.12

36.74 ( 4.6

63.13 ( 10.81 52.59 ( 3.76 59.51 ( 3.77 19.59 ( 1.08 17.32 ( 0.63* 18.88 ( 0.55

32.84 ( 3.93 60.95 ( 4.51 33.73 ( 13.9 53.74 ( 16.57 18.02 ( 1.79

0.3 ( 0.05 0.17 ( 0.03 0.09 ( 0.03 3.07 ( 0.19

0.27 ( 0.08 0.15 ( 0.03 0.06 ( 0.03 3.57 ( 0.26*

0.31 ( 0.03 0.14 ( 0.02 0.08 ( 0.01 3.58 ( 0.15

0.39 ( 0.03* 0.18 ( 0.02* 0.1 ( 0.01* 3.95 ( 0.31*

0.38 ( 0.05 0.18 ( 0.02 0.1 ( 0.01 3.72 ( 0.22

0.38 ( 0.03 0.15 ( 0.01* 0.12 ( 0.01* 4.41 ( 0.14*

0.37 ( 0.04 0.15 ( 0.02 0.09 ( 0.01 3.82 ( 0.26

0.40 ( 0.05 0.17 ( 0.03 0.11 ( 0.02* 3.92 ( 0.18

6.66 ( 0.65

7.88 ( 0.38*

7.41 ( 0.32

7.51 ( 0.22

7.58 ( 0.43

8.09 ( 0.29*

7.57 ( 0.32

8.41 ( 1.02

9.5 ( 0.48

8.75 ( 1.65

6.79 ( 0.14

7.07 ( 0.26

7.02 ( 0.56

7.78 ( 0.42*

6.96 ( 0.24

8.0 ( 0.9*

0.26 ( 0.07 0.12 ( 0.02 0.05 ( 0.02 0.05 ( 0.05 6.53 ( 0.87

0.22 ( 0.01 0.12 ( 0.02 0.02 ( 0.02 0.05 ( 0.01 7.42 ( 0.63

0.19 ( 0.01 0.05 ( 0.01 0.08 ( 0.01 0.06 ( 0.01 7.31 ( 0.54

0.28 ( 0.05* 0.06 ( 0.01* 0.07 ( 0.02 0.07 ( 0.01* 8.74 ( 0.92*

0.21 ( 0.03 0.05 ( 0.02 0.09 ( 0.02 0.07 ( 0.01 7.69 ( 0.46

0.27 ( 0.02* 0.05 ( 0.01 0.06 ( 0.02* 0.06 ( 0.01* 9.31 ( 0.73*

0.23 ( 0.02 0.06 ( 0.01 0.09 ( 0.01 0.06 ( 0.01 8.09 ( 0.53

0.29 ( 0.07 0.06 ( 0.01 0.07 ( 0.01* 0.06 ( 0.01 8.3 ( 0.43

a Key: Values are given as area normalized peak signals mean ( standard deviation. Section mark (§) designates spectral signals integrated from diffusion-edited spectra, all the other signals being integrated from CPMG spectra. The values for the IL-10 -/- animals were compared at each time point with the wild type mice. Asterisk (*) designates significant difference at 95% confidence level.

impaired intestine. Our observations are supported by previous findings on differential expression of key enzymes of the citrate cycle associated with the development of inflammation.27 Moreover, chronic intestinal inflammation has recently been linked to an energy deficient state of the gut epithelium with alterations in the oxidative metabolism,13,27 which may be related to this loss of energy homeostasis. In addition to discrepancies in the energy homeostasis between WT and IL-10 -/- mice, higher levels of free amino acids (arginine, lysine, phenylalanine, tyrosine, glutamine, alanine) were observed in the IL-10 -/- mice, the differences being the greatest at 8 weeks of age (Table 3). IL-10 deficient mice indeed gradually develop diarrhea and muscle atrophy associated with increased protein catabolism,10 which may lead to the augmentation of amino acid concentrations observed in blood in the current investigation. In particular, the elevation of alanine and glutamine in plasma was associated with decreased levels of the branched-chain amino acids valine and leucine, which reflected both breakdown of proteins and

increased gluconeogenesis and carbon flux through the anapleurotic pathway to support ATP production. In addition, the relative increased plasma concentration of arginine in IL-10 deficient mice could reflect alteration of the nitric oxide pathway, a key metabolic process involved in the pathogenesis of IBD.53 As a conclusion, our results showed that silencing the IL-10 gene in mice leads to gradual development of colitis in parallel to a multifactorial disruption of normal metabolism. Having demonstrated the potential of metabonomics to decipher colitis-induced homeostasis loss by metabolic profiling of blood plasma, it is now feasible to target specific metabolic pathways to get insights into molecular mechanisms in future studies. In particular, this metabonomic study could be completed by urine and fecal analysis to gain insights in time-averaged metabolic changes and on the activity of the gut microbiota. The perspective of preventing IBD and normalizing their undesirable effects by specific nutritional interventions, such as probiotics or other functional ingredients, could benefit from Journal of Proteome Research • Vol. 8, No. 5, 2009 2385

research articles the application of metabonomic for therapeutic surveillance, assessment of treatment efficacy, and therefore, it should have an impact on management of IBD. Abbreviations: CPMG, Carr-Purcell-Meiboom-Gill; GPC, Glycerophosphorylcholine; ICA, Independent Component Analysis; IBD, Irritable Bowel Disease; NMR, Nuclear Magnetic Resonance; O-PLS-DA, Orthogonal Projection to Latent Structure Discriminant Analysis; PCA, Principal Component Analysis; PLS-DA, Projection to Latent Structure Discriminant Analysis.

Acknowledgment. The authors acknowledge the help and input of Ziad Ramadan for chemometric analysis. Supporting Information Available: Supplementary material and methods, procedure of animal handling for trinitrobenzenesulfonic acid (TNBS)-induced colitis; Supplementary Figure 1, statistical total correlation spectroscopy (STOCSY) coefficients plots derived from proton nuclear magnetic resonance (1H NMR) diffusion-edited spectra of blood plasma; Supplementary Figure 2, projection to latent structurediscriminant analysis (PLSDA) coefficients plot derived from 1 H NMR diffusion-edited spectra of blood plasma; Supplementary Figure 3, typical 600 MHz proton nuclear magnetic resonance (1H NMR) diffusion-edited spectra of pooled blood plasma samples from wild-type and IL-10 -/- mice. This material is available free of charge via the Internet at http:// pubs.acs.org. References (1) Ahmad, T.; Tamboli, C. P.; Jewell, D.; Colombel, J. F. Clinical relevance of advances in genetics and pharmacogenetics of IBD. Gastroenterology 2004, 126 (6), 1533–1549. (2) Loftus, E. V., Jr. Inflammatory bowel disease extending its reach. Gastroenterology 2005, 129 (3), 1117–1120. (3) Loftus, E. V., Jr. Clinical epidemiology of inflammatory bowel disease: Incidence, prevalence, and environmental influences. Gastroenterology 2004, 126 (6), 1504–1517. (4) Levine, A. D.; Fiocchi, C. Immunology of inflammatory bowel disease. Curr. Opin. Gastroenterol. 2000, 16 (4), 306–309. (5) Werner, T.; Haller, D. Intestinal epithelial cell signalling and chronic inflammation: From the proteome to specific molecular mechanisms. Mutat. Res. 2007, 622 (1-2), 42–57. (6) Hampe, J.; Heymann, K.; Kruis, W.; Raedler, A.; Folsch, U. R.; Schreiber, S. Anticipation in inflammatory bowel disease: a phenomenon caused by an accumulation of confounders. Am. J. Med. Genet. 2000, 92 (3), 178–183. (7) Schreiber, S.; Hampe, J. Genomics and inflammatory bowel disease. Curr. Opin. Gastroenterol. 2000, 16 (4), 297–305. (8) Haller, D. Intestinal epithelial cell signalling and host-derived negative regulators under chronic inflammation: to be or not to be activated determines the balance towards commensal bacteria. Neurogastroenterol. Motil. 2006, 18 (3), 184–199. (9) Swidsinski, A.; Ladhoff, A.; Pernthaler, A.; Swidsinski, S.; LoeningBaucke, V.; Ortner, M.; Weber, J.; Hoffmann, U.; Schreiber, S.; Dietel, M.; Lochs, H. Mucosal flora in inflammatory bowel disease. Gastroenterology 2002, 122 (1), 44–54. (10) Kuhn, R.; Lohler, J.; Rennick, D.; Rajewsky, K.; Muller, W. Interleukin-10-deficient mice develop chronic enterocolitis. Cell 1993, 75 (2), 263–274. (11) de Waal, M. R.; Abrams, J.; Bennett, B.; Figdor, C. G.; de Vries, J. E. Interleukin 10(IL-10) inhibits cytokine synthesis by human monocytes: an autoregulatory role of IL-10 produced by monocytes. J. Exp. Med. 1991, 174 (5), 1209–1220. (12) Caligiuri, G.; Rudling, M.; Ollivier, V.; Jacob, M. P.; Michel, J. B.; Hansson, G. K.; Nicoletti, A. Interleukin-10 deficiency increases atherosclerosis, thrombosis, and low-density lipoproteins in apolipoprotein E knockout mice. Mol. Med. 2003, 9 (1-2), 10–17. (13) Shkoda, A.; Ruiz, P. A.; Daniel, H.; Kim, S. C.; Rogler, G.; Sartor, R. B.; Haller, D. Interleukin-10 blocked endoplasmic reticulum stress in intestinal epithelial cells: impact on chronic inflammation. Gastroenterology 2007, 132 (1), 190–207.

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PR801006E

Journal of Proteome Research • Vol. 8, No. 5, 2009 2387

Metabolic Assessment of Gradual Development of Moderate

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