Quantitative Metabolomic Profiling of Serum and Urine in DSS-Induced Ulcerative Colitis of Mice by 1H NMR Spectroscopy Rudolf Schicho,*,†,‡ Alsu Nazyrova,§ Rustem Shaykhutdinov,§ Gavin Duggan,§ Hans J. Vogel,§ and Martin Storr† Snyder Institute of Infection, Immunity and Inflammation, Department of Medicine, Division of Gastroenterology, University of Calgary, Alberta, Canada, Institute of Experimental and Clinical Pharmacology, Medical University of Graz, Austria, and Department of Biological Sciences, Metabolomics Research Centre, University of Calgary, Alberta, Canada Received June 2, 2010
Quantitative profiling of a large number of metabolic compounds is a promising method to detect biomarkers in inflammatory bowel diseases (IBD), such as ulcerative colitis (UC). We induced an experimental form of UC in mice by treatment with dextran sulfate sodium (DSS) and characterized 53 serum and 69 urine metabolites by use of 1H NMR spectroscopy and quantitative (“targeted”) analysis to distinguish between diseased and healthy animals. Hierarchical multivariate orthogonal partial leastsquares (OPLS) models were developed to detect and predict separation of control and DSS-treated mice. DSS treatment resulted in weight loss, colonic inflammation, and increase in myeloperoxidase activity. Metabolomic patterns generated from the OPLS data clearly separated DSS-treated from control mice with a slightly higher predictive power (Q2) for serum (0.73) than urine (0.71). During DSS colitis, creatine, carnitine, and methylamines increased in urine while in serum, maximal increases were observed for ketone bodies, hypoxanthine, and tryptophan. Antioxidant metabolites decreased in urine whereas in serum, glucose and Krebs cycle intermediates decreased strongly. Quantitative metabolic profiling of serum and urine thus discriminates between healthy and DSS-treated mice. Analysis of serum or urine seems to be equally powerful for detecting experimental colitis, and a combined analysis offers only a minor improvement. Keywords: inflammatory bowel disease • dextran sulfate sodium • CD1 mice • OPLS • metabolomics • chemometrics
Introduction Inflammatory bowel diseases (IBDs), which manifest themselves in two clinical forms, that is, Crohn’s disease (CD) and ulcerative colitis (UC), are a major burden for patients and society. Prevalence rates for UC (per 100 000 persons) amount to 238 individuals in the US to as low as 63.6 persons in Japan.1,2 The pathophysiology of IBD is still unclear but it has been widely accepted that multiple factors, including genetic, environmental and microbiological components, contribute to the occurrence and perpetuation of the disease.3,4 At present, the therapeutic options consist of nonsteroidal and steroidal anti-inflammatory drugs as well as immunosuppressants, immunomodulators and so-called novel biologicals. However, the currently used treatments are frequently ineffective or may cause serious side effects. Appropriate therapeutic decisionmaking, which crucially depends on accurate diagnosis and regular surveillance, would be a prerequisite to minimizing side effects in IBD therapy. Currently used methods for diagnosis * To whom correspondence should be addressed. Telephone: +43 316380 4319. Fax: +43 316380 9645. E-mail:
[email protected]. † Division of Gastroenterology, University of Calgary. ‡ Medical University of Graz. § Metabolomics Research Centre, University of Calgary. 10.1021/pr100547y
2010 American Chemical Society
include endoscopic techniques, diagnostic imaging and laboratory tests which can be time-consuming and costly. Endoscopy is a technique that is not without risks,5 although complications associated with colonoscopy are reportedly rare.6 Less invasive methods for diagnosis, such as determination of biomarkers from urine, serum or feces, however, would be of significant advantage and could be extremely useful for primary diagnosis, surveillance, early detection of relapses, and for directing therapeutic decisions. Several studies have started to address this issue by performing nontargeted metabolomic profiling, for example, in urine of IL-10 deficient mice and in serum of DSS-treated mice using gas or liquid chromatography-mass spectrometry.7-9 Nontargeted metabolomic profiling by 1H NMR spectroscopy has been applied in an experimental model of colitis,10 as well as in IBD patients to characterize metabolites in urine,11 fecal extracts,12 and biopsy samples.13 A recent study employing ion cyclotron resonance-Fourier transform mass spectrometry to discriminate between a thousand metabolites revealed differences in fecal samples collected from identical twin pairs, including healthy individuals and CD patients.14 Thus, metabolomic profiling is rapidly emerging as a powerful method for characterizing IBD in experimental animal models and humans. Journal of Proteome Research 2010, 9, 6265–6273 6265 Published on Web 10/04/2010
research articles Quantitative metabolomic profiling may significantly improve the chances for the discovery of disease-related biomarkers. Such an approach involves the analysis of a large group of compounds whose characteristics (e.g., NMR spectra) are known and stored in a database library. From the complex mixture of individual metabolite spectra in the biofluid, the spectra in the database can be used to identify and quantify the targeted metabolites. This type of approach allows quantification of a large amount of metabolites of a single specimen in a high-throughput manner and is highly suitable for many metabolites.15 The method has been successfully used to detect metabolomic changes in urine samples of IL-10 deficient mice, another experimental mouse model of IBD in which mice develop colitis spontaneously.16 In our search for new metabolites that could potentially be used as biomarkers, we have used 1H NMR spectroscopy to perform quantitative metabolomic analysis of metabolites in urine and blood serum of mice in which experimental ulcerative colitis has been induced with dextran sulfate sodium (DSS). As “targeted” metabolomic profiling has not been tested in DSS colitis before, we selected this experimentally inducible animal model to investigate the correlation of colon inflammation with the occurrence or absence of specific metabolites in serum and urine. DSS-induced colitis is one of the most reproducible and widely used animals models for UC.17 Its colitis-inducing mechanism has been extensively investigated in colon mucosa and the intestinal immune system.18,19 We screened for 69 metabolites in urine and 53 metabolites in serum that were identified and quantified by Chenomx NMR Suite 5.1 software and verified by 2D NMR methods. We finally compared urinary metabolomic profiles with those from serum, which is less prone to environmental influences,20 and examined which biofluid would be more reliable for biomarker detection.
Materials and Methods Experimental procedures were approved by the University of Calgary Animal Care Committee (approval number M07102). Experiments were performed in accordance with the guidelines established by the Canadian Council on Animal Care in agreement with international guidelines. CD1 mice (males, 3-5 weeks old, 20-25 g) were purchased from Charles River (SaintConstant, Quebec, Canada) and housed in plastic sawdust floor cages at constant temperature (22 °C) and a 12:12-h light-dark cycle with free access to standard laboratory chow and tap water. In vivo Experiments. (a) Induction of Colitis and Sample Collection. Colitis was induced in CD1 mice by adding 4% (wt/ vol) DSS (reagent grade; 36-50 000 Da; MP Biomedicals, Solon, OH) to the drinking water (tap water) for 7 days while control animals received tap water only. A dose of 4% produced the highest reproducibility of colonic inflammation and the lowest mortality rate in these mice in preliminary experiments. Body weights were measured daily. Fresh-void urine was collected in sterile microfuge tubes over 24 h on day 1, 3, 5, and 8 according to the method described by Cohen et al.21 During sampling, urine was kept on ice and was stored after dilution with sterile saline at -80 °C until processing. On day 8, around 0.5 mL of blood was drawn by intracardiac puncture from mice under deep isoflurane anesthesia immediately before euthanization (cervical dislocation). (b) Colitis Scoring. After euthanization, the colon was removed, rinsed gently with saline solution, opened longitu6266
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Schicho et al. dinally along the mesenteric border and examined. Colonic damage was assessed according to a scoring system by Kimball et al.22 adapted for the present study. The amount of ulcers present were scored from 0 () normal mucosa) to 4 (more than 5 ulcers). Loss of colon weight, shortening of colon length, stool consistency and presence of fecal blood was scored from 0 to 4 with 0 depicting the normal and 4 the maximally affected state. The score index represents the sum of all subscores and had a maximum of 16. (c) Determination of Tissue Myeloperoxidase (MPO) Activity. Colon samples were weighed, immediately frozen on dry ice, and stored at -80 °C prior to further processing and determination of MPO activity. Tissue was placed in 0.5% HTAB buffer (50 mg of tissue/mL; pH 6.0) and disrupted with a Polytron homogenizer (Brinkman Instruments, Mississauga, Ontario, Canada). HTAB (hexadecyl-trimethyl-ammoniumbromide; Sigma-Aldrich, Oakville, Ontario, Canada) is a detergent that releases MPO from the primary granules of neutrophils and enhances enzyme activity through the presence of bromide.23 Afterward, the homogenate was centrifuged for 15 min at maximum speed and 4 °C. Before measuring MPO activity, 7 µL of supernatant was added to 200 µL of 50 mM potassium phosphate buffer (pH 6.0) containing 0.167 mg/mL of o-dianisidine hydrochloride and 0.5 µL of 1% H2O2/mL. The kinetics of MPO activity was measured at 460 nm (Thermo Fischer Labsystems Multiskan, Thermo Scientific, Ottawa, Ontario, Canada). A mean was calculated for the control group and set at 100%. Values of the DSS-treated group are expressed as a % of the control group. 1 H NMR Spectroscopy. (a) Metabolite Sample Preparation. Serum and urine samples were thawed and filtered twice using 3-kDa Nanosep microcentrifuge filters to remove proteins. The final volume of filtrate ranged from 100 to 400 µL. Samples were brought to 650 µL by addition of D20, 130 µL of phosphate buffer containing dimethyl-silapentane-sulfonate (final concentration 0.5 mM) and 10 µL of 1 M sodium azide. The final sample pH was adjusted to 7 ( 0.01. (b) Spectrum Acquisition. NMR spectra were acquired using an automated NMR Case sample changer on a 600 MHz Bruker Ultrashield Plus NMR spectrometer. Regular one-dimensional proton NMR spectra were obtained using a standard pulse sequence (Bruker pulse program prnoesy1d) that has good water suppression characteristics and is commonly used for metabolite profiling analysis of serum or plasma samples.15,24 It utilizes the following pulse sequence: RD-90°-t1-90°-tm-90°acquire FID; where RD is a relaxation delay of 1 s, during which the water resonance is selectively irradiated; t1 is set to 4 µs and tm has a value of 100 ms, during which the water resonance was again selectively irradiated. Initial samples for each batch were shimmed to ensure half-height line width of ∼1.1 Hz for the dimethyl-silapentane-sulfonate peak, calibrated to 0.0 ppm. Spectra were acquired with 1024 scans, then zero filled and Fourier transformed to 128k data points. For proper quantitative fitting of the NMR spectra it is important that the spectra are collected under the same conditions as the metabolite standard spectra in the Chenomx database. (c) Sample Fitting. Processed spectra were imported into Chenomx NMR Suite 5.1 software (Chenomx Inc., Edmonton, Canada) for quantification using the “targeted profiling” approach.15 Baseline correction was performed manually using a spline function, followed by deletion of the water region. Overall 69 compounds in urine and 53 compounds in serum spectra were detectable with sufficient signal-to-noise. Spectra
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Serum and Urine in DSS-Induced Ulcerative Colitis were randomly ordered for profiling. Compounds were profiled in order of decreasing typical concentration. Each compound concentration was then normalized using Probabilistic Quotient Normalization, dividing the measured concentrations by the median ratio of that sample’s measurements to the average sample.25 Statistical Analysis. (a) Model Generation. To reveal patterns in metabolite concentration shifts, hierarchical multivariate analysis was conducted using SIMCA-P+ 12.0 software (Umetrics, Sweden). Factors correlated with the effects of DSS treatment were identified using orthogonal partial least-squares (OPLS), with the use of a base model to eliminate structured artifactual variation. OPLS is based on projection to latent structures (PLS), a pattern recognition method which reduces numerous collinear variables (in our case metabolites) to a few subsets to demonstrate their interdependence. It therefore maximizes the independence of the DSS treatment factors from others present, resulting in better separation. OPLS yields a loading value for each metabolite (X-variable) and a score value for each sample. The former represent a pattern of change correlated to a supervisory Y-variable, while the latter indicate the degree to which the pattern is seen in each sample. For each OPLS model, 7-fold cross validation (CV) was used to validate the statistical significance of each model dimension and to calculate bootstrap p-values for the statistical significance of the change in each metabolite. Component significance was estimated using the Prediction Error Sum of Squares (0.05 threshold in Q2Y). Metabolite significance was estimated using a t-test, with six degrees of freedom, for a nonzero loading value based on the CV standard errors (cvSE, CINT method).26 (b) Urine OPLS. Urine metabolite concentrations were determined for each animal on day 1, 3, 5, and 8. First, a simple preliminary PCA model was performed on all urine samples, with four significant components as determined with a Q2Y threshold of 0.05 (Supporting Information, Supplementary Figure 1). Based on the structure of the PCA model, a hierarchical OPLS-DA was used to isolate treatment-related variation. Because of environmental and intrinsic influences on metabolite profiles, such as animal handling and aging, we created a two-tier model with the change in control samples over time as a baseline. The day of urine sample acquisition was the Y variable for the base model, with normalized urine metabolite concentrations from control animals serving as X variables. A similar model was then created for the samples from DSS-treated animals, and the resulting model structures were compared to validate the baseline. First component residuals from the DSS-treated animal model were then used to construct two hierarchical OPLS models of colitis-induced effects. The residuals, representing variation in the samples of DSS-treated animals unrelated to artifactual sources, constituted the X-variables in both cases. The first, a discriminant model, was optimized to create a model which could differentiate animals with colitis regardless of the degree of affectedness. The supervisory Y-variable was zero (0) for control and one (1) for DSS-treated animals. OPLS scores, indicating the model’s assessment of treatment character in each sample, were regressed against body mass change to show the discriminatory ability of the model; body mass was the only indicator variable measured daily throughout the course of the experiment. A second, explanatory model was structured to identify progressive changes correlated with the onset of colitis. The percentage change in body mass, relative to day zero, was used as an aggregate indicator of colitis
Figure 1. (Day 8) The increases in the macroscopic score index (damage score; * p < 0.001) and MPO activity (scatter plot; p < 0.01) are shown. Body weight decreased significantly after treatment with 4% DSS in CD1 mice as compared to control animals which received tap water only (p < 0.05); n ) 5-11; Student’s t test.
severity, and the supervisory Y-variable. OPLS scores were regressed to the day of sample acquisition to show the model’s ability to capture the onset trend. (c) Serum OPLS. The single serum sample acquired from each animal was profiled to give blood metabolite levels after 8 days of DSS treatment vs controls. A preliminary PCA model (Supporting Information, Supplementary Figure 2) and an OPLS model were constructed of the difference between samples from control and DSS-treated mice. The day 8 percentage change in body mass (relative to day zero) was again used as the supervisory variable. A predicted body mass for each animal was calculated based on those cross validation models, which did not include those samples in its construction. This CVpredicted body mass is compared to the actual body mass of the animals as additional validation.
Results Animal Experiments and Evaluation of Colitis. Mice with access to 4% DSS in their drinking water developed severe colitis as indicated by an increase in the macroscopic damage score index and the myeloperoxidase (MPO) activity (Figure 1, day 8). MPO activity represents an index of neutrophil infiltration and inflammation in the colon.23 Animals from the control group (with access to tap water only) gained more than 20% of body mass in the course of the experiment, whereas DSSJournal of Proteome Research • Vol. 9, No. 12, 2010 6267
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Figure 2. Discriminant-OPLS score plot of urine samples taken on days 1, 3, 5, and 8 from DSS-treated and control mice. The changes in body mass (x axis) are compared to the OPLS-DA score of the metabolomic profiles of urine samples per time point of collection (y axis). All metabolomic profiles of urine samples from DSS-treated animals (except for one) gather in the upper half (positive values) of the plot, while profiles from control mice are in the lower half (negative values) demonstrating reliable separation between the treatment groups regardless of change in body mass. Values on x and y are relative.
treated mice started to lose weight on day 4-5 of the experiment and had on average 16% lower body mass on day 8 (control group 29.3 ( 0.9 g vs DSS-treated group 24.5 ( 1 g; means ( sem; n ) 5-11; Student’s t test). Urine Models. Parameters to determine the presence of colitis in DSS-treated mice consisted of elevated damage scores and increased MPO activities. Since these parameters were associated with weight loss in the diseased animals (the correlation coefficient between weight loss and damage score was R2 ) 0.48), the change in body mass was used as an indicator of colitis in several models. The preliminary PCA model (Supporting Information, Supplementary Figure 1) indicated a prevailing pattern of variation associated with date. Variation further separated DSS-treated animals into two distinct groups, corresponding to animals with strong or weak responses to DSS treatment. The remaining extensive uncaptured variation led us to believe that significant sources of unrelated metabolic noise might be present, such as differences in food intake or gut bacteria composition; hierarchical OPLS models were used to isolate these various sources of noise. After comparison to the changes in control animals, first component OPLS-residuals were used to create both OPLS-DA and OPLS models of treated animals. In the discriminant model (Figure 2) the change in body mass of DSS-treated and control animals over time (x axis) was compared to changes in concentration of urine metabolites per sample (y axis). The urine samples of the control group (black triangles in Figure 2) can be clearly separated from the DSS group (red triangles in Figure 2). While the metabolomic profiles of the control animals are concentrated in one group, metabolomic profiles of the DSS-treated animals still separated into several groups, indicating that the animals responded with either large (negative values on x axis) or small (positive values on x axis) weight loss relative to controls. The model is nevertheless able to recognize all samples of DSS-treated animals, except for one, as belonging to the colitis group (positive values on y axis), independent of 6268
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Figure 3. Plot of 2nd tier OPLS scores from the explanatory model, representing disease-onset correlated changes in concentration. Plot shows the changes in urine metabolite profiles (y axis) over the time course of the DSS colitis experiment (x axis). Negative y-values correlate with lower body masses. Profiles of control samples from day 5 all overlap, appearing as one data point (black square).
how strong the decrease in body weight was. Our urine model can therefore distinguish between diseased and healthy animals by the pattern of changes in metabolite concentration and composition. We also wanted to investigate whether and how urine metabolomic profiles would change over time during the development of the colitis. Thus, a second, explanatory OPLS model was generated that would identify patterns in urine metabolites correlated with changes in body mass over time (Figure 3, days 1, 3, 5, 8). From the scores plot in Figure 3 it is obvious that changes in metabolite concentrations are not apparent until around day 5 (when the DSS-treated mice start to lose weight; see Figure 1), but are evident on day 8 when colitis has fully developed and the drop in weight is maximal. The profiles of the control samples from day 5 in Figure 3 all overlap and therefore appear as one data point (black square). Urine Metabolites. Quantitative (“targeted”) analysis using 1 H NMR spectroscopy and Chenomx NMR Suite 5.1 software allowed us to evaluate 69 known metabolites in urine samples. Figure 4 (days 1, 3, 5, 8) shows the changes in urine metabolite concentration in DSS-treated mice based on their body mass as an indicator of colitis. Increases in concentrations were observed for the amino acids creatine, carnitine, glycine and phenylalanine, for allantoin and for methylamines such as di-, trimethylamine and trimethylamine N-oxide while urea (a terminal metabolite of catabolic processes), methionine, nicotinamide and ascorbate were decreased. Serum Model. Serum was obtained from mice on day 8 of the DSS experiment and metabolomic profiling was employed the same way as for urine samples. A preliminary PCA model is shown in Supplementary Figure 2 (Supporting Information). To overcome the variation from the PCA model, we created an OPLS model that was based on the animals’ weight loss as a parameter for colitis and that would predict the change in body mass, and hence the development of colitis, from changes in metabolite concentration and composition. The OPLS plot in Figure 5 (day 8) shows that the predicted body mass, based on the pattern of the serum metabolites of DSS-treated mice, correlates well with the actual body mass of the DSS-treated mice and indicates that profiling of serum metabolites may detect the presence of colitis.
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Serum and Urine in DSS-Induced Ulcerative Colitis
Figure 4. OPLS coefficient plot displays the metabolites of urine samples from DSS-treated mice taken at four time points (days 1, 3, 5, 8). The metabolite loadings are correlated to changes in body mass. Negative (relative) values indicate metabolites that significantly decreased, while positive (relative) values indicate metabolites that significantly increased over time. Only metabolites with a significant change in concentration are shown (p < 0.05).
Figure 5. (Day 8) OPLS score plot of metabolomic profiles in serum samples taken on day 8 from DSS-treated mice. The predicted day-8 change which is based on the metabolite pattern of the serum samples correlates with the actual change in body mass and therefore with the appearance of colitis. x and y values are relative.
Serum Metabolites. For the serum samples, 53 metabolites were quantified. The OPLS coefficient plot in Figure 6 is similar to the one in Figure 4 in which the metabolite loadings were correlated with the changes in body mass. Large increases in concentration are shown for ketone bodies (acetoacetate, acetone and 3-hydroxybutyrate), hypoxanthine, inosine and tryptophan, while glucose, Krebs cycle intermediates (fumarate, 2-oxoglutarate, citrate), and several amino acids are decreased. Comparison of the Urine and the Serum Models. It would be of considerable clinical interest to know which of the body fluids, either serum or urine, are more reliable in discriminating
Figure 6. (Day 8) This OPLS coefficient plot shows the changes in serum metabolite concentrations in DSS- and control animals on day 8. The metabolite loadings are correlated to changes in body mass as an indicator of DSS colitis. In mice with decreasing body mass (and hence with colitis), positive values in the upper part of the diagram indicate increased metabolite concentrations while negative values in the lower part denote a decrease in metabolite concentrations, as compared to controls. Only the metabolites with significant changes in concentration are shown (p < 0.05).
DSS-colitis from controls. We therefore created an additional day-8 urine OPLS model similar to the serum model in Figure 5. We plotted, in 3D, the actual changes in bodymass against the changes predicted by day-8 serum and urine models (Figure 7). Both metabolomic profiles of serum and urine have a tendency to group and show equally good separation between control and DSS-treated mice. This signifies that the serum and urine models are relatively comparable in predictive strength. Another way to describe the reliability of our models to predict separation of treatment groups is by comparing the Q2 values. The Q2 value was slightly, but no significantly, higher for the serum (Q2 ) 0.73) than for the urine model (Q2 ) 0.71). Combined evaluation of metabolomic data from urine and serum samples (Q2 ) 0.76) is only little more predictive than that of serum or urine samples alone.
Discussion Quantitative metabolomic profiling represents a new approach for the determination of biomarkers in IBD and has the potential to support or even replace invasive methods of diagnosis. In this study, we were able to show that metabolomic profiles of urine and serum determined by 1H NMR spectroscopy clearly distinguished between healthy mice and mice with Journal of Proteome Research • Vol. 9, No. 12, 2010 6269
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Schicho et al. oxidative stress-related molecules like ascorbate and nicotinamide while in serum, we saw a strong presence of ketone bodies and decreased levels of tricarboxylic acid cycle (TCA) intermediates. Changes in metabolite concentrations in urine did not become apparent before day 5 when the body weight started to drop. Urine showed high levels of host-derived metabolites like creatine and carnitine, amino acids involved in energy supply of mammalian cells which indicates the need of ATP and fatty acids as energy supply during the disease. High levels of creatinine (degradation product of creatine) were also seen in another study of colitis which used IL-10 knockout mice.16
Figure 7. 3D plot shows predicted vs actual changes in body mass in serum and urine samples. In both serum and urine metabolites, the model separates healthy (controls: black triangles) from diseased animals (DSS-treated: red triangles) equally well.
Figure 8. Representative 1H NMR spectra (0.7- 6 ppm area) of serum samples from CD1 mice from day 8: (A) sample from a control (tap water-treated) mouse; (B) sample from a DSS-treated mouse. NMR peaks arising from glucose, 3-hydroxybutyrate and acetoacetate are labeled to show the concentration difference in samples from control and DSS-treated mice.
an experimental form of IBD (Figure 8). The diseased mice were identified by evaluation of macroscopic damage of the colon, increased MPO levels and reduced body weight (Figure 1). The loss in body weight during DSS colitis goes in hand with reduced food intake.27,28 Several OPLS models were employed using the change in body weight, which was measured every day, as a supervisory variable to delineate the pattern of changes in metabolite concentrations in serum and urine during DSS colitis and as to whether these changes would be significant enough to separate the treatment groups. Concerning the composition of metabolites, serum and urine responded differently to DSS colitis. Profiles and concentrations of measured metabolites in both biofluids were significantly separated between diseased and nondiseased. According to their Q2 values, serum and urine are equally reliable and predictive in determining DSS colitis. Metabolomic profiles of the two biofluids were quite distinct in DSS colitis. In urine, we saw changes in the concentration of bacterial metabolites, such as the methylamines,29 and of 6270
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Large amounts of methylamines (dimethylamine [DMA], trimethylamine [TMA] and its oxidation product trimethylamine N-oxide [TMAO]) that are produced in gut bacteria,29 were found elevated in urine of DSS-treated mice. Gut microbiota are known to play an important role in the development of IBD.30 Like in our DSS colitis model, high TMA and TMAO levels were also detected in urine of IL-10 knockout mice.16 Methylamines could therefore represent crucial metabolites in characterizing colitis. What is also important is the fact that DMA, TMA and TMAO are substrates for in vivo nitrosylation to form dimethylnitrosamines known as potent carcinogens.31 Nitrite is a potential donor for nitrosylation and has been demonstrated to be increased in experimental colitis.32 Nitrosylated TMA and DMA could thus play a role in the still unknown pathogenesis of colitis-induced cancer. Carnitine and creatine are known to be increased by oxidative stress33 and heavy weight loss,34 which is a typical sign in models of DSS colitis.17 Both metabolites were elevated in urine during the development of the disease and might reflect an overall stress condition of the animal. Elevated creatine levels could arise from an increased glomerular filtration rate that developed secondary to the DSS colitis. It should be also considered that stress-related metabolites may not be totally specific to the DSS colitis but are influenced by experimentation and environment. Two other metabolites that were strongly increased in DSS colitis are allantoin and phenylalanine. Allantoin is generated from uric acid by oxidation and is a regular component of purine catabolism in lower mammals but is also metabolized in gut bacteria.35 The daily output of allantoin correlates with the uptake of dietary purines.36 Its increase may signify disturbances in the purine metabolism of the host but could also point to changes in the bacterial population since DSS colitis in mice has been shown to increase the presence of proinflammatory enterobacteriaceae, such as Escherichia coli, in the colon.37 DSS-treated mice had elevated levels of phenylalanine and 3-methyl-2-hydroxybutyrate which are normally observed in phenylketonuria. This genetic disorder is characterized by a low activity of phenylalanine-4-hydroxylase, a hepatic enzyme that converts phenylalanine into tyrosine. In accordance, tyrosine levels were lowered in the serum of the DSS-treated mice. The occurrence of phenylalanine and 3-methyl-2-hydroxybutyrate could indicate a potential hepatotoxic side effect associated with the colitis.38 Nevertheless, although DSS also spreads to the liver,39 no pathohistological alterations were seen in liver tissue after acute DSS treatment.40 The rise of 3-indoxyl sulfate in DSS colitis is of interest because this metabolite is also increased in urine of IL-10 knockouts16 and is able to enhance cellular oxidative stress by decreasing the antioxidant glutathione.41 It also has been implicated in glomerular necrosis.42 Hepatobiliary and kidney manifestations are not
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Serum and Urine in DSS-Induced Ulcerative Colitis 43,44
uncommon observations in IBD, suggesting that the DSS treatment could have effected kidney functions. In similar studies, DSS was found to accumulate in epithelial cells of the proximal renal tubules and to produce hemorrhages in the renal cortex.39,45 These observations and the fact that rodents display lowered water intake and urine output in DSS colitis27,28 are in accordance with the low urine output seen in IBD patients46,47 and the occurrence of tubular injury in the acute state of IBD.48 We also found increased levels of fucose and lactate (Llactate) in urine of DSS-treated mice. This is in line with the observation that lactate is secreted from inflamed colonic mucosa and increases in feces of active, but not quiescent UC patient.49 High lactate thus can be a sign of severe colitis.50 High fucose levels were also observed in the IL-10 knockout colitis model.16 Several metabolites showed decreased concentrations in urine of DSS-treated mice. Among them, urea reveals the strongest decrease. It is formed in the liver and represents the final metabolite of the amino acid metabolism. It is not quite clear why urea is decreased in our colitis model because urea synthesis is reportedly increased in IBD patients.51 It could be a consequence of the low amino acid levels that we observed in the serum. Nicotinamide (vitamin B3), its metabolite 3-methylnicotinamide and ascorbate (vitamin C) were also decreased in DSS colitis which could indicate intestinal malabsorption, a prominent feature of IBD.52 The ascorbate content was shown to be decreased by 73% in patients with UC.53 The low levels of ascorbate and nicotinamide in serum of DSStreated mice could impair antioxidant defense in the inflamed tissue. Interestingly, a vitamin B3 deficiency syndrome (pellagra), has been reported as a complicating factor of IBD.54 The serum metabolome responded with a different pattern to DSS exposure than the urine metabolome. Metabolic products of bacteria (DMA, TMA and TMAO) or oxidative stress-related (3-indoxylsulfate, nicotinamide and ascorbate) and phenylketonuria metabolites are missing. However, as in urine, we saw changes in creatine and carnitine concentrations and other amino acids, such as glycine. While creatine increased during the disease, carnitine and glycine, unlike in urine, decreased in the serum. The metabolomic profile of the serum showed strong increases in ketone bodies, such as acetoacetate, acetone and 3-hydroxybutyrate and a drop in glucose concentrations which could mirror the high demand of the body for energy. In addition, the increase in the serum metabolites hypoxanthine and inosine (and allantoin in urine) may underline the deteriorating state of the energy level in the diseased mice. Increases in hypoxanthine have been associated with ATP depletion,55 and high levels of uric acid, hypoxanthine and inosine were found in plasma of critically ill patients in intensive care.56 However, one study, which included only a small number of UC patients, did not reveal significant increases in inosine levels in these patients.57 In this context it is interesting to note that exogenous inosine has been shown to exert protective effects in DSS colitis.58 Quite a few amino acids (proline, tyrosine, glutamine, glycine, methionine, histidine, alanine) were decreased in serum which may reflect malnutrition of the sick animals. Inflammatory conditions are known to stimulate general protein catabolism and a release of amino acids from muscle to provide substrate for proteins of the immune system.59 The demand for these amino acids may result in a general decrease of amino acid levels in the blood. Another group of serum metabolites that was decreased
in DSS colitis is associated with the TCA cycle and energy metabolism (acetate, fumarate, 2-oxoglutarate, citrate and glucose) which indicates the high demand and rapid utilization of metabolites that feed energy producing pathways. The decrease in TCA cycle products and amino acids together with the increase of ketone bodies could also mirror the fact that animals lower their food intake during DSS colitis.27 Caloric restriction in mice has been shown to lower glucose levels and to increase ketone bodies.60 The increase in serum tryptophan is of major interest as tryptophan is the precursor of serotonin, a widely expressed metabolite and transmitter throughout the gut. Tryptophan hydroxylase is decreased in rectal biopsies from patients with UC,61 highlighting the possibility that tryptophan levels have gone up because of the lack of its metabolizing enzyme. Another study also implicates that serotonin is involved in the pathogenesis of experimental colitis.62 High levels of tryptophan thus could contribute to the role of serotonin as a pathogenic mediator in colitis. In conclusion, our results have shown that quantitative metabolic profiling effectively discriminates between DSS colitis and healthy mice in both urine and serum samples. The metabolomic profiles in the two biofluids differ in their composition. Urine samples revealed mostly changes in bacterial metabolites and metabolites associated with oxidative stress, while serum samples showed changes in metabolites associated with the regulation of the organism’s energy level. Both, serum and urine are equally reliable and predictive in determining the diseased state while combined analysis is of little advantage over single analysis. The metabolites described in this study may act as a guide for the determination of metabolites in other colitis models and human conditions of IBD.
Acknowledgment. This work was supported by Genome Alberta and the Alberta Department of Advanced Education and Technology. H. J. Vogel is a scientist of the Alberta Heritage Foundation for Medical Research. Supporting Information Available: Supplementary Figure 1 shows a simple preliminary PCA model performed on all urine samples, with four significant components as determined with a Q2Y threshold of 0.05. Supplementary Figure 2 shows a preliminary PCA model generated from serum samples that were obtained from mice on day 8 of the DSS experiment. Metabolomic profiling was employed the same way as for urine samples. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Kappelman, M. D.; Rifas-Shiman, S. L.; Kleinman, K.; Ollendorf, D.; Bousvaros, A.; Grand, R. J.; Finkelstein, J. A. The prevalence and geographic distribution of Crohn’s disease and ulcerative colitis in the United States. Clin. Gastroenterol. Hepatol. 2007, 5 (12), 1424–1429. (2) Asakura, K.; Nishiwaki, Y.; Inoue, N.; Hibi, T.; Watanabe, M.; Takebayashi, T. Prevalence of ulcerative colitis and Crohn’s disease in Japan. J. Gastroenterol. 2009, 44 (7), 659–665. (3) Hanauer, S. B. Inflammatory bowel disease: epidemiology, pathogenesis, and therapeutic opportunities. Inflamm. Bowel Dis. 2006, 12 (Suppl 1), S3–S9. (4) Strober, W.; Fuss, I.; Mannon, P. The fundamental basis of inflammatory bowel disease. J. Clin. Invest. 2007, 117, 514–521. (5) Panteris, V.; Haringsma, J.; Kuipers, E. J. Colonoscopy perforation rate, mechanisms and outcome: from diagnostic to therapeutic colonoscopy. Endoscopy 2009, 41 (11), 941–951. (6) Crispin, A.; Birkner, B.; Munte, A.; Nusko, G.; Mansmann, U. Process quality and incidence of acute complications in a series
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