Nontargeted Urinary Metabolite Profiling of a Mouse Model of Crohn’s Disease Hui-Ming Lin,†,‡,§ Shelley J. Edmunds,‡,§,| Nuala A. Helsby,† Lynnette R. Ferguson,†,§ and Daryl D. Rowan*,‡,§ School of Medical Sciences, University of Auckland, Auckland, New Zealand, The New Zealand Institute for Plant and Food Research Limited, New Zealand, School of Biological Sciences, University of Auckland, Auckland, New Zealand, and Nutrigenomics New Zealand Received November 18, 2008
Crohn’s disease is an inflammatory disorder of the bowel, believed to arise from the dysregulation of intestinal mucosal immunity. The interleukin-10-deficient (IL10-/-) mouse, which develops intestinal inflammation in the presence of gut microflora, serves as a mouse model of Crohn’s disease. Nontargeted urinary metabolite profiling was carried out to identify systemic metabolic changes associated with the development of intestinal inflammation caused by IL10-deficiency. Spot urine samples, collected from IL10-/- and wildtype mice at ages 5.5, 7, 8.5, and 10.5 weeks old were analyzed by gas chromatography-mass spectrometry (GCMS). The data were analyzed using XCMS software, multiple t tests, and ANOVA. Among the key metabolic differences detected were elevated urinary levels of xanthurenic acid and fucose in IL10-/- mice relative to wildtype, indicating upregulation of tryptophan catabolism and perturbed fucosylation in IL10-/- mice. Three short-chain dicarboxylic acid metabolites were decreased in urine of IL10-/- mice relative to wildtype, suggesting the downregulation of fatty acid oxidation in IL10-/- mice. These metabolic differences were reproducible in an independent set of mice. This study demonstrates that nontargeted GCMS metabolite profiling of IL10-/- mice can provide insights into the metabolic effects of IL10-deficiency and identify potential markers of intestinal inflammation. Keywords: metabolite profiling • metabolomic • metabonomic • gas chromatography-mass spectrometry • urine • Crohn’s disease • interleukin-10-deficient mouse
Introduction Crohn’s disease is a chronic debilitating inflammatory disorder of the bowel.1,2 The disease is characterized by recurring inflammation in the gastrointestinal tract especially in the small and large intestine, resulting in abdominal pain and persistent diarrhea. There is no cure; thus, therapy is aimed at inducing and sustaining remission from intestinal inflammation using anti-inflammatory medication.2 The exact etiology of Crohn’s disease is unknown, but the disease is attributed to the dysregulation of the intestinal mucosal immune response toward gut microflora.3 The discovery of various disease susceptibility genes4 and a higher incidence of Crohn’s disease in developing countries5 indicate a complex interplay of genetic and environmental factors in disease etiology. Interleukin-10 (IL10) is an immunosuppressive cytokine that regulates cell-mediated immune responses.6 IL10 has an important role in intestinal mucosal immunity toward gut * To whom correspondence should be addressed. Mailing address: Plant and Food Research Palmerston North, Private Bag 11600, Palmerston North 4442, New Zealand. Fax: +64-9537701. E-mail:
[email protected]. † School of Medical Sciences, University of Auckland. ‡ The New Zealand Institute for Plant and Food Research Limited. § Nutrigenomics. | School of Biological Sciences, University of Auckland. 10.1021/pr800999t CCC: $40.75
2009 American Chemical Society
microflora, as IL10-deficient (IL10-/-) mice spontaneously develop chronic enterocolitis.7 Inflammation occurs predominantly in the colon8 and requires the presence of gut microflora as IL10-/- mice raised in germ-free conditions do not develop enterocolitis.9 While an exact animal model of Crohn’s disease does not exist, intestinal inflammation in the IL10-/- mouse has physiological7 and biochemical8 similarities to Crohn’s disease, thus the IL10-/- mouse has been widely used for studying Crohn’s disease pathogenesis and developing potential therapies.10-12 Nontargeted metabolite profiling, also known as metabolomic or metabonomic analysis, refers to the comprehensive study of the many small molecule metabolites present in biological samples.13,14 Changes in metabolite levels indicate changes in biochemical pathways, thus metabolite profiling of pathological conditions has the potential to provide insights into disease mechanisms and identify diagnostic biomarkers or drug targets. Several reviews15-19 have discussed the use of metabolite profiling approaches to study the effects of genetic manipulation, pathological conditions, physiological changes, diet and drugs. Analytical techniques typically used are nuclear magnetic resonance (NMR)20 and mass spectrometry (MS),21 where the latter can be coupled to chromatographic separation techniques such as gas chromatography or liquid chromatogJournal of Proteome Research 2009, 8, 2045–2057 2045 Published on Web 01/29/2009
research articles raphy. However, no single analytical technique can measure all the metabolites that are present in a sample due to the diverse chemical properties of metabolites and the limitations of each analytical technique. A number of metabolite profiling approaches have been used to study Crohn’s disease pathogenesis.22-25 Chen et al. (2008) performed LC-MS metabolite profiling of serum from mice with dextran sulfate sodium (DSS)-induced colitis and discovered that intestinal inflammation was exacerbated by DSSinhibition of stearoyl-coA desaturase 1 (SCD1)-mediated oleic acid biogenesis.22 Marchesi et al. (2007) showed that NMR metabolite profiling of faecal samples can detect differences between Crohn’s disease patients and healthy controls, and that these differences arose from changes in the gut microbial community.23 Murdoch et al. (2008) performed NMR metabolite profiling of 24-h pooled urine from IL10-/- and wildtype mice of 129/SvEv background, and discovered urinary metabolite changes that were concomitant with the timeline of intestinal inflammation.24 The use of nontargeted gas chromatography-mass spectrometry (GCMS) metabolite profiling to find urinary metabolite biomarkers of diseases has been reported.26-28 Urinary metabolites are ideal biomarkers, as the collection of urine samples is noninvasive and multiple samples can be obtained to monitor disease progression. GCMS metabolite profiling of the IL10-/- mouse or an animal model of Crohn’s disease has not been reported. The identification of urinary metabolite markers of inflammation in the IL10-/- mouse would be useful in monitoring response toward experimental therapy, as disease activity in the same animal could be monitored noninvasively. Current methods for assessing efficacy of experimental therapy require terminal sampling of intestinal tissue for intestinal histopathology or blood for measuring plasma biomarkers of inflammation. In this study, we describe the use of nontargeted urinary metabolite profiling by GCMS to identify metabolite differences between IL10-/- and wildtype mice.
Methods Mice. Experimental procedures were reviewed and approved by the Crown Research Institute Animal Ethics Committee in Hamilton, New Zealand, according to the New Zealand Animal Welfare Act (1999). Urine samples were collected as part of a larger experiment examining the effects of diet on inflammation in IL10-/- mice. Results of diet effects will be reported elsewhere. The urine samples used in this study were collected from fifteen IL10-/mice of C57BL/6 background strain and eight wildtype C57BL/6 mice. All mice were male, weaned and purchased from The Jackson Laboratory (Bar Harbor, ME). The average age upon arrival at the experimental facility was 4 weeks old ((3 days). Mice were individually housed in a shoebox-style cage under conventional conditions in a room with 14 h light-10 h dark cycle and temperature of 22 °C. Mice were fed AIN76A powdered diet prepared in-house. Food intake was adjusted to equal the mean amount of food consumed by IL10-/- mice the previous week. Access to drinking water was ad libitum. Four days after arrival into the facility, all mice were orally dosed with a mixture of Enterococcus faecalis, E. faecium and complex intestinal flora to ensure that they all received the same microbial exposure and that all IL10-/- mice developed consistent intestinal inflammation as described by Roy et al. (2007).29 2046
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Lin et al. Spot urine samples were collected from mice when the average age of the mice was 5.5, 7, 8.5. and 10.5 weeks old, between 9 a.m. and 4 p.m. Urine sampling was conducted by briefly restraining each mouse on a glass Petri dish, resulting in spontaneous urination onto the dish. A mechanical pipet was used to transfer the urine from the dish into a 1.5 mL microfuge tube which was immediately placed on ice. After all mice were sampled, the urine samples were transferred to -80 °C for long-term storage. The final urine collection at 10.5 weeks of age was performed immediately before mice were euthanized, marking the end of the experiment. Prior to euthanasia, mice were not fed for 14 h, then allowed to feed for 2 h and finally fasted again for 2 h to ensure the timing of final food intake was standardized for intestinal sampling for gene expression analysis (will be reported elsewhere). This fasting regime was adapted from Paisley et al. (1996), who showed that gene expression can be influenced by the time of eating.30 Mice were euthanized by carbon dioxide asphyxiation followed by cervical dislocation. Intestinal tissue was immediately removed, rinsed with saline solution and fixed in phosphate-buffered formalin for histopathology analysis to confirm the presence of intestinal inflammation in IL10-/- mice. GCMS Analysis. Since urine samples were obtained as part of a larger experiment, all urine samples were analyzed together in 14 analytical batches with triplicates of a quality control urine sample per batch. Altogether, 255 urine samples were analyzed, of which 76 samples were from the mice used in this study and 42 samples were replicates of the quality control urine sample. Urine samples collected from the same urine sampling time point were analyzed in the same analytical batch or in consecutive batches. The quality control urine sample was created by pooling 43 urine samples from 17 wildtype C57BL/6 mice and a urine sample from one IL10-/- mouse, of which 6 of the wildtype mice and the IL10-/- mouse were from this experiment. The use of pooled samples as quality controls was proposed by Sangster et al. (2006), since appropriate analytical standards cannot be decided when the metabolites of interest are unknown.31 Sample preparation for GCMS analysis was based on methods developed for targeted GCMS analysis of human urinary metabolites.32,33 Ten to 20 µL of urine was incubated with 40 µL of 1200 units/mL urease (Type C-3, Sigma) at 37 °C for 15 min. Then 350 µL of ice-cold ethanol was added and the mixture placed on ice for 20 min, followed by centrifugation to precipitate protein. The supernatant was lyophilized, reconstituted with 40 µL dry pyridine, then trimethylsilyl-derivatized with 80 µL MSTFA (Sigma) at 65 °C for 2 h. Derivatized samples were analyzed by splitless injection of 1 µL volume, using a HP-5 column (30 m length, 0.32 mm i.d, 0.25 µm film thickness; Agilent Technologies) on a Shimadzu QP5050A quadrupole GCMS operating under GCMS Solution software package version 2.40. Injection temperature was 250 °C, split sampling time was 0.5 min, helium carrier gas flow was 1.4 mL/min and column inlet pressure was 12 kPa. The temperature gradient for the gas chromatograph separation started with 60 °C for 4 min, increased from 60 to 120 °C at 10 °C/min, 120 to 200 °C at 2.5 °C/min, 120 to 300 °C at 10 °C/min, and held at 300 °C for 8 min. The m/z scan range was 50 to 550 m/z, with scan speed of 1000 and scan interval of 0.5 s. Linear retention indices of metabolites were measured using C10-C32 alkanes. Data Preprocessing. XCMS software34 (version 1.12.0) was used to deconvolute and align mass ions from the datafiles of the 255 samples into a single data set. The input files were in
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Metabolite Profiling of a Mouse Model of Crohn’s Disease Table 1. Number of Urine Samples Collected at Each Sampling Timepoint for Each Experiment
number of urine samples
First experiment Validation experiment
IL10-/wildtype IL10-/wildtype
number of mice
1st time point
2nd time point
3rd time point
4th time point
15 10 10 6
12 8 9 5
13 7 8 5
13 6 4 4
11 6 4 3
NetCDF format, created by format conversion of the raw GCMS datafiles using the Shimadzu GCMS Postrun Analysis software. XCMS parameters were default settings except for the following: full-width half-maximum of 8, maximum groups in single m/z slice of 200, minfrac of 0.15, bandwidth of 8 for first grouping command and 4 for second grouping command. The data set of aligned mass ions was exported from XCMS as “tsv” format, which could be viewed using Microsoft Excel. Artifacts arising from the MSTFA derivatizing reagent and from contamination of urine by the powdered diet were identified from the GCMS analysis of a water extract of the AIN76A diet (approximately 1 mg), which was chemically analyzed in the same manner as urine samples. GCMS ions present in this sample were removed from the data set prior to data normalization. Data normalization was performed using R statistical software35 (version 2.5.1). The data set was first normalized for differences between the analytical batches of samples by using the average of the triplicate quality controls to standardize the intensities of mass ions within each analytical batch, resulting in intensities that were relative to the quality control. Next, the data set was normalized for differences in the dilution of urine samples using the principles of the Probabilistic Quotient Normalization method.36 The average of all quality controls for all analytical batches was used as the estimate for the dilution factors of urine samples. Finally, intensities of the mass ions were transformed to log2 scale. Statistical Analyses. The quality of the XCMS data set and trends in metabolite profiles were examined by Principal Components Analysis (PCA), performed using the ‘prcomp’ package in the R statistical software. The metabolite profiles of IL10-/- and wildtype mice were compared by performing multiple t tests on the peak areas of XCMS aligned mass ions using the ‘multtest’ package in R. Comparisons of the mass ions between IL10-/- and wildtype mice were made at each independent urine sampling time point. P-value adjustment was not used as it proved too stringent. P-value adjustment is based on the number of t test comparisons, which equals the number of mass ions in the data set, but the number of mass ions is not equivalent to the number of metabolites as some mass ions represent the same metabolite. Thus, P-value adjustment will inflate the P-values, resulting in decreased statistical significance. Therefore, we adopted other approaches to ensure confidence in the statistical significance of the differences. We looked at the frequency of occurrence of significantly different mass ions at each of the urine sampling time pointssif a metabolite is really different between the two mouse genotypes, the mass ions representing that metabolite should appear as significantly different at all or most of the sampling time points. Also, mass ions of similar retention times should correlate highly with each other (R2 g 0.8) if they represent the same metabolite. The corresponding mass ions for replicate quality control samples should also show satisfactory precision, indicating that the metabolite is a stable trimethylsilyl-derivative. Further inspection using the original peak areas from raw GCMS datafiles for representative mass ions should also show
statistically significant differences. The peak areas from the raw GCMS datafiles were quantitated for selected mass ions using the Shimadzu GCMS Postrun Analysis. The quantitated peak areas were exported as text files and collated into a single data set using an in-house Windows application written in C# (a programming language developed by Microsoft as part of the NET Framework). The data set was normalized for differences between analytical batches and differences in urine dilution in the same manner as for the XCMS data set. Differences in dilution of urine samples were normalized with the same dilution factors used in normalization of the XCMS data set. The normalized data set was tested for differences between IL10-/- and wildtype mice using multiple t test (“multtest”, R) and Analysis of Variance (ANOVA) using the General Linear Model in Minitab version 15.1.0.0. Metabolite Identification. Metabolites were identified by searching for mass spectral matches in the following GCMS mass spectral library databases: NIST02, Wiley 7 and Golm Metabolome Database.37 Matches were confirmed by GCMS analysis of chemical standards purchased from Sigma-Aldrich, except for 2-hydroxyadipic acid and hexanoylglycine, which were both purchased from Lumila Research Group, Universidad Autonoma de Madrid, Spain. Validation Experiment. Metabolite differences identified between IL10-/- and wildtype mice were validated using urine samples from ten IL10-/- mice and six wildtype mice in a second experiment conducted eight months later. Mice were also of C57BL/6 strain, all male, purchased from The Jackson Laboratory (Maine) and kept under the same conditions as the first experiment. However, the average age upon arrival to the facility was 5.3 weeks old ((3 days). Oral dosing of all mice with the bacteria mixture was performed four days after arrival. Urine samples were obtained when the average age of mice was 7, 9, 11.5 and 12 weeks old. The final urine collection at 12 weeks of age was performed immediately before the mice were euthanized and after the mice were subjected to the same fasting regime as in the first experiment. GCMS analysis was performed as for the first experiment, except the GC column was ZB-5MS (0.25 mm i.d, 30 m length, 0.25 µL film; Phenomenex) and a slightly different temperature program was used.
Results Urine Samples. The number of urine samples obtained by spot collection are displayed in Table 1. Typical sample volumes ranged from 5 to 100 µL, with a few up to 300 µL. The success rate of urine sampling for the first experiment was 90% of the mice at the first time point, but decreased to 74% at the last time point. The second experiment also displayed a similar trend, with the sampling success rate declining from 88% at the first time point to 44% at the last time point. The declining sampling rate may be a result of the increasing familiarity of the mice to handling. Alternatively, the low sampling rate for the last time point may be related to the decreased food intake of mice during the fasting preparation prior to tissue sampling. Urine samples obtained at the last time point were pale in color Journal of Proteome Research • Vol. 8, No. 4, 2009 2047
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Figure 1. Representative GCMS chromatograms (total ion) of urine samples from IL10-/- and wildtype mice, quality control urine sample and an aqueous extract of diet fed to mice in the first experiment.
and of lower volume compared to the other time points, suggesting that the concentration of urinary metabolites were reduced, which was also indicated by GCMS analysis as chromatograms of these samples contained peaks of lower intensity. Representative GCMS chromatograms of urine samples from the first experiment are displayed in Figure 1. Also displayed is the GCMS chromatogram of water extract prepared from the AIN76A powdered diet, which was used to identify and remove 2048
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artifacts arising from the derivatization process and from contamination of urine by diet during sampling. Metabolite Profiles. XCMS peak deconvolution and alignment of the GCMS datafiles of urine samples from the first experiment produced a single data set containing peak area intensities for 2990 aligned mass ions. Removal of artifacts reduced the number of aligned mass ions to 2444. These aligned mass ions represent the metabolite profile of the mice and could be divided into 592 groups of ions with the same
Metabolite Profiling of a Mouse Model of Crohn’s Disease retention time (rounded to the nearest 0.1 min), giving a rough estimate of the number of different metabolites detected. An exact estimate of the number of metabolites cannot be determined, as multiple trimethylsilyl derivatives of the same metabolite will produce multiple peaks with different retention times in the chromatogram and coeluting metabolites will produce mass ions with the same retention time. Principal Components Analysis (PCA) of samples was carried out to assess the variation among the samples, which can provide information on data quality and trends in metabolite profile. PCA of samples within the same analytical batch prior to data normalization (data not shown) showed that triplicate quality control samples generally clustered more closely together than other urine samples on the first and second principal components (PC1 and PC2). As the quality control samples were replicate preparations of the same urine sample, their close clustering indicated that the variation introduced by sample preparation and GCMS analysis was much less than the biological variation between samples. PCA of the normalized complete data set showed no clear grouping of samples according to the analytical batches on PC1 and PC2 (Figure 2A), indicating that normalization had corrected for batch differences. Separation between urine samples from IL10-/- and wildtype mice was not present on PC1 and PC2 (Figure 2B). Instead, the strongest variation among the samples appeared to be caused either by the two different tuning files used by the mass spectrometry (Figure 2C) or by the different urine sampling time points (Figure 2D). Quality control samples did not separate according to the two different tuning files (Figure 2B), which indicated that the normalization procedure had corrected for differences caused by the use of different mass spectrometry tuning files and it is likely that the major variation in the urine samples were caused by the different sampling time points. Samples also did not group according to the time of day when the samples were collected, ruling out any major effects from diurnal variation (Figure 2E). The clustering of samples according to sampling time points (Figure 2D) indicates that the urinary metabolite profiles of mice at the second sampling time point (7 weeks old) were more similar to metabolite profiles at the third sampling time point (8.5 weeks old) compared to other time points (Figure 2D). The separation of metabolite profiles at the first time point (5.5 weeks old) may be caused by differences in physiological development, as the typical age for sexual maturity in mice is around 6 to 7 weeks old.38 The separation of metabolite profiles at the last time point may be caused by a decreased production of metabolites due to reduced food intake during the fasting treatment prior to sampling. The variation among the metabolite profiles for the last time point was also higher, as indicated by the wide spread of datapoints (Figure 2D), presumably caused by the lower levels of metabolites measured in these urine samples as measurement precision is reduced for lower intensity peaks. Differential Metabolites at All Time Points. Metabolites that were different between IL10-/- and wildtype mice were determined by using multiple t tests to compare the intensity of mass ions between IL10-/- and wildtype mice at the same urine sampling time points. The number of significantly different mass ions (P < 0.05) produced from each time point varied, but generally increased with the age of the mice (Table 2). Groups of mass ions with the same retention time and high correlation (g0.8) among the mass ions are likely to represent the same metabolite. The repetitive occurrence of such groups
research articles of mass ions as significantly different between the mice at all or most time points are likely to be real biological differences. Only eight groups of mass ions with the same retention times and high correlation were significantly different (P < 0.05) between IL10-/- and wildtype mice at all four sampling time points in the first experiment. These differences were confirmed using peak area intensities quantitated directly from GCMS datafiles (rows 1-8 of Table 3, P-values in Supporting Information). Mass spectral matching to library databases followed by comparison with authentic chemical standards revealed that these eight groups of mass ions represented seven metabolites with two groups of mass ions being different trimethylsilyl derivatives of fucose. The other identified metabolites were glutaric acid, 2-hydroxyglutaric acid, hexanoylglycine, 2-hydroxyadipic acid and xanthurenic acid. The identity of the seventh metabolite remains unknown. (See Supporting Information for chromatograms and mass spectra of these eight groups of mass ions.) 2-Hydroxyglutaric acid, 2-hydroxyadipic acid and xanthurenic acid were confirmed to be significantly different (P < 0.05) between IL10-/- and wildtype mice at all four sampling time points of the validation experiment (P-values in Supporting Information). Glutaric acid and fucose were only significantly different between IL10-/- and wildtype mice at the first three sampling time points of the validation experiment (P < 0.05). The lack of statistical significance at the last time point may be due to the low sample number of three for wildtype mice at that time point and the high variation of the levels of glutaric acid and fucose in these three samples (individual value plots in Figure 3 and 4). However, glutaric acid and fucose were overall significantly different between IL10-/- and wildtype mice when all sampling time points were analyzed together by ANOVA (P < 0.05, Supporting Information). Similarly, the unknown metabolite was significantly different between the two mice types when all sampling time points were analyzed by ANOVA even though levels were not significantly different at the first sampling time point according to t test. Hexanoylglycine was not detected in the urine samples of the validation experiment and was possibly hidden under a large coeluting peak, due to a change in elution profile caused by the use of a different GC column. Fold differences and individual value plots of the levels of the metabolites in IL10-/- and wildtype mice are displayed in Table 3, Figure 3 and Figure 4. The levels of glutaric acid, 2-hydroxyglutaric acid and 2-hydroxyadipic acid were lower in urine of IL10-/- mice compared to wildtype. Fold differences for glutaric acid and 2-hydroxyglutaric acid were large because these metabolites were hardly detectable in the urine samples of IL10-/- mice. The levels of fucose and xanthurenic acid were higher in urine of IL10-/- mice compared to wildtype. Differential Metabolites at Last Three Time Points. Fifteen other groups of mass ions with the same retention times and high correlation showed significant differences (P < 0.05) between IL10-/- and wildtype mice for each of the last three sampling time points of the first experiment (rows 9-23 of Table 3, t test P-values in Supporting Information). GCMS library matching with confirmation using chemical standards revealed that these 15 groups of mass ions represent 14 metabolites. Eight of these groups of mass ions were identified as seven metabolitesscytosine, 5-aminovaleric acid, cis-aconitic acid, hippuric acid, isocitric acid, pantothenic acid and glucose (two trimethylsilyl derivatives). Journal of Proteome Research • Vol. 8, No. 4, 2009 2049
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Figure 2. Principal Components Analysis (PCA) biplots of 2444 mass ions measured by GCMS for 49 urine samples from IL10-/- mice, 27 urine samples from wildtype mice and 42 replicate quality controls in the first experiment. Samples are labeled (A) according to GCMS analytical batches, with “A” to “N” sequentially representing 14 batches; (B) according to sample type, with “WT” ) wildtype mouse, “IL10” ) IL10-/- mouse, “QC” ) quality control; (C) according to mass spectrometry tuning files, with “A” and “B” representing two different tuning files; (D) according to urine sampling time point and (E) according to the time of day when samples were obtained.
Multiple t tests of peak area intensities from the GCMS datafiles for these 14 metabolites revealed that only nine metabolites were significantly different at the last three sampling time points (fold differences in Table 3, P-values in Supporting Information)scytosine, 5-aminovaleric acid, glucose and six unknowns. However, ANOVA using all time points 2050
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showed that all 14 metabolites were overall significantly different between the two mice type (ANOVA, Supporting Information). Validation of these 14 metabolites with the second experiment revealed that only nine metabolites could be detected in the urine samplesscytosine, 5-aminovaleric acid, cis-aconitic acid, hippuric acid, isocitric acid, glucose and
Metabolite Profiling of a Mouse Model of Crohn’s Disease Table 2. Number of Mass Ions and Groups of Mass Ions with Significantly Different Peak Areas (P < 0.05, T-tests) between IL10-/- and Wildtype Mice of the First Experimenta
Age of mice (weeks) Number of mass ions Number of groups with unique retention times (rounded to 0.1 min) a
1st time point
2nd time point
3rd time point
4th time point
5.5 234 72
7.0 496 131
8.5 484 127
10.5 593 156
Total number of mass ions that were tested was 2444.
three unknowns. None of these nine metabolites were significantly different between the mice at the last time point, and only some were significantly different between the mice at both the second and third time point according to t test (fold differences in Table 3, P-values in Supporting Information). However, ANOVA of all time points showed that these metabolites, except cytosine, were significantly different between IL10-/- and wildtype mice (P < 0.05, Supporting Information). These metabolites may still provide information on metabolic pathways that differ in IL10-/- mice, by representing metabolic responses to unknown external factors that changed during the course of the experiment or differed between the two experiments. Metabolites that were not detected in the validation experiment may be really absent or were not detected because of coelution with interfering peaks due to the different elution profile from the use of a different GC column. Intestinal Inflammation. Histopathology analysis of intestinal tissues confirmed the presence of intestinal inflammation in all IL10-/- mice, with inflammation predominantly in the colon (data not shown).
Discussion Metabolite differences between IL10-/- and wildtype mice were identified by comparing the metabolite profiles of the mice at multiple sampling time points. The detection of the same differential metabolites at all the time points indicates that these metabolites are likely to reflect real biological differences between IL10-/- and wildtype mice. Validation with a second experiment enhanced the credibility of the observed differences. Urine samples of the last sampling time point were obtained after the mice were subjected to a fasting regime, which may have changed the metabolite profile of the mice. Despite any change in nutritional status at the last sampling time point, the metabolites that differed between IL10-/- and wildtype mice at the earlier time points were also different at the last time point, suggesting that these metabolites are robust markers of differences between IL10-/- and wildtype mice. Intestinal inflammation is the main phenotypic difference between IL10-/- and wildtype mice; thus, metabolic differences between these mice are likely to be associated with intestinal inflammation. Two possible types of metabolic differences may be present. The first are metabolic changes arising from biochemical processes leading to intestinal inflammation, and the second are changes arising from the presence of established intestinal inflammation. Metabolites indicative of the first type of metabolic change may be the five urinary metabolites that were consistently different between IL10-/- and wildtype mice at all individual sampling time points of both experiments (excluding the last time point of the second experiment due to low sample number). These five metabolites were glutaric acid, 2-hydroxyglutaric acid, 2-hydroxyadipic acid, fucose and
research articles xanthurenic acid. These metabolites are likely to be early indicators of pathological changes in IL10-/- mice as their differences in urinary levels of IL10-/- mice relative to wildtype were detected at 5.5 weeks of age, whereas mild intestinal inflammation was detected at 7 weeks of age (earliest sampling age) for IL10-/- mice under the same experimental conditions (AgResearch, New Zealand, unpublished work). The increase in urinary levels of fucose and xanthurenic acid was observed from 5.5 weeks of age to 7 weeks of age (Figure 4), indicating that the levels of these metabolites increased with the development of inflammation. NMR urinary metabolite profiling of IL10-/- mice by Murdoch et al. (2008)24 found that urinary levels of fucose were dramatically elevated in IL10-/- mice compared to wildtype, as found in this study. Fucose is a common sugar component in the carbohydrate chains of glycoproteins with cell recognition and cell signaling functions.39 Examples of fucosylated glycoproteins include ABO blood group antigens and ligands for the selectin family of cell adhesion receptors.39 Fucosylation of ligands for selectins are required for recruitment of neutrophils and lymphocytes to inflammation sites.40 Changes in fucosylation content of plasma proteins and elevated urinary levels of fucose have been reported for various inflammation and pathological conditions. Examples include increased defucosylation of plasma proteins during the acute phase response in rats,41 increased urinary fucose levels in liver diseases42,43 and increased levels of fucosylated plasma proteins in oral carcinoma.44 Increased urinary levels of fucose could be caused by the increased activity of enzymes that remove fucose from glycoconjugates, as was observed for serum R-Lfucosidase in patients with hepatocellular carcinoma.45 Aberrant fucosylation has also been observed in gastrointestinal diseases, with decreased fucosylation observed in the colonic mucin from patients with colon cancer,46 Crohn’s disease47 or ulcerative colitis.47 Therefore, the high levels of fucose observed in IL10-/- mice may be related to inflammation-induced glycosylation changes in plasma proteins or intestinal mucosal tissue as part of the immune response process. Xanthurenic acid is a side product of the tryptophan catabolism pathway48 (Figure 5), and thus increased levels of xanthurenic acid in IL10-/- mice indicate upregulation of tryptophan catabolism. Gene expression analysis of intestinal tissue from IL10-/- mice showed that the expression of the enzyme that catabolizes tryptophan, indoleamine-2,3-dioxygenase (IDO), was upregulated in IL10-/- mice.49 The upregulation of tryptophan catabolism in IL10-/- mice may be a response to intestinal inflammation toward gut microflora, as IDO is activated by pro-inflammatory stimuli such as bacterial lipopolysaccharide and interferon-γ cytokine.50 Tryptophan depletion by IDO during inflammation serves as a defense mechanism toward invading pathogens by suppressing their proliferation, as tryptophan is an essential amino acid.51 High excretion of urinary xanthurenic acid during inflammation has been observed in children with infections.52 Therefore, the constant elevated urinary levels of xanthurenic acid for IL10-/mice may be a result of increased tryptophan catabolism caused by chronic inflammation. Alternatively, xanthurenic acid may be involved in the induction of immune tolerance toward intestinal microflora, as IDO activation is associated with the induction of immune tolerance in T-cell responses, presumably through the action of tryptophan catabolites.53,54 A recent study showed that xanthurenic acid contributed to the induction of immune Journal of Proteome Research • Vol. 8, No. 4, 2009 2051
2052
Journal of Proteome Research • Vol. 8, No. 4, 2009 2022 2410
79-83-4 -
unknown
1885 1934 2037
pantothenic acid
unknown glucose glucose
495-69-2 320-77-4 (trisodium salt) 50-99-7 50-99-7
1842 1864
1801
1240 1396 1535 1640 1660 1730 1777
71-30-7 660-88-8 585-84-2 -
1912 2285
1705 1742
1699
59-00-7
2438-80-4 2438-80-4
18294-85-4
1647
1415 1600
375
291
292 191 191
105 273
260
128 230 240 174 231 188 229
98 408
191 191
171
172
158 247
mass ion quantitated in retention raw datafiles index (m/z)
hippuric acid isocitric acid
Unknown
unknown unknown cytosine 5-aminovaleric acid unknown unknown cis-aconitic acid
unknown xanthurenic acid
fucose fucose
2-hydroxyadipic acid
glutaric acid 110-94-1 2-hydroxyglutaric acid 103404-90-6 (disodium salt) hexanoylglycine 24003-67-6
CAS registry number of chemical standard
1/1.4
1.1(NS)
1/1.2
2.3 1/1.5 1/1.4
1.3(NS) 1.0(NS) 1.0(NS)
1.5(NS)
1.9 1/1.5
5.5
6.0 2.4 4.0 3.2 1.5 6.1 1/2.0
1/6.3d 3.1
2.4 2.2
1/5.5
1/2.1
1/1.2(NS)
2.2 1/1.4 1/1.3
3.4 1/1.4(NS)
18d
6.8 2.8 5.3 4.0 1.8 16d 1/1.8
1/6.2d 3.0
2.7 2.7
1/5.8
1/1.9
1/11d 1/18d
1/14d 1/24d 1/1.5
8.5 weeks old
7.0 weeks old
1.1(NS) 1/1.2(NS)
1/1.6(NS)
1/1.3(NS) 1.2(NS) 2.5(NS) 1.2(NS) 1/1.1(NS) 1.3(NS) 1/1.7(NS)
1/7.8d 3.6
1.4 1.3
1/5.1
1/1.4
1/5.2 1/43
5.5 weeks old
1/3.2(NS)
1/2.5
3.2 1/1.4 1/1.3
1/1.2(NS) 1/1.3
33d
4.0 3.9 8.2 11 1.8 42d 1/1.3(NS)
1/7.4d 3.4
3.0 3.3
1/5.1
1/1.6
1/6.3 1/26d
10.5 weeks old
2.0 1.0(NS) 1.0(NS)
1.3(NS) 1/1.6(NS)
1.7(NS)
1/1.2(NS)
2.2(NS) 1/1.7e 2.6(NS)
1/2.5(NS) 2.6
1.7 1.8
1/2.7
1/3.4 1/4.0
7 weeks old
1/4.1
1/5.1d 2.1
2.3 2.3
1/3.7
2.3 1.3 1.3
2.5 1/1.1 (NS) 1/1.2(NS)
1.7 1/1.3(NS)
6.1
Not detected
Not detected
1.8 1/1.8
5.7
Not detected 2.4 1.6 4.6 Not detected Not detected 1/1.3(NS) 1/1.5(NS) 4.4 1.6 4.2
1/7.2d 2.1
1.9 1.9
1/3.3 1/6.8
11.5 weeks old
Not detected
1/10 1/16
9 weeks old
validation set of mice
2.5(NS) 1/1.6(NS) 1/1.6(NS)
1.3(NS) 1/1.5(NS)
2.8(NS)
1/1.3(NS)
1.6(NS) 6.1(NS) 2.9(NS)
1/4.3 5.3
1.8(NS) 2.1(NS)
1/3.6
1/2.4(NS) 1/15
12 weeks old
a Data from first set of mice. RT: retention time in seconds. b Mass ions listed according to t-test significance (P < 0.05)sbold, significantly different between mice at all time points; not bold, significantly different between mice at last three time points; round brackets, significantly different between mice at the last two time points; square brackets, significantly different between mice at the 2nd and 3rd time points; italics, significantly different between mice at 2nd and 4th time points. c Intensities were normalized but not log transformed. See Table 1 for number of samples. “NS”: no significant difference according to t-test (P > 0.05). d Large fold difference as metabolite was barely detected in either IL10-/- or wildtype mice. e Skewed by outlier.
1874 117, 292, 333 1992 - 1993 191, 192, 193, [148] 2221 129, 147, 169, 189, 191, 192, 193, 205, 206, 207, 231, 317, [345] 2182 422, 55, 98, 103, 104, 144, 145, 157, 158, 159, 160, 201, 247, 248, 249, 261, 291, 420, 421 2800 375, [151, 166, 257, 376, 377]
1780 - 1782 1828 - 1829
1683 - 1685
657 861 1111 1327 - 1328 1369 - 1370 1524 1631 - 1632
1933 - 1934 2677, 2678
1468 1551
1454, 1455
71, 99, 130, 132, 145, 158, 159, 172, 189, 230, [160] 55, 83, 99, 101, 109, 129,131, 142, 171, 172, 203, 261, 262, 263, 363, 364, 365, 71 191, 192, 204, 205, 206, 143, 193, 217 189, 191, 192, 193, 204, 205, 206, 217, 57, 83, 85, 134, 143, 147, 148, 149, 155, 207, 218, 229, 245, 291, 303, 305, 393 98, 140, 170, 200, 230, 56, [111, 112] 200, 216, 229, 230, 232, 272, 274, 275, 276, 288, 289, 290, 302, 303, 304, 318, 348, 362, 378, 390, 391, 392, 404, 406, 407, 410, 421, 422 172, (128) 156, 230, 245 98, 238, 240, 255, [241, 254] 82, 154, 174, 175, 176, 228, 318 231, 232, [69] 82, 98, 156, 159, 188 67, 211, 212, 215, 230, 375, [229, 147, 149, 285, 286, 376] 55, 82, 83, 173, 174, 175, 190, 219, 232, 233, 260, 261, 262, 362, 377, [86, 157, 202, 203, 218, 246, 247] 106, [51, 105, 206, 207, 208] 83, 348, [364]
1340
XCMS mass ions
147, 158, 186, 261, 262, 893 129, 247
a ,b
893 1240
XCMS RT (sec)a
metabolite (measured as trimethylsilyl derivatives)
1st set of mice
fold difference calculated from raw datafiles(average of IL10-/- mice/average of wildtype mice)c
Table 3. Groups of Mass Ions Corresponding to Metabolites with Significantly Different Urinary Levels (P < 0.05, ANOVA) between IL10-/- and Wildtype Mice
research articles Lin et al.
Metabolite Profiling of a Mouse Model of Crohn’s Disease
research articles
Figure 3. Peak areas of selected mass ions of trimethylsilyl derivatives (TMS) of glutaric acid, 2-hydroxyglutaric acid and 2-hydroxyadipic acid quantitated from GCMS datafiles of urine samples of IL10-/- and wildtype mice, and quality control urine samples (QC) of the same analytical batches. Each data point represents a urine sample. Peak areas are relative to the average of triplicate QCs within the same analytical batch, normalized for dilution differences but not log transformed.
tolerance during allergen immunotherapy in a mouse model of allergic asthma.55 Another study showed that the precursor of xanthurenic acid, 3-hydroxykynurenine, suppressed antigenspecific proliferation of T-cells from a mouse model of autoimmune neuroinflammation, and that suppression of T-cell proliferation was accompanied by IL10 production.56 IL10 acts on dendritic cells to induce immune tolerance of T-cells toward antigens.57 Therefore, xanthurenic acid or precursors may be the link between IDO activation and induction of immune tolerance. Perhaps IDO activation by antigens from gut microflora initiates tryptophan catabolism to produce xanthurenic acid which stimulates the production of IL10, leading to the induction of immune tolerance toward intestinal microflora.
The lack of IL10 as the negative feedback control may result in the continuous production of xanthurenic acid in IL10-/- mice. Alternatively, precursors of xanthurenic acid could be the active metabolites that stimulate IL10 production and the accumulation of these metabolites from continuous tryptophan catabolism without negative feedback control from IL0 production leads to the formation of xanthurenic acid as a product for excretion through urine. Upregulation of tryptophan catabolism has been reported in Crohn’s disease, with increased gene expression of IDO in colonic biopsies,58 and higher plasma levels of kynurenine, an early precursor of xanthurenic acid, in Crohn’s patients.59 Therefore, the upregulation of tryptophan catabolism in IL10-/Journal of Proteome Research • Vol. 8, No. 4, 2009 2053
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Lin et al.
Figure 4. Peak areas of selected mass ions of trimethylsilyl derivatives (TMS) of fucose and xanthurenic acid quantitated from GCMS datafiles of urine samples of IL10-/- and wildtype mice, and quality control urine samples (QC) of the same analytical batches. Each data point represents a urine sample. Peak areas are relative to the average of triplicate QCs within the same analytical batch, normalized for dilution differences but not log transformed.
mice supports the relevance of the IL10-/- mouse as a model of Crohn’s disease. However, no significant difference was reported in plasma levels of xanthurenic acid between Crohn’s patients and healthy controls,59 but urinary levels of xanthurenic acid were not measured and perhaps xanthurenic acid that was present in the plasma was rapidly excreted by the kidneys. Glutaric acid, 2-hydroxyglutaric acid and 2-hydroxyadipic acid are dicarboxylic acid metabolites which may be produced by biochemical pathways that branch off the tryptophan pathway (Figure 5). The glutarate pathway which produces glutaric acid and 2-hydroxyadipic acid is downstream from 3-hydroxykynurenine, precursor of xanthurenic acid. The pathway for production of 2-hydroxyglutaric acid is downstream from kynurenic acid which is formed from kynurenine, an early precursor of xanthurenic acid. Therefore, the decreased urinary levels of these three dicarboxylic acids in IL10-/- mice may be caused by the upregulated production of xanthurenic acid which diminishes the levels of precursors entering into the pathways that produces these dicarboxylic acids. Alternatively, the decreased urinary levels of these dicarboxylic acids may indicate downregulation of fatty acid oxidation, as fatty acid oxidation is suppressed during the process of inflammation,60 and dicarboxylic acids can be produced by ω-oxidation of fatty acids.61 The lower urinary levels of hexanoylglycine in IL10-/mice relative to wildtype in our first experiment also supports the downregulation of fatty acid oxidation as hexanoylglycine is derived from hexanoyl-CoA, an intermediate from β-oxida2054
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tion of fatty acids.48 Gene expression analysis of intestinal tissue from IL10-/- mice showed that the expression of genes associated with fatty acid oxidation was downregulated compared to those of wildtype mice.49 Metabolites indicative of the second type of metabolic change (established inflammation) may include the fourteen urinary metabolites that differed between IL10-/- and wildtype mice at later sampling time points of the first experiment. The occurrence of these metabolic differences at later sampling time points suggests that these metabolites are associated with progressing intestinal inflammation. However, these metabolic differences are not consistent at all of the last three sampling time points for both experiments, and thus may also represent metabolic responses to unknown external factors that change during or between the experiments. Murdoch et al. (2008)24 reported that urinary levels of succinic acid and uracil were higher in IL10-/- mice compared to wildtype. Targeted inspection of our GCMS data showed that while succinic acid and uracil were overall significantly higher in IL10-/- mice compared to wildtype in the first experiment, only uracil was significantly higher in IL10-/- mice in the second experiment (P < 0.05, ANOVA, Supporting Information). Murdoch et al. (2008)24 also found no significant difference in urinary levels of hippuric acid. In contrast, we found that urinary levels of hippuric acid were higher for IL10-/- mice than wildtype in both experiments. The production of hippuric acid is associated with gut microbial breakdown of plant phenolics and aromatic acids,62 thus the elevated urinary levels of
Metabolite Profiling of a Mouse Model of Crohn’s Disease
research articles
Figure 5. Metabolic pathways from tryptophan catabolism showing possible relationships between xanthurenic acid, glutaric acid, 2-hydroxyadipic acid and 2-hydroxyglutaric acid.48,64
hippuric acid for IL10-/- mice suggests differences in gut microbial populations, which has been demonstrated by Denaturing Gradient Gel Electrophoresis (DGGE) analysis of the colonic microflora of IL10-/- mice.63 The DGGE analysis also showed that the composition of the bacterial population changed with the progression of intestinal inflammation in IL10-/- mice.63 Therefore, the development of intestinal inflammation in IL10-/- mice may favor the establishment of
certain bacterial populations, resulting in differences in urinary levels of hippuric acid. The association of intestinal inflammation with changes in gut microbial populations means that metabolites arising from the change in gut microbial population can act as indirect markers of IL10 deficiency. The differences in hippuric acid levels observed in our study but not in the NMR study by Murdoch et al. (2008) suggest that differences in gut microbial population may contribute Journal of Proteome Research • Vol. 8, No. 4, 2009 2055
research articles to the discrepancies in our results, in addition to the use of the different mouse strain, 129/SvEv, by Murdoch et al. (2008). Differences in the detection capabilities of the analytical platforms may also explain why we did not find some of the differential metabolites reported by Murdoch et al. (2008) and vice versa. However, different metabolites may yet point to the involvement of the same biochemical pathways. For example, Murdoch et al. (2008) reported that urinary levels of 2-oxoglutaric acid levels were decreased for IL10-/- mice. This metabolite is a precursor of 2-hydroxyglutaric acid, which was also decreased in the urine from IL10-/- mice of our study. In conclusion, GCMS metabolite profiling identified five key metabolic differences between IL10-/- and wildtype mice which indicated that tryptophan metabolism, fucosylation and fatty acid metabolism were perturbed in IL10-/- mice. Perturbation of these pathways can be biochemically related to intestinal inflammation induced by the absence of IL10. Fucose and xanthurenic acid could be useful as markers of intestinal inflammation. Further studies are underway to confirm this.
Acknowledgment. We thank Dr. Nicole Roy, Dr. Matt Barnett and Ric Broadhurst for assistance with the mouse experiments, Dr. Shuotun Zhu for performing histopathology analysis, Martin Hunt for GCMS technical assistance and Cecilia Deng for creating the in-house software for the collation of GCMS raw intensities. Nutrigenomics New Zealand is a collaboration between AgResearch Limited, Plant & Food Research and The University of Auckland, with funding through the Foundation for Research, Science and Technology (FRST). This research was funded by FRST under contract CO6X702. Supporting Information Available: Supporting Figures and Tables. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Baumgart, D. C.; Carding, S. R. Inflammatory bowel disease: cause and immunobiology. Lancet 2007, 369 (9573), 1627–40. (2) Baumgart, D. C.; Sandborn, W. J. Inflammatory bowel disease: clinical aspects and established and evolving therapies. Lancet 2007, 369 (9573), 1641–57. (3) Strober, W.; Fuss, I.; Mannon, P. The fundamental basis of inflammatory bowel disease. J. Clin. Invest. 2007, 117 (3), 514–21. (4) Cho, J. H. The genetics and immunopathogenesis of inflammatory bowel disease. Nat. Rev. Immunol. 2008, 8 (6), 458–66. (5) Loftus, E. V. Clinical epidemiology of inflammatory bowel disease: Incidence, prevalence, and environmental influences. Gastroenterology 2004, 126 (6), 1504–17. (6) Mocellin, S.; Marincola, F.; Rossi, C. R.; Nitti, D.; Lise, M. The multifaceted relationship between IL-10 and adaptive immunity: putting together the pieces of a puzzle. Cytokine Growth Factor Rev. 2004, 15 (1), 61–76. (7) Kuhn, R.; Lohler, J.; Rennick, D.; Rajewsky, K.; Muller, W. Interleukin-10-deficient mice develop chronic enterocolitis. Cell 1993, 75 (2), 263–74. (8) Berg, D. J.; Davidson, N.; Kuhn, R.; Muller, W.; Menon, S.; Holland, G.; Thompson-Snipes, L.; Leach, M. W.; Rennick, D. Enterocolitis and colon cancer in interleukin-10-deficient mice are associated with aberrant cytokine production and CD4(+) TH1-like responses. J. Clin. Invest. 1996, 98 (4), 1010–20. (9) Sellon, R. K.; Tonkonogy, S.; Schultz, M.; Dieleman, L. A.; Grenther, W.; Balish, E.; Rennick, D. M.; Sartor, R. B. Resident enteric bacteria are necessary for development of spontaneous colitis and immune system activation in interleukin-10-deficient mice. Infect. Immun. 1998, 66 (11), 5224–31. (10) Sheil, B.; MacSharry, J.; O’Callaghan, L.; O’Riordan, A.; Waters, A.; Morgan, J.; Collins, J. K.; O’Mahony, L.; Shanahan, F. Role of interleukin (IL-10) in probiotic-mediated immune modulation: an assessment in wild-type and IL-10 knock-out mice. Clin. Exp. Immunol. 2006, 144 (2), 273–80.
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