Metabolomics of Fecal Extracts Detects Altered Metabolic Activity of

ARTICLE pubs.acs.org/jpr

Metabolomics of Fecal Extracts Detects Altered Metabolic Activity of Gut Microbiota in Ulcerative Colitis and Irritable Bowel Syndrome Gwena€elle Le Gall,*,†,|| Samah O. Noor,†,|| Karyn Ridgway,† Louise Scovell,‡ Crawford Jamieson,§ Ian T. Johnson,† Ian J. Colquhoun,† E. Kate Kemsley,† and Arjan Narbad† †

Institute of Food Research, Norwich Research Park, Norwich NR4 7UA, United Kingdom The Ipswich Hospital NHS Trust, Heath Road, Ipswich IP4 5PD, United Kingdom § Norfolk and Norwich University Hospital, Colney Lane, Norwich NR4 7UY, United Kingdom ‡

bS Supporting Information ABSTRACT: 1H NMR spectroscopy of aqueous fecal extracts has been used to investigate differences in metabolic activity of gut microbiota in patients with ulcerative colitis (UC) (n = 13), irritable bowel syndrome (IBS) (n = 10), and healthy controls (C) (n = 22). Up to four samples per individual were collected over 2 years giving a total of 124 samples. Multivariate discriminant analysis, based on NMR data from all three groups, was able to predict UC and C group membership with good sensitivity and specificity; classification of IBS samples was less successful and could not be used for diagnosis. Trends were detected toward increased taurine and cadaverine levels in UC with increased bile acid and decreased branched chain fatty acids in IBS relative to controls; changes in short chain fatty acids and amino acids were not significant. Previous PCR-denaturing gradient gel electrophoresis (PCR-DGGE) analysis of the same fecal material had shown alterations of the gut microbiota when comparing UC and IBS groups with controls. Hierarchical cluster analysis showed that DGGE profiles from the same individual were stable over time, but NMR spectra were more variable; canonical correlation analysis of NMR and DGGE data partly separated the three groups and revealed a correlation between the gut microbiota profile and metabolite composition. KEYWORDS: ulcerative colitis, irritable bowel disease, fecal extracts, metabolomics, NMR, DGGE

’ INTRODUCTION Ulcerative colitis (UC) and irritable bowel syndrome (IBS) are two very different disorders of the gastrointestinal tract that share some common symptoms such as pain and alteration of bowel habits. The causes of UC and IBS are still not clear, but it is generally accepted that both are multifactorial in origin and that host genetic factors,1,2 environmental factors,3,4 and unregulated immune responses5,6 are involved. Additionally, most studies suggest that the gut microbiota in the colon and their metabolites are important factors implicated in the pathogenesis of UC7 and IBS. There is a symbiotic relationship between microbiota and the host which offers significant benefits to both. For example, growth substrates from the diet and a stable environment for proliferation are provided by the host. In turn, the host has evolved to use bacterial fermentation products as a source of energy for the epithelial cells in the distal bowel.8 These fermentation products are known as the colonic microbiota metabolites and are produced when indigestible foods reach the colon without undergoing significant modification while passing through the small intestine. These metabolites can positively influence the biochemical and physiological processes in the colon.9 Gut diseases are often characterized by dysregulation of colonic microbiota and their metabolic activities. Microbial r 2011 American Chemical Society

metabolites may also manipulate the metabolic integrity of intestinal epithelial cells and induce immune responses in the human gut.10 The gut bacteria are able to break down indigestible food components and produce essential metabolites which cannot be produced by the host to support important functions in the body. The bacterial functions in the gut are an essential factor to consider in understanding gut conditions such as UC and IBS. The presence of some fermentation products, particularly short chain fatty acids (SCFAs), is considered a sign of bowel health, but the influence of the bacterial population composition on interindividual differences in metabolite production and colonic health is poorly understood. This study was undertaken to investigate the potential use of high resolution 1H NMR spectroscopy in identifying the complex mixture of metabolites present in fecal extracts in order to explore differences between UC, IBS, and control subjects. The major benefits of using NMR are the simplicity of sample preparation, the high throughput, and the ability to discover the chemical identities of the unknown peaks in the spectra. NMR technology is a well-developed analytical tool which can be of a great use in the field of metabolomics research.11,12 Received: April 18, 2011 Published: July 18, 2011 4208

dx.doi.org/10.1021/pr2003598 | J. Proteome Res. 2011, 10, 4208–4218

Journal of Proteome Research

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Sample Collection and Preparation

Table 1. Subject Characteristics characteristic

C

UC

IBS

number (subjects)

22

13

10

number (samples)

72

31

21

7

5

4

females

15

8

age (mean ( SD)

46.55 ( 8.8

49.0 ( 13.5

44.1 ( 7.9

age (minimum)

36

26

35

age (maximum)

60

63

57

males

disease score (median) disease score (minimum)

2 1

disease score (maximum) calprotectin

6

6 22.6 ( 3.1

45.05 ( 20.6a

29.8 ( 16.0

(ng/mL, mean ( SD) a

p < 0.01 (C vs UC, other group comparisons ns).

In combination with appropriate statistical analysis, NMR profiling of fecal water, colonic tissue, tissue extracts, and urine have all been successfully used to distinguish healthy individuals from patients with inflammatory bowel conditions such as Crohn’s disease (CD) and UC,1316 although IBS has not previously been studied in this way. LC-MS and GC-MS profiling of fecal water or urine has also been applied to profiling studies of CD17 in humans and to a mouse model of CD.18,19 The main objectives of this study were to investigate the metabolic composition of fecal water from UC and IBS patients using 1H NMR and to determine if NMR could be a useful noninvasive method for distinguishing between the two conditions. In addition, data from PCR-denaturing gradient gel electrophoresis (PCR-DGGE) obtained from the same sample set was also available, and the correlation of these results with the NMR data is explored. However, the detailed analysis of the PCR-DGGE data has been discussed elsewhere.20

’ EXPERIMENTAL DETAILS Study Design

This study was approved by the Institute of Food Research Human Research Governance Committee, East Norfolk & Waveney Research Governance Committee, and the Suffolk Research Ethics Committee for 3 years (20072009), project ref (06-Q0102-91). Patients were recruited via the gastroenterology clinics at the Norfolk and Norwich University Hospital (NNUH). Each patient had a diagnosis of IBS (ROME II criteria) or UC (Mayo Clinic criteria > 2) made by their clinician. The healthy volunteers were recruited locally as a control group (C) via the IFR Human Nutrition Unit. All volunteers in this study were recruited under restricted criteria for each group. Generally, infectious diseases and structural abnormalities of the gastrointestinal tract were excluded in all subjects. Volunteers enrolled in the study had not taken probiotics or prebiotics in any form in the previous 2 weeks and had not received any antibiotics within the 4 weeks before providing their samples. All the UC patients, except for one, were taking daily doses of an aminosalicylate drug. Table 1 provides a summary of the subject characteristics, while details of individual subjects, replicate samples, and collection times are given in Table S1 of the Supporting Information. There was no significant difference (p < 0.05) in the age distribution of the three groups.

Fecal samples were obtained from subjects at 6 month intervals at up to four different time points (Table S1, Supporting Information). Each volunteer was given a sample collection kit with instructions. The samples were produced inside sterilized plastic bags, sealed with a clip, and placed immediately in sealed insulated containers with ice. Containers were delivered to the laboratory within 2 h of sample production. Multiple aliquots of 20 mg were rapidly collected from the same area below the surface of the stool, and these subsamples were immediately frozen at 70 °C until analysis. In total, over the 2 year period, 124 unique samples were analyzed by NMR with between one and four samples available from each person. Fecal extracts were prepared for NMR analysis by mixing thoroughly 20 mg of frozen fecal material with 1 mL of saline phosphate buffer (1.9 mM Na2HPO4, 8.1 mM NaH2PO4, 150 mM NaCl, and 1 mM TSP (sodium 3-(trimethylsilyl)-propionate-d4)) in D2O (deuterium oxide), followed by centrifugation (18000g, 1 min). Supernatants were removed, filtered through 0.2 μm membrane filters, and stored at 20 °C until required. Samples were thawed, and 550 μL of each filtrate was transferred to a 5 mm o.d. NMR tube for analysis. NMR

High resolution 1H NMR spectra were recorded on a 600 MHz Bruker Avance spectrometer fitted with a 5 mm TCI cryoprobe and a 60 slot autosampler (Bruker, Rheinstetten, Germany). Sample temperature was controlled at 300 K. Each spectrum consisted of 128 scans of 32 768 complex data points with a spectral width of 14 ppm (acquisition time 1.95 s). The noesypr1d presaturation sequence was used to suppress the residual water signal with low power selective irradiation at the water frequency during the recycle delay (D1 = 2 s) and mixing time (D8 = 0.15 s). A 90° pulse length of 8.8 μs was set for all samples. Spectra were transformed with 1 Hz line broadening and zero filling, manually phased, and baseline corrected using the TOPSPIN 2.0 software. Metabolites were identified using information found in the literature21,22 or on the web (Human Metabolome Database, http://www.hmdb.ca/) and by use of the 2D-NMR methods, COSY, HSQC, and HMBC (see “2D NMR methods” in the Supporting Information) Data Analysis

Spectra were prepared for statistical analysis using the Bruker AMIX software v3.9. The “underground removal tool” of AMIX was applied to all spectra (filter width = 20 Hz) to remove the broad irregular envelope that extends from ∼0.7 to 4.5 ppm and is present to differing extents in fecal water NMR spectra. The resulting spectra were divided along the horizontal axis into variable width “buckets” (or bins) using the AMIX graphical editor to draw buckets that include, where possible, recognizably complete individual peaks or multiplets. Lists of the buckets used are provided (Tables S2 and S3, Supporting Information). The intensities within each bucket were summed and divided by the bucket width, and the bucket intensities were normalized to the same total intensity for each sample to give the final bucket table. Regions with only background noise, the water resonance, and, for all multivariate analyses involving UC patients, drug metabolite signals (see below) were not included in the buckets. Multivariate statistical analysis (PCA and PLS-DA) was carried out using the PLS Toolbox v5.5 (Eigenvector Research Inc., Wenatchee, WA) running within Matlab, v7.6 (The MathWorks 4209

dx.doi.org/10.1021/pr2003598 |J. Proteome Res. 2011, 10, 4208–4218


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Figure 1. Typical 600 MHz 1H NMR spectra of aqueous fecal extracts from (a) UC patient (inset shows aromatic region with drug and drug metabolite signals present in UC spectra); (b) UC high lactate sample; (c) IBS patient; (d) control. Labels correspond to numbering of metabolites in Table 2.

Inc., Natick, MA). For PLS-DA, Pareto scaling was applied to the columns of the bucket table and cross-validation was carried out. The performance of models with different numbers of latent vectors (LVs) was compared using the PLS Toolbox “random subsets” cross-validation (CV) method with s splits of the data and r iterations (e.g., for the full data set with 124 samples s = 10 and r = 20 giving 200 validation partitions with different allocation of samples to training and test sets). The model chosen was the one that gave the lowest mean classification error rate on the test samples excluded from the training set. Results from other cross validation schemes are compared below. These were the “leave one out” and “leave one individual out” schemes. In the latter, all of the samples acquired from an individual were left out together as a group. The selectivity ratio23 (SR), calculated in the PLS Toolbox, was used to rank the NMR buckets in order of importance to the classification models generated. Hierarchical cluster analysis (HCA) and canonical correlation analysis (CCA) were performed using Matlab in-built functions and in-house written scripts. Analysis of variance (ANOVA) was carried out for individual buckets in GraphPad Prism v5.01 (GraphPad Software, San Diego, CA) using the KruskalWallis test with Dunn’s post test. Other Measurements

Other measurements were made on subsamples of the same fecal material used in the NMR analyses. PCR-denaturing gradient gel electrophoresis was carried out as described.20 Calprotectin was measured using a commercial ELISA assay (PhiCal test, Eurospital SPA, Trieste, Italy) according to the manufacturer’s instructions (http://www.phical.com/uploads/ Instructions.pdf, accessed 15 April 2011).

’ RESULTS AND DISCUSSION Composition of Metabolite Profiles

The NMR spectra obtained were complex due to the high number of metabolite signals present in the samples. A higher feces-to-buffer ratio (1:50 w/v) was used for the extraction compared with that recommended in a recent methodological study;24 this high ratio allowed use of a single extraction and helped to reduce intersample variation in chemical shifts. Figure 1 shows an example of a spectrum from each group after the baseline flattening routine described above had been applied: two examples of spectra from UC patients are shown to illustrate the variability within this group. There are some clear systematic differences between the groups. For example, the UC group shows a series of distinct peaks in the phenolic region (inset to Figure 1a) which are absent from the IBS and control groups. These signals were identified by 2D NMR (Table 2) and from the literature13,25 as belonging to the drug 5-aminosalicylic acid (5-ASA), which was taken daily by most of the UC patients, and its metabolite N-acetyl-5-aminosalicylic acid (NAc-5-ASA). Consequently, the whole phenolic region and the methyl signal (2.16 ppm) of NAc-5-ASA were excluded from the buckets in all multivariate analyses involving UC samples. Other peaks, for instance the doublet at 1.33 ppm, which represents lactate, are exceptionally strong for a few UC individuals (Figure 1b) but absent for the majority. The 2D NMR techniques were applied to selected UC and control samples (see Figure S1 of the Supporting Information) to confirm the identity of some metabolites and to identify additional unknowns using as far as possible both 1H and 13C data. Assignment by 2D NMR yielded the identities of the metabolites which are summarized in 4210



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Table 2. 1H and 13C Chemical Shifts of Metabolites Identified by 2D NMR in Selected C, UC, and IBS Samples metabolite

δ1H (ppm)

δ13C (ppm)a

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

n-butyrate propionate valine leucine isoleucine threonine isobutyrate isovalerate n-valerate n-caproate n-heptanoate alanine lysine arginine acetate glutamate aspartate glycine 3-phenylpropionate 3-(40 -hydroxyphenyl)propionate tyrosine phenylalanine

16.10, 22.14, 42.36 12.90, 33.49 19.44, 20.68, 32.05, 63.50 23.68, 24.90, 26.86, 42.44, 56.28 13.81, 17.48, 27.49, 27.49, 38.82, 62.40 22.14, 63.34, 68.75 22.26, 40.06 24.83, 28.75, 50.20 16.0, 24.80, 30.92, 40.30 16.0, 33.90, 24.80, 28.49, 40.30 31.20 19.03, 53.62 24.38, 29.26, 32.77, 41.98, 57.37 26.86, 30.45, 43.25, 57.37 26.10 29.81, 36.37, 57.60 39.36, 39.36, 55.10 44.34 41.96, 34.84, 129.10, 131.20, 131.50 42.40, 33.95, 118.22, 132.51 38.31, 38.31, 58.95, 118.69, 133.66 39.27, 39.27, 58.95, 132.2, 130.5, 131.9

23

tryptophan

24 25 26 27 28 29 30 31 32 33 34

uridine succinate trimethylamine glycerol serine taurine cadaverine putrescine lactate ethanol R-glucose β-glucose 5-aminopentanoate methionine glutamine 2-methylbutyrate proline ethanolamine choline 5-N-acetylneuraminate 5-aminosalicylate N-acetyl-5-aminosalicylate deoxycholate phenylacetate glutarate methylsuccinate p-cresol isocaproate

0.90(t), 1.56(m), 2.16(t) 1.06(t), 2.19 (q) 0.99(d), 1.05(d), 2.29(m), 3.62(d) 0.96(d), 0.97(d), 1.70(m), 1.72(m), 3.74(m) 0.94(t), 1.02(d), 1.27(m), 1.48(m), 1.99(m), 3.68(d) 1.33(d), 3.59(d), 4.26(m) 1.07(d), 2.39(m) 0.91(d), 1.96(m), 2.06(d) 0.89(t), 1.31(m), 1.53(m), 2.18(t) 0.87(t), 1.29(m), 1.31(m), 1.55(m), 2.18(t) 1.31(m) 1.48(d), 3.78(q) 1.48(m), 1.73(m), 1.91(m), 3.03(t), 3.77(t) 1.70(m), 1.92(m), 3.26(t), 3.77(t) 1.92(s) 2.10(m), 2.36(m), 3.78(dd) 2.69(dd), 2.82(dd), 3.91(dd) 3.57(s) 2.50(t), 2.89(t), 7.27(t), 7.32(d), 7.37(t) 2.45(t), 2.82(t), 6.85(d), 7.19(d) 3.06(dd), 3.21(dd), 3.95(dd), 6.91(d), 7.20(d) 3.13(dd), 3,29(dd), 4.00(dd), 7.34(m), 7.38(m), 7.44(m) 3.31(dd), 3.50(dd), 4.07(dd), 7.21(t), 7.28(t), 7.33(s), 7.55(d), 7.74(d) 4.36(t), 5.91(d), 5.92(d), 7.88(d) 2.41(s) 2.91(s) 3.57(dd), 3.67(dd), 3.79(m) 3.85(dd), 3.96(dd), 4.00(dd) 3.27(t), 3.43(t) 1.48(m), 1.73(m), 3.02(t) 1.77(m), 3.06(m) 1.33(d), 4.11(q) 1.19(t), 3.66(q) 3.42(t), 3.53 (dd), 3.71(t), 3.73(dd), 3.83(m), 3.84(dd), 5.249(d) 3.24(dd), 3.41(t), 3.46(m), 3.49(t), 3.77(dd), 3.90(dd), 4.65(d)| 1.63(m), 1.65(m), 2.24(t), 3.02(t) 2.13(m), 2.14(s), 2.21(m), 2.65(t), 3.87(dd) 2.14(m), 2.46(m), 3.78(t) 0.86(t), 1.05(d), 1.38(m), 1.50(m), 2.21(m) 2.01(m), 2.07(m), 3.34(m), 3.42(m), 4.14(dd) 3.14(t), 3.82(t) 3.20(s), 3.53(m) 1.83(t), 2.22(dd), 4.03(m) 6.84(d), 7.01(dd), 7.32(d) 2.16(s), 6.95(d), 7.44(dd), 7.73(d) 0.72(s), 0.93(s), 0.97(s) 3.54(s), 7.31(m), 7.31(m), 7.37(m) 1.80(m), 2.19(t) 1.09(d), 2.13(dd), 2.53(dd), 2.63(m) 2.26(s), 6.83(d), 7.15(d) 0.88(d), 1.45(m), 1.49(m), 2.19 (t)

35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

29.25, 29.25, 58.02, 122.3, 125.0, 127.9, 114.8, 121.4 36.66 47.68 65.44, 65.44, 74.91 59.0, 63.13, 63.13 50.28, 38.12 25.37, 29.11, 42.0 26.77, 41.73 22.89, 71.40 19.62, 60.30 72.5, 74.2, 75.6, 63.4, 74.3, 63.4, 94.9 77.0, 72.5, 78.8, 78.8, 63.6, 63.6, 98.8 25.5, 29.2, 39.66, 42.0 32.7, 16.84, 32.7, 31.67, 56.8 29.3, 33.7, 57.1 14.46, , 30.2, 30.2, 47.6 , , 48.96, 48.96, 43.9, 60.6 56.7, 70.38 , , 119.95, 126.48, 121.22 25.51, 119.8, 132.02, 127.93 14.97, 25.25, 19.47 47.44, 129.6, 132.0, 131.6 25.96, 40.13 20.3, 44.9, 44.9, 43.1 22.4, 118.3, 133.3 24.6, 38.0, 30.1, 38.68

a 13

C shifts follow the same order as 1H shifts in the preceding column; corresponding values in the two columns are chemical shifts of 1H/13C pairs detected in the HSQC experiment. Signals marked “” in the 13C column were too weak for detection by HSQC; in that case, 1H shifts are from COSY or TOCSY.

Table 2 with the same numbering scheme as in Figure 1. Visual comparison shows that all spectra include resonances from SCFAs (butyrate, propionate, and acetate), branched chain acids,

amino acids, and carbohydrate (predominantly glucose), with phenolics, (poly)amines, bile acids, and glycerol also present in many of the spectra. 4211



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Figure 2. (a) PLS scores plot, three groups, latent vectors 1 and 3; random subsets cross validated predictions from 11 LV PS-DA model (Table 3) for (b) controls, (c) UC, and (d) IBS. In (bd), the predicted group has a target value y = 1, the other two groups have target value y = 0, and the discriminant threshold (y ∼ 0.5) is the red dashed line.

Table 3. Prediction statistics (sensitivity, specificity) for three group PLS-DA models with different cross-validation methods C cross-validation

LVsa

class. err. (CV)b

sens.

UC spec.

sens.

IBS spec.

sens.

spec.

random subsets (10/20)

11

0.22

0.87

0.77

0.80

0.89

0.54

0.82

leave one out

11

0.19

0.88

0.79

0.84

0.90

0.62

0.88

leave one individual out

11

0.31

0.79

0.71

0.61

0.85

0.43

0.77

a

Number of latent vectors in optimum model. This is the number of LVs for which class. err. (CV) is a minimum. b Classification error under crossvalidation: fraction of total number of samples that are misclassified (based on excluded samples in cross validation) for the number of LVs in previous column.

Classification and Cross-Validation Results

The relatively small number of patients per group did not permit use of an independent test set to validate the predictions of multivariate modeling. Therefore, a cross-validated PLS-DA analysis was carried out to determine if it was possible to distinguish between all three groups (C, UC, and IBS) using one model. The scores plot shows a partial separation of disease (UC and IBS) from control samples (Figure 2a). The optimum model size was determined as 11 LVs, with this being the number of dimensions that minimized the overall classification error rate with the random subsets CV method. Then the predictions were tested for each group in turn on samples excluded from the training set according to the CV scheme. Results are shown in Figure 2bd and are summarized in terms of sensitivity and specificity in Table 3. Samples with scores above the red dashed line in Figure 2bd are classified as true positives if they belong to the class indicated on the y-axis, but as false positives if they belong to either of the other classes. It is clear from both Figure 2 and Table 3 that the C and UC samples are well classified but the prediction performance is not so good for the IBS group. In fact, for the three-group model, the optimal dimensionality is a compromise, since if the predictions for each group are

considered alone, the optima are 11 LVs (C), 18 LVs (UC), and 2 LVs (IBS). The random subsets CV method treats all samples as independent. Independence is questionable in the present, case since all groups had several (up to four) replicates from each individual. Therefore, two other CV methods were also applied: these were the standard “leave one out” method and a related “leave one individual out” scheme in which all spectra originating from one individual are left out together. The prediction statistics are compared with the random subsets method in Table 3. Leave one out gives the best result but is recognized as overoptimistic and is generally reserved for very small data sets. The leave one individual out method produced somewhat lower success rates than either of the other two methods, implying that they both lead to slight overfitting. This further implies that samples from an individual cannot be treated as independent, even when the repeated measures are widely separated in time. This point is further considered below. Two-group PLS-DA models were also calculated for UC versus controls; IBS versus controls; and UC versus IBS. This is the same approach as was taken in previous NMR studies involving three sample groups (Crohn’s disease, UC, and controls) 4212



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Table 4. Prediction Statistics for Two Group PLS-DA Models with Different Cross-Validation Methods class. err. cross-validation

LVsa

(CV)b

sensitivity specificity

C versus UC (sensitivity and specificity given for UC) random subsets (10/20)

11

0.065

0.93

leave one out

12

0.04

0.97

0.94 0.96


12

0.13

0.81

0.93

random subsets (9/20) UC set 2

10

0.08

0.90

0.94

C versus IBS (sensitivity and specificity given for IBS) random subsets (9/20) leave one out

3 5

0.28 0.25

0.63 0.71

0.81 0.79


3

0.33

0.57

0.76

UC versus IBS (sensitivity and specificity given for UC) random subsets (9/20)

9

0.23

0.81

0.72

leave one out

8

0.20

0.84

0.76


9

0.29

0.71

0.71

a

Number of latent vectors in optimum model. This is the number of LVs for which class. err. (CV) is a minimum. b Classification error under cross validation: fraction of total number of samples that are misclassified (based on excluded samples in cross validation) for the number of LVs in previous column.

using fecal water13 or urine.16 An augmented set of buckets covering the whole spectral range was used for the IBS versus controls model, as there was no requirement to exclude the regions containing the drug and drug metabolite signals that were excluded for UC samples. The three CV methods were again compared, and the results are summarized in Table 4. Overall, the two-group models performed better than the three-group model, with the different CV methods showing the same trend in prediction rates as observed for the three-group model. Taking the most rigorous CV method, leave one individual out, the best prediction rate was for C versus UC (sensitivity = 81%, specificity = 93% for UC); the worst was C versus IBS (sensitivity = 57%, specificity = 76% for IBS). As reported above, the UC set included five samples with prominent NMR signals from lactate, a metabolite that was generally absent from other samples. Four of these samples were among those with the highest scores on LV1 (Figure 2a). UC set 2 was formed by removal of the five high lactate samples to check that a good model could still be obtained to distinguish UC from C. Results for UC set 2 versus C are reported in Table 4 and gave almost as good a prediction rate as the full UC set versus C when the same CV method was used. Four UC samples (out of a total of 31) were collected from three patients during flare-up episodes that occurred in the course of the study. Two of these samples were in the small number that contained lactate, but the flare-up specimens did not classify as a group separate from remission samples: in fact, they tended to cluster (in PCA) with other replicates from the same individuals. Combination of NMR and DGGE Data

Up to four samples were collected from each individual over the course of 2 years, and the majority of these were analyzed by both DGGE and NMR. The intraindividual variability was evaluated for the DGGE20 and NMR data. Hierarchical cluster

analysis (HCA) showed that the DGGE microbial profile of most volunteers and patients remains remarkably consistent over the period of study (Figure 3a). HCA based on the NMR spectra also showed some clustering but to a lesser extent (Figure 3b: see, for example, C8, C18, UC11, UC15, IBS4). This is consistent with the behavior of the multivariate models described above, which suggested that some CV models are overfit due to the replicate nature of samples originating from single individuals. Canonical correlation analysis (CCA) was used to explore the link between the NMR and DGGE data. It was applied to subsets of principal component (PC) scores from each data set, in both cases accounting for 90% of the original information content. The first canonical variates for NMR and DGGE are plotted against one another in Figure 3c. The correlation is r = 0.85 (p < 0.002). This is strong evidence for a direct causal link between the microbial composition in both health and disease and the corresponding fecal metabolite profiles. An interesting feature of the CCA analysis is that it also provides evidence of a systematic difference between the healthy and disease states, in both sample sets. The symbols used in Figure 3c are color coded according to sample type. We emphasize that the grouping information is not used in the PCA/CCA algorithm at all; hence, any evidence of grouping can in no sense be overfit. We see that the majority of samples on the lower part of the diagonal are controls, whereas the majority on the upper part are IBS or UC. Note the three samples from volunteer C8, which are quite far removed from the center of the control group. It is worth remarking that the multivariate analyses consistently failed to classify these three samples correctly on cross-validation. The four UC samples with the highest NMR canonical variate scores all had a pair of relatively intense broad resonances (half-height line width 15 Hz) at 3.43 ppm (δ13C 61.4 ppm) and 3.61 ppm (δ13C 63.5 ppm). These signals, which were absent or very weak in all other samples, have not yet been identified. We attempted to identify correlations between NMR signals and DGGE band intensities using a correlation heat map (data not shown) following previous successful approaches in which covariation of urinary metabolites with fecal bacterial species populations has been demonstrated.26,27 Although some correlation values > 0.7 were observed, similarly high values were obtained in over 25% of cases in a permutation test (in which the observation labels of the NMR and DGGE sets were scrambled 1000 times) and hence we did not pursue this approach further. Identification of Altered Metabolites

The recently introduced selectivity ratio23 (SR) was used to rank the variables (NMR buckets) according to their importance for the PLS-DA classification results. The SR calculation was originally applied to a binary classification problem in the context of a target-projection or OPLS type analysis. It may also be applied to the three-group PLS-DA model when, as here, a binary classification is carried out for each group with respect to the other two combined as illustrated in Figure 2bd. Then a different set of SRs is calculated for each group, rather than a single SR for every variable as in the true two-group case. SR plots are shown for UC (versus controls plus IBS) and for IBS (versus controls plus UC) in Supporting Information Figure S2. These plots show the importance for UC of taurine (3.43, 3.27 ppm); polyamines and/or lysine (3.093.03, 1.72, 1.45 ppm); glucose (3.40. 5.23, 4.65 ppm and other buckets); and some unknowns 4213



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Figure 3. (a) Hierarchical cluster analysis (HCA) on DGGE data (autoscaled data, Euclidean distance, complete linkage). (b) HCA on NMR data, details as above. (c) Canonical correlation analysis between NMR and DGGE data sets, based on PC scores accounting for 90% of original variance. r = 0.85, p < 0.002.

(1.97, 1.96 ppm); and for IBS of bile acids (0.72, 0.93, 0.68 ppm) and branched chain fatty acids (0.92, 0.86 ppm). It is interesting to compare these SR plots with the corresponding variable importance in projection (VIP) scores plots (Supporting Information Figure S3), since VIP scores have often been used for interpretation of multidimensional PLS-DA models. The two methods have some influential variables in common but the VIP gives greater importance than the SR plot to lactate (1.33 ppm) and acetate (1.92 ppm): the acetate signal is present at high intensity in all samples; lactate occurred in only a few UC samples, although with high intensity when present. Univariate analysis (see below) indicates no significant difference between groups for these two buckets, so in these two cases the VIP method would be misleading. SR is the ratio between explained variance (with respect to the known sample grouping) and residual variance and, as such, is more open to a simple numerical interpretation than the VIP value. In fact the maximum SR values (0.30.35) for the most influential variables correspond to rather low values of the ratio, and this reflects the relatively high number of LVs included in the model. Two-group models (UC versus controls or IBS versus controls) rank the same variables as most influential but only have marginally higher maximal SR values. In view of the rather low SR values obtained by multivariate analysis, a univariate analysis of intensities in each column of the bucket table was carried out. Table 5 shows a list of

buckets/metabolites with p < 0.001 (three groups, Kruskal Wallis test) together with results of posthoc multiple comparison tests for pairwise differences. Results for a few other metabolites of interest with p > 0.001 have been added. Graphs of the intensity distributions for selected metabolites, discussed below, are shown in Figure 4. The distribution pattern for certain metabolites indicates the possible existence of subgroups within the UC and IBS samples and this is commented on below although the relatively low number of volunteers does not permit a rigorous investigation of the possibility. Role and Origin of Individual Metabolites

Acetate, propionate, and butyrate are the main SCFAs produced during fermentation by gut bacteria28 and are responsible for some of the most prominent signals in NMR spectra of fecal water. However, these SCFAs are mostly absorbed in the colon with less than 5% remaining in the faeces.29 In this study, we found that fecal butyrate and acetate were slightly reduced in UC compared with controls while in IBS butyrate was slightly increased. However, none of the major SCFAs showed significant pairwise differences between groups following multiple comparison tests. A previous NMR study of fecal extracts had shown significant reductions in butyrate and acetate in CD patients and a nonsignificant reduction of butyrate in UC.13 A reduced level of SCFAs in feces has been linked to a shift in the composition and, presumably, in metabolic activity of colonic 4214



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Table 5. Significance of Metabolites for Discrimination between the Three Groups multiple comparison testsb ppm

metabolite

p valuea

C vs UC

C vs IBS

UC vs IBS

ns

metabolites with p < 0.001 1.96 (s)

U1

1.2 106

C > UCe

ns

6

UC > Ce

ns

ns

3.43

taurine

2.8 10

3.20

choline

5.8 106

UC > Ce

IBS > Ce

ns

3.27

taurine

1.1 105

UC > Ce

IBS > Cc

ns

5

e

C > UC UC > Cd

C > IBSe IBS > Cd

ns ns

C > IBSd

ns

2.73 (s) 3.49

U2 glucose

1.7 10 0.00017

0.86

2-methylbutyrate

0.00022

C > UCd e

1.72

cadaverine

0.00035

UC > C

ns

ns

0.92

isovalerate

0.0006

ns

C > IBSe

ns

0.72

deoxycholate

0.00084

ns

IBS > Ce

IBS > UCc

metabolites with low/no significance

a

1.09

isobutyrate

0.0058

ns

C > IBSd

ns

1.92 1.56

acetate butyrate

0.033 0.041

ns ns

ns ns

ns ns

2.36

glutamate

0.17

ns

ns

ns

4.11

lactate

0.19

ns

ns

ns

1.48

alanine

0.35

ns

ns

ns

1.06

propionate

0.98

ns

ns

ns

KruskalWallis test, three groups. b Dunn’s post test. c 0.01 < p < 0.05. d 0.001 < p < 0.01. e p < 0.001. Not significant (ns).

Figure 4. Distributions of intensities for selected metabolites based upon the normalized bucket table. Horizontal lines show median and interquartile range.

microbiota in IBD,7,30 specifically a reduction in butyrateproducing bacterial groups. Altered profiles of fecal microbiota have also been found in IBS patients.20 Butyrate is a chief energy source for intestinal epithelial cells and is also believed to have anti-inflammatory properties and to enhance epithelial

antibacterial barrier defenses.31 Defects in butyrate utilization32 or in uptake33 by epithelial cells have been considered to be a characteristic of UC. There have been contradictory reports on changes in levels of fecal butyrate in relation to UC and IBS. It has been reported to increase over controls in active UC,32,34 but also 4215


Journal of Proteome Research to decrease in severe active cases and increase in remission.35,36 In IBS, increased butyrate and reduced acetate and propionate have been reported in one study,37 but in another study increased acetate and propionate with unchanged butyrate were indicated.38 Resistant starch, cellulose, and other dietary plant polysaccharides that reach the colon as well as host derived glycans (mucin, etc.) are the major nutrient source for gut bacteria; bacterial enzymes degrade the polysaccharides to glucose and other constituent monosaccharides. Glucose is further metabolized to produce SCFAs and is often reduced to levels where it is not detectable by NMR. However, residual glucose was detected in a proportion of samples in all three groups and was somewhat higher in UC fecal extracts (and to a lesser extent in IBS) than in controls; the selectivity ratios showed that buckets associated with glucose contributed to the separation of the UC group in multivariate analysis. Lactate levels were unusually high in five UC samples (from four volunteers; two of these samples, 3-2 and 3-F, from the same volunteer were exceptionally high). None of the control or IBS samples had unusual values. Two of the five were “flare-up” samples, although the other two “flare-up” samples collected did not have elevated lactate. Lactate concentrations in feces are usually low because lactate consuming, as well as lactate producing, bacteria are present in the gut microbiota.39 However, levels of lactate that were much higher than normal have been measured in faeces from patients with active ulcerative colitis.35,40 A dialysis experiment on a similar group of patients suggested that the lactate was secreted from inflamed mucosal cells rather than being a product of bacterial metabolism.41 The same mechanism could apply to volunteers in our cohort except that the cited observations of elevated lactate were made for patients with moderate-severe rather than quiescent colitis. By measurement of fecal calprotectin, a marker of intestinal inflammation, the UC subjects in our study were divided into two groups (Figure S4, Supporting Information). One group (n = 6) had calprotectin levels < 75 mg/kg, whereas in the second group (n = 7) levels were > 125 mg/kg with a maximum value (including “flare-up” samples) of 175 mg/kg. These values are comparable with those previously reported42 for UC patients in remission and receiving medication, but considerably less than the median value of 327 mg/kg reported for patients with active UC.43 Three of the four volunteers with elevated lactate levels detected by NMR were members of the group with higher calprotectin measurements. Elevated levels of taurine were found in a number of UC samples, but very rarely in controls. Four out of the five samples described above with high lactate also had high taurine. There were cases where all repeat samples from a volunteer showed this feature as well as instances where only one repeat had high taurine. Taurine may be present in fecal water as a result of bacterial deconjugation of bile acids.44 However, it may also originate from mucosal cells where NMR studies of human colonic biopsies15 and mouse intestinal tissue45,46 have shown it to be a major constituent. Intracellular levels of taurine were significantly raised in patients with active UC,15 a change that was related to the antioxidant and anti-inflammatory properties of taurine;47 however, significant differences were not established between metabolites in biopsies from controls and UC cases in remission.15 The role of taurine as a regulator of muscle contractility or as an osmolyte has also been discussed in relation to altered taurine levels in mouse intestinal tissues following

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either deliberate modification to gut microbiota46 or indirect modification through parasitic infection.45 Cadaverine was observed at levels comparable with or exceeding lysine in about one-third of UC samples but was not generally detected in control samples, whereas lysine was always present. Some UC samples with cadaverine also contained related compounds in 5-aminopentanoate, putrescine, and N-acetylputrescine, which were not observed in controls. Colonic bacteria can utilize peptides and amino acids through deamination or decarboxylation reactions producing carboxylic acids or phenols by the first reaction and amines by the second one.48 Cadaverine is the decarboxylation product of lysine degradation, a reaction that provides an acid resistance mechanism in Escherichia coli. Cadaverine has been previously measured in fecal water from healthy volunteers, although it was not present in all samples: only two (E. coli and Clostridium bifermentans) out of 16 species of intestinal bacteria grown anaerobically in PY medium produced cadaverine.48 Increased levels of cadaverine in UC could arise as a result of changed conditions in the colon, for example, an increase in acidity, or from a shift in the bacterial population that favored cadaverine producers. Cadaverine has not previously been mentioned in connection with UC, although greatly increased levels of fecal polyamines were reported in children suffering from nutrient malabsorption.49 Urines from IL10 deficient mice (a Crohn’s disease model), that had developed intestinal inflammation, were found to contain increased amounts of 5-aminopentanoate, a metabolite of cadaverine.19 Although 5-aminopentanoate did not appear as a marker in our study, it was detected in the majority of UC samples with raised cadaverine. Colonic bacteria are capable of producing branched chain fatty acids (BCFAs), as well as SCFAs, from proteins, peptides, and amino acids. In particular, isobutyrate, isovalerate, and 2-methylbutyrate are produced by oxidation of valine, leucine, and isoleucine, respectively.50 In comparison with butyrate, the absorption and metabolism of colonic BCFA has been little investigated.51 We found a reduction in BCFA levels in both UC and IBS samples with respect to controls, with the difference being more significant in IBS. There was no difference in levels of the precursor branched chain amino acids in the three groups, nor indeed of other amino acids such as alanine and glutamate. Since the supply of branched chain amino acids was apparently unchanged, the difference seen in IBS patients may result from a reduction in the number of BCFA producing bacteria. In related studies, higher levels of amino acids were found in fecal extracts from CD and, to a lesser extent, UC patients,13 and fermentations in an in vitro colon model with fecal microbiota from CD and UC patients resulted in production of higher levels of BCFAs and SCFAs compared with inocula from controls.52 Bile salt (deoxycholate) levels were high in about 50% of IBS samples relative to control and UC groups, with only a few outliers from those two groups having comparably high levels. As a result of the enterohepatic circulation, only a small proportion of bile salts enter the colon; however, application of a reliable measurement method for whole body bile acid retention revealed that bile acid malabsorption was not uncommon in cases of chronic diarrhea, including ones where a diagnosis of IBS had been made.53 The dihydroxy bile acids have a cathartic effect on the colon: at higher concentrations, they inhibit sodium reabsorption in the colon, leading to reduced water transport53 and possibly diarrhea. A systematic literature review concluded that bile acid malabsorption was not rare in diarrhea-predominant 4216


Journal of Proteome Research IBS (IBS-D) and that clinicians should consider it as a possible cause in recommending therapies.54 Indeed, suggested therapies for IBS include both drugs for reducing colonic bile acids in IBSD and for increasing them in constipation-predominant IBS.55 The difference in the metabolic profiles observed in fecal waters of UC and healthy controls was correlated with the distinct difference in the composition of the gut microbiota in the two groups. Further understanding of the activity of gut bacteria in the development of inflammatory conditions will allow development of strategies for modulating the composition or metabolic activity of the gut bacteria through dietary interventions or application of probiotics.56

’ ASSOCIATED CONTENT

bS

Supporting Information Additional experimental details. This material is available free of charge via the Internet at http://pubs.acs.org

’ AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected]. Telephone: +44 1603 255280. Fax: +44 1603 507723. )

Author Contributions

These authors contributed equally.

’ ACKNOWLEDGMENT This work was supported by the BBSRC Competitive Strategic Grant and by the Ministry of Higher Education of Saudi Arabia (to S.O.N.). ’ REFERENCES (1) Hotoleanu, C.; Popp, R.; Trifa, A. P.; Nedelcu, L.; Dumitrascu, D. L. Genetic determination of irritable bowel syndrome. World J. Gastroenterol. 2008, 14 (43), 6636–6640. (2) Mathew, C. G.; Lewis, C. M. Genetics of inflammatory bowel disease: progress and prospects. Hum. Mol. Genet. 2004, 13 (Spec No 1), R161–168. (3) Alonso, C.; Guilarte, M.; Vicario, M.; Ramos, L.; Ramadan, Z.; Antolin, M.; Martinez, C.; Rezzi, S.; Saperas, E.; Kochhar, S.; Santos, J.; Malagelada, J. R. Maladaptive intestinal epithelial responses to life stress may predispose healthy women to gut mucosal inflammation. Gastroenterology 2008, 135 (1), 163–172 e1. (4) Loftus, E. V., Jr.; Sandborn, W. J. Epidemiology of inflammatory bowel disease. Gastroenterol. Clin. North Am. 2002, 31 (1), 1–20. (5) Andoh, A.; Yagi, Y.; Shioya, M.; Nishida, A.; Tsujikawa, T.; Fujiyama, Y. Mucosal cytokine network in inflammatory bowel disease. World J. Gastroenterol. 2008, 14 (33), 5154–5161. (6) Chadwick, V. S.; Chen, W.; Shu, D.; Paulus, B.; Bethwaite, P.; Tie, A.; Wilson, I. Activation of the mucosal immune system in irritable bowel syndrome. Gastroenterology 2002, 122 (7), 1778–1783. (7) Sartor, R. B. Microbial influences in inflammatory bowel diseases. Gastroenterology 2008, 134 (2), 577–594. (8) Macfarlane, G. T.; Macfarlane, S. Human colonic microbiota: ecology, physiology and metabolic potential of intestinal bacteria. Scand. J. Gastroenterol., Suppl 1997, 222, 3–9. (9) Backhed, F.; Ley, R. E.; Sonnenburg, J. L.; Peterson, D. A.; Gordon, J. I. Host-bacterial mutualism in the human intestine. Science 2005, 307 (5717), 1915–1920. (10) van Nuenen, M. H.; de Ligt, R. A.; Doornbos, R. P.; van der Woude, J. C.; Kuipers, E. J.; Venema, K. The influence of microbial

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Metabolomics of Fecal Extracts Detects Altered Metabolic Activity of

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