Age-Related Topographical Metabolic Signatures ... - ACS Publications

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Age-Related Topographical Metabolic Signatures for the Rat Gastrointestinal Contents Yuan Tian,†,‡ Limin Zhang,† Yulan Wang,*,† and Huiru Tang*,† †

State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Centre for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, the Chinese Academy of Sciences, Wuhan 430071, P. R. China ‡ Graduate University of the Chinese Academy of Sciences, Beijing 100049, P. R. China S Supporting Information *

ABSTRACT: Symbiotic gut microbiota is essential for mammalian physiology and analyzing the metabolite compositions of gastrointestinal contents is vital for understanding the microbiome−host interactions. To understand the developmental dependence of the topographical metabolic signatures for the rat gastrointestinal contents, we systematically characterized the metabolite compositional variations of the contents in rat jejunum, ileum, cecum, and colon for two age-groups using 1H NMR spectroscopy and multivariate analysis. Significant topographical metabolic variations were present for the jejunal, ileal, cecal, colonic contents, and feces, reflecting the absorption functions for each intestinal region and the gut microbiota therein. The concentrations of amino acids, lactate, creatine, choline, bile acids, uracil and urocanate decreased drastically from jejunal to ileal contents followed with steady decreases from cecal content to feces. Short-chain fatty acids (SCFAs) and arabinoxylan-related carbohydrates had highest levels in cecal content and feces, respectively. Such topographical metabolic signatures for the intestinal contents varied with animal age highlighted by the level changes for lactate, choline, taurine, amino acids, carbohydrates, keto-acids, and SCFAs. These findings provided essential information for the topographical metabolic variations in the gastrointestinal tract and demonstrated metabolic profiling as a useful approach for understanding host−microbiome interactions and functional status of the gastrointestinal regions. KEYWORDS: gastrointestinal content, feces, gut microbiota, 1H NMR spectroscopy, metabonomics, multivariate statistical analysis

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INTRODUCTION Symbiotic gut microbiota (microbiome) plays important roles in many aspects of mammalian physiology and thus in the hosts’ health and diseases. The compositional structure and functions of microbiome have, therefore, become an important topic across the research fields in microbial ecology,1,2 drug metabolism,3,4 microbiome-mammal interactions4−6 and mammalian pathophysiology.7,8 It is now known that human body harbors trillions of microbes mainly in the gastrointestinal tract with thousands of different species.2,6,9 Their compositional structure is dynamic depending on both endogenous factors, such as host’s genotype, age and immunological capabilities, and environmental factors such as diets and living conditions.2 Gut microbiota not only functions in the development of the immune system,10 food digestion and absorption2 but also affects and even is involved in the host metabolic regulations such as drug and bile acid metabolisms.3,11 Consequently, the states of gut microbiota appeared to be associated with the pathogenesis of many noninfectious diseases such as inflammatory bowel disease,10 colorectal cancer,12 atherosclerosis,13 metabolic disorders14 and even neurological disorders.15 Co-metabolisms of gut microbiota and its mammalian hosts are important aspects of the mammal−microbiome interactions underpinning the aforementioned gut microbiota functions.2,3 © 2011 American Chemical Society

The communal compositions of gut microbiota vary with growth development16 and most likely the gastrointestinal topography17 as well to facilitate the functions of different intestinal regions. Therefore, information of the gut microbiota metabolism and its variations in different intestinal regions is essential for understanding the topographical or compartmentational aspects of the gut microbiota and host cometabolisms. Such information is also vital for understanding the mechanistic aspects of microbiome contributions to the normal physiology of hosts and for developing new therapeutic strategies thus for maintaining health and disease preventions. However, both the microbial ecology and the gut microbiota metabolisms in different intestinal regions are not fully understood so far and it is not possible to isolate and culture all these microbiota externally. Nevertheless, a potentially effective solution to this problem is to understand the metabolic profiles of intestinal contents as a whole since these profiles contain information of the host and gut microbiota metabolisms as well as their interactive cometabolism. NMR-based metabonomics approaches ought to be effective in defining the topographical metabolic signatures of intestinal Received: October 17, 2011 Published: December 1, 2011 1397

dx.doi.org/10.1021/pr2011507 | J. Proteome Res. 2012, 11, 1397−1411

Journal of Proteome Research

Article

contents. In fact, high-resolution 1H NMR methods have proven to be able to simultaneously detect a wide range of metabolites presented in many different biological matrixes, such as plasma and urine, especially when used in conjunction with pattern recognition methods. With such methods, characteristics of metabolite compositional information associated with many physiological and pathological conditions can be readily extracted. For understanding the topographical intestinal biochemistry, the metabonomic features for rat and human intestinal tissues showed that each intestinal regions had their own unique metabolic fingerprints and the greatest differences were present between the small and large intestines.18−20 Such intestinal metabolic fingerprints are dependent on the animal developmental process as well.18 It is thus conceivable that the gastrointestinal contents may have their own unique metabolite profiles and dependence on the animal development. A number of works have been published in metabolic profiling of fecal samples using NMR methods. The results showed that interspecies differences were present for humans, mice and rats in terms of their fecal metabolite compositions.21 Several other studies of feces have also been reported on method optimizations for metabolite compositional analysis22 and applications for the diagnosis of diseases, such as inflammatory bowel disease,23 chronic pancreatitis24 and colorectal cancer.25 With more relevance to what discussed, 1 H NMR spectral profiles of ileal content have been found to carry important information for the complex interactions between mammalian metabolisms and their gut microbiome.3 These studies have undoubtedly proved the feasibility of 1H NMR spectroscopy for analysis of the fecal metabolite composition and potential usefulness of such analysis. However, there is still a knowledge gap in the topographical metabolic signatures of mammalian intestinal contents and their dependence on different physiological factors. In this study, therefore, we systematically analyzed the metabolite compositions of the rat jejunal, ileal, cecal, colonic contents and feces, which can be considered as the rectal content, for two different age groups using 1H NMR spectroscopy in conjunction with multivariate data analysis. The objectives of this work are to define the topographical metabolic profiles for rat gastrointestinal contents and their variations associated with animal developmental processes under normal physiological conditions. The results will provide baseline information for evaluating the functions of gut microbiota in dietary and drug effects and disease processes.

Provincial Office of Science and Technology (SYXKE 2009− 0051). Twenty male Sprague−Dawley rats (seven weeks old) obtained from the Animal Center of Tongji Medical College (Wuhan, China) were randomly divided into two groups (n = 10) and were allowed to have free access to the drinking water and basal diet. Animals in one group were sacrificed at the age of 12 weeks old with the average body weight as 339 ± 21 g whereas those in the other group were sacrificed three weeks later (15 weeks old) with body weight as 416 ± 25 g. Twelve hour fasting was allowed for all animals prior to sacrifice by neck dislocation under isoflurane anesthesia. All fecal samples were collected one day before sacrifice whereas the intestinal contents from jejunum, ileum, cecum and colon were collected immediately after sacrifice from the middle part of these intestinal regions, respectively. All samples were snap frozen with liquid nitrogen upon collection and stored at −80 °C until further analysis. Sample Preparation

The intestinal contents and fecal samples were directly extracted using an optimized procedure described previously.22 Briefly, samples (50−60 mg) were mixed with 600 μL precooled phosphate buffer described earlier. After vortex mixing for about 30 s, the mixed slurry was subjected to freeze− thaw treatments (3 times) and followed with homogenization with a tissuelyser (QIAGEN, Hilden, Germany) at 20 Hz. Following centrifugation (11,180g, 4 °C) for 10 min, the supernatants (550 μL) were transferred into 5 mm NMR tubes directly for NMR analysis. Parallel intestinal content samples from both animal groups were also lyophilized and weighted to obtain the relevant dry weight (or water content). NMR Spectroscopic Analysis

D2O (99.9% in D) was obtained from Sigma-Aldrich Inc. (St. Louis, MO). Sodium 3-(trimethylsilyl) [2,2,3,3-2H4] propionate (TSP) was purchased from Norell Inc. (Landisville, NJ). K2HPO4·3H2O and NaH2PO4·2H2O were obtained from Sinopharm Chemical Reagent Co. Ltd. (Shanghai, China). Na−K phosphate buffer (PB) containing 10% D2O and 0.58 mM TSP was prepared with K2HPO4 and NaH2PO4 (0.1 M, pH = 7.4) and employed as the extracting solvent due to its good stability at low temperature.26

1 H NMR spectra were recorded at 298 K on a Bruker AVIII 600 MHz spectrometer equipped with an inverse cryogenic probe (Bruker Biospin, Germany). An one-dimensional NMR spectrum was acquired for each of all samples employing the first increment of NOESY pulse sequence (NOESYPR1D). To suppress water signal, a weak continuous wave irradiation was applied to the water peak during recycle delay (2 s) and mixing time (100 ms). The 90° pulse length was adjusted to approximately 10 μs for each sample and 64 transients were collected into 32 k data points for each spectrum with spectral width of 20 ppm. For resonance assignment purposes, a series of 2D NMR spectra including 1H−1H correlation spectroscopy (COSY), 1 H−1H total correlation spectroscopy (TOCSY), 1H−13C heteronuclear single quantum correlation (HSQC), and 1 H−13C heteronuclear multiple bond correlation (HMBC) were acquired for selected samples and processed with similar parameters described previously.27,28 To measure the apparent diffusion-coefficients of metabolites especially some oligosaccharides, diffusion-ordered spectroscopy (DOSY) experiments were carried out using the Wu-Johnson methods29,30 with the diffusion constant of 200 ms, SINE-shaped gradient pulse length of 1 ms, a recovery delay of 150 μs and 48 pulsed-field gradients (1.6−31.2 G/cm). The intensities of concerned signals were fitted as a function of gradient strengths on a personal computer to obtained diffusion coefficients.

Sample Collection

Spectral Data Processing and Multivariate Data Analysis

Animal experiments were carried out in accordance with the National Guidelines for Experimental Animal Welfare (MOST, P.R. China, 2006) with a SPF facility certified by the Hubei

Free induction decays were multiplied by an exponential function individually with a line-broadening factor of 1.0 Hz prior to Fourier transformation. Each spectrum was corrected

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MATERIALS AND METHODS

Chemicals

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for phase- and baseline-distortions manually with the chemical shift referenced to TSP (δ0.00). The spectral region at δ0.5− 9.0 was then integrated into bins of 0.004 ppm (2.4 Hz) using AMIX package (v3.8, Bruker Biospin). The regions at δ4.455− 5.200 were discarded to eliminate the effects of imperfect water suppression. The areas of all bins were then normalized to the dry weight of respective raw samples. Multivariate data analysis was carried out on the normalized NMR data sets with the software package SIMCA-P+ (v 12.0, Umetrics, Sweden). Principal component analysis (PCA) was carried out on the mean-centered data with the scores plots showing intergroup differences and the possible presence of outliers. The orthogonal projection to latent structurediscriminant analysis (OPLS-DA) was further performed with 7-fold cross-validation using the unit-variance scaled (UV) NMR data as X-matrix and class information as Y-matrix.31 The quality of the model was described by the parameters R2X representing the total explained variations for X matrix and Q2 indicating the model predictability. The significance validity of all models was ensured with CV-ANOVA approach (p < 0.05).32 In order to maintain the spectral features for the ease of interpretation, back-transformation33 of the loadings generated from the OPLS-DA was performed prior to generating the loadings plots, which were color-coded with the Pearson linear correlation coefficients of variables (or metabolites) using an inhouse developed script for MATLAB (The Mathworks Inc.; Natwick, MA). In these loadings plots, the warm-colored variables meant more significant contributions to intergroup differentiations than cool-colored ones. Correlation coefficients of 0.602 was used as the cutoff value (i.e., |r| > 0.602) for the statistical significance based on the discrimination significance at the level of p ≤ 0.05, which was determined according to the test for the significance of the Pearson’s product-moment correlation coefficients. In order to illustrate topographical variations of metabolites along the intestinal tract, the relative changes for some representative metabolites were also calculated against their levels in jejunal content in the form of (Cm − Cj)/Cj, where Cj is the metabolite concentration in the jejunal content and Cm is that in the other intestinal contents. These data were used to illustrate the topographical dependence of metabolite levels and the significances of such were based on OPLS-DA analysis.

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Figure 1. 1H NMR spectra (600 MHz) of (A) jejunal content, (B) ileal content, (C) cecal content, (D) colonic content, and (E) fece from a rat. The δ5.15−8.50 region was vertically expanded 8 times for the purpose of clarity. Key: a full assignment of metabolites is given in Table 1.

xylose, arabinose and galactose were readily assignable by comparing with the standard 1D and 2D NMR data for them. An oligosaccharide has been detected in the form of arabinoxylan in cecal, colonic contents and fecal samples with doublets from α-L-arabinofuranosyl (α-L-Araf) residues at δ5.2−5.4 in the 1H NMR spectra. This was further confirmed with correlations between H-1 (δ5.24) and H-2 of α-L-Araf residues (δ4.10−4.14) in its COSY and TOCSY spectra (Table 1, Figure S1, Supporting Information). These and 1H−13C HSQC data (Table 1) indicated this sugar as α-L-Araf-(1→2)[α-L-Araf-(1→3)]-β-D-Xylp-(1→4)-D-Xylp (A2X2) by showing similar NMR data reported for this hemicellulosic arabinoxylan previously.35 Its diffusion coefficient (3.43 × 10−10 m2/s, Figure S2B, Supporting Information) from a DOSY experiment agreed with its molecular mass (594 Da) estimated from the Viel’s equation29 for uncharged oligosaccharides. The diffusion coefficients for xylose, arabinose and galactose were found to be 7.40 × 10−10, 7.10 × 10−10 and 6.22 × 10−10 m2/s, respectively (Figure S2 and Table S1, Supporting Information), which were also consistent with the results from Viel’s equation.29 The spectral features of these extracts (Figure 1) indicated distinct metabolite compositional signatures for the contents of different intestinal regions. This is clearly highlighted by the obvious abundances of amino acids in the jejunal and ileal contents including glycine, glutamate, glutamine, aspartate, asparagine, histidine, and branched chain amino acids (BCAAs; valine, leucine and isoleucine). In contrast, highest levels for SCFAs were observable in the cecal content whereas fecal samples, which could be regarded as the rectal content, showed the highest levels in hemicellulose sugars including xylose, arabinose, galactose and A2X2 but lowest levels in amino acids. To extract the detailed metabolic differences in the different intestinal contents, multivariate data analysis was conducted on the NMR profiles.

RESULTS

1

H NMR Spectra of Rat Intestinal Contents and Feces

Figure 1 shows the representative 1H NMR spectra for extracts of intestinal contents from jejunum, ileum, cecum, colon and feces, respectively, from a randomly selected rat. Assignments of the metabolite resonances were achieved based on the published results,21,34 publically available and in-house databases and further confirmed individually with a series of 2D NMR spectra. Fifty-four metabolites were assigned with their 1 H and 13C data tabulated in Table 1. These results showed that metabolites in rat intestinal contents and feces consisted of amino acids, nonglucose carbohydrates, organic acids and amines, choline, creatine, methanol, nucleosides, aromatic acids related to gut microbiota metabolism, bile acids and short-chain fatty acids (SCFAs). However, some metabolites were only detected in certain gastrointestinal regions with 3-hydroxyphenylacetate detected only in feces and several saccharides detected only in large intestinal contents and feces (Figure 1 and Table 1). Among them, three monosaccharides including

Topographical Metabolic Variations in Intestinal Contents

PCA results (Figure 2) showed that, for both 12 and 15 weekold rats, separations were present for their metabolic profiles of the intestinal contents in different compartments with the greatest differences occurred between the small intestinal 1399



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Table 1. NMR Data for the Metabolites Found in the Contents of Jejunum, Ileum, Cecum, Colon and Feces no.

metabolites

moieties

δ 1H (ppm) and multiplicitya

δ 13C (ppm)

1

butyrate

0.90(t) 1.56(m) 2.15(t)

2

α-ketoisocaproate

3

isoleucine

4

leucine

5

valine

6

propionate

7

α-keto-β-methyl-valerate

8

α-ketoisovalerate

9

lactate

10

threonine

11

alanine

12

lysine

13

citrulline

14

arginine

CH3 βCH2 αCH2 COOH 2*CH3 CH CH2 CO δCH3 γCH3 γCH2 γ′CH2 βCH αCH COOH δCH3 δCH3 γCH βCH2 αCH COOH γCH3 γCH3 βCH αCH COOH CH3 CH2 COOH δCH3 γ′ CH3 γCH γCH′ βCH CH3 CH CO CH3 CH COOH γCH3 αCH βCH COOH βCH3 αCH COOH γCH2 δCH2 βCH2 εCH2 αCH COOH γCH2 βCH2 δCH2 αCH CO COOH γCH2 βCH2

16.3 21.5 42.7 186.8 25.0 26.3 46.0 181.6 14.2 17.7 27.5 27.5 37.7 62.4 177.1 24.5 23.5 27.3 42.8 56.4 178.3 19.6 20.7 32.0 63.3 177.1 13.2 33.7 187.4 12.9 16.5 27.4 27.4 42.2 19.3 40.2 185.6 22.5 71.9 185.3 22.3 63.2 69.1 175.8 19.2 53.4 178.8 23.9 29.4 33.0 42.2 57.6 177.5 27.8 30.7 42.0 57.6 164.0 177.5 26.9 30.6

0.92(d) 2.06(m) 2.61(d) 0.94(t) 1.01(d) 1.25(m) 1.48(m) 1.98(m) 3.67(d) 0.96(d) 0.97(d) 1.69(m) 1.71(m) 3.74(t) 0.99(d) 1.04(d) 2.27(m) 3.62(d) 1.06(t) 2.19(q) 0.88(t) 1.10(d) 1.47(m) 1.69(m) 2.93(m) 1.13(d) 3.02(m) 1.33(d) 4.11(q) 1.33(d) 3.59(d) 4.26(m) 1.48(d) 3.79(q) 1.48(m) 1.72(m) 1.90(m) 3.03(t) 3.76(t) 1.57(m) 1.87(m) 3.15(t) 3.75(t)

1.73(m) 1.93(m) 1400

locationb ce,co,f

ce,co,f

j,i,ce,co,f

j,i,ce,co,f

j,i,ce,co,f

ce,co,f

ce,co,f

ce,co,f

j,i,ce,co,f

j,i,ce,co,f

j,i,ce,co,f

j,i,ce,co,f

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Table 1. continued no.

metabolites

15

acetate

16

proline

17

glutamate

18

methionine

19

glutamine

20

aspartate

21

asparagine

22 23

trimethylamine (TMA) creatine

24

choline

25

taurine

26 27

methanol glycine

28

β-xylose

29

α-xylose


moieties δCH2 αCH CNH COOH CH3 COOH γCH2 βCH2 β′CH2 δCH2 δ′CH2 αCH COOH βCH2 β′CH2 γCH2 αCH CO COOH δCH3 βCH2 γCH2 αCH COOH βCH2 γCH2 αCH CO COOH βCH2 β′CH2 αCH βCOOH αCOOH βCH2 β′CH2 αCH CO COOH 3*CH3 CH3 CH2 CNH COOH N(CH3)3 NCH2 OCH2 CH2SO3 NCH2 CH3 CH2 COOH 2CH 3CH 4CH 5CH2 5′CH2 1CH 2CH 3CH 4CH

3.23(t) 3.75(t)

1.92(s) 2.01(m) 2.07(m) 2.36(m) 3.34(m) 3.45(m) 4.13(m) 2.10(m) 2.09(m) 2.36(m) 3.77(m)

2.14(s) 2.16(m) 2.65(t) 3.86(m) 2.10(m) 2.46(m) 3.77(m)

2.68(m) 2.82(m) 3.91(m)

2.86(dd) 2.96(dd) 4.00(m)

2.88(s) 3.04(s) 3.93(s)

3.21(s) 3.52(m) 4.07(m) 3.25(t) 3.43(t) 3.37(s) 3.57(s) 3.23(dd) 3.46(t) 3.63(m) nd 3.93(dd) 4.57(d) 3.54(dd) 3.67(t) 3.63(m) 1401

δ 13C (ppm) 43.4 57.6 159.6 177.4 26.2 184.2 26.6 31.9 31.9 49.0 49.0 64.1 177.4 30.1 30.1 36.4 57.6 184.0 177.5 16.8 33.2 31.6 56.9 176.6 30.1 33.8 57.5 180.5 177.4 39.5 39.5 55.3 180.5 176.9 37.6 37.6 54.3 177.1 176.3 47.6 40.0 57.1 159.4 177.2 56.8 58.5 70.2 50.7 38.5 51.9 44.6 175.2 77.6 79.2 72.9 nd nd 99.9 75.1 75.4 73.1

locationb

j,i,ce,co,f j,i,ce,co,f

j,i

j,i,ce,co,f

j,i

j,i

j,i

ce,co,f j,i,ce,co,f

j,i,ce,co,f

j,i,ce,co,f j,i,ce,co,f j,i,ce,co,f ce,co,f

ce,co,f



Article


metabolites

30

uracil

31

fumarate

32

tyrosine

33

tryptophan

34

phenylalanine

35

urocanate

36

histidine

37

malate

38

succinate

39

5-aminovalerate

40 41

formate pyruvate

moieties 5CH2 1CH CH CH CO CO CH COOH βCH2 β′CH2 αCH 3 or 5CH 2 or 6CH C(ring) C−OH(ring) COOH βCH2 β′CH2 αCH 5CH 6CH 2CH 7CH 4CH COOH βCH2 β′CH2 αCH 2 or 6CH 4CH 3 or 5CH C(ring) COOH CHCOOH CH(ring) 5CH 3CH COOH βCH2 β′CH2 αCH 5CH 3CH C(ring) COOH βCH2 β′CH2 αCH βCOOH αCOOH CH2 COOH 2CH2 3CH2 1CH2 4CH2 COOH CH CH3 CO COOH 1402


δ 13C (ppm)

3.67(dd) 5.20(d) 5.81(d) 7.54(d)

nd 95.6 103.9 146.5 170.6 155.9 138.1 179.2 38.3 38.3 59.2 118.8 132.4 129.4 157.7 177.1 29.5 29.5 58.5 122.5 125.0 128.2 114.9 121.5 177.4 38.4 38.4 59.3 130.7 131.9 132.0 139.4 176.4 124.5 132.3 123.9 144.4 178.8 30.8 30.8 58.7 120.1 138.3 133.6 176.4 45.3 45.3 73.2 182.4 183.6 37.3 185.4 25.2 29.3 39.4 42.2 186.1 172.4 29.2 172.9 207.9

6.53(s) 3.06(dd) 3.15(dd) 3.94(dd) 6.91(d) 7.20(d)

3.31(dd) 3.49(dd) 4.06(dd) 7.21(t) 7.29(t) 7.33(s) 7.55(d) 7.74(d) 3.13(dd) 3.29(dd) 3.98(dd) 7.33(m) 7.38(m) 7.43(m)

6.40(d) 7.31(d) 7.43(s) 7.89(s) 3.14(dd) 3.25(dd) 3.99(dd) 7.08(s) 7.83(s)

2.38(dd) 2.70(dd) 4.31(dd)

2.41(s) 1.62(m) 1.65(m) 2.24(t) 3.02(t) 8.45(s) 2.38(s)

locationb

j,i,ce,co,f

j,i,ce,co,f j,i,ce,co,f

j,i

j,i,ce,co,f

j,i,ce,co,f

j,i

j,i,ce,co,f

ce,co,f j,i,ce,co,f

i,ce,co,f ce,co,f



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Table 1. continued δ 1H (ppm) and multiplicitya

no.

metabolites

moieties

42

α-ketoglutarate

43 44 45

dimethylamine (DMA) bile acids adenine

46

inosine

47

β-arabinose

48

α-arabinose

49

β-galactose

50

α-galactose

51

uridine

52

3-hydroxyphenylacetate

53

3-hydroxyphenylpropionate

54

A2X2 (Arabinoxylan)c α-L-Araf-(1→2)d

γCH2 βCH2 CO COOH(CO) COOH CH3 C18 axial CH3 2CH 6CH CH2 ′CH2 5H′ 4H′ 2H′ 8H 2H 2CH 3CH 4CH 5CH2 5′CH2 1CH 2CH 3CH 4CH 5CH2 5′CH2 1CH 2CH 3CH 4CH 5CH 1CH CH2 2CH 3CH 4CH 5CH 1CH CH2 CH2 ′CH2 4H′ 3H′ 2H′ 5H 6H 1H′ CH2 4CH 2CH 6CH 5CH CH2 CH2COOH 4CH 2CH 6CH 5CH 2CH 3CH 1403

2.45(t) 3.01(t)

2.76(s) 0.70(m) 8.19(s) 8.21(s) 3.85(dd) 3.92(dd) 4.28(q) 4.44(t) 6.10(d) 8.24(s) 8.34(s) 3.52(dd) 3.67(t) 3.95(m) nd 3.86(dd) 4.52(d) 3.85(dd) 3.99(t) 3.90(m) nd 4.02(dd) 5.25(d) 3.48(dd) 3.65(dd) 3.93(m) 3.71(m) 4.59(d) 3.74(m) 3.81(dd) 3.85(dd) 3.99(m) 4.09(m) 5.27(d) 3.74(m) 3.81(d) 3.92(d) 4.14(q) 4.24(t) 4.36(t) 5.90(d) 5.91(d) 7.87(d) 3.48(s) 6.75(m) 6.81(d) 6.89(dd) 7.25(t) 2.47(t) 2.85(t) 6.76(m) 6.80(m) 6.87(m) 7.25(m) 4.13(dd) 3.96(t)

δ 13C (ppm) 33.4 38.6 208.1 172.6 183.9 39.4, 149.0 145.5 63.8 63.8 88.6 73.4 91.4 150.1 143.3 75.0 76.2 73.6 nd nd 99.1 74.4 72.4 71.3 nd nd 95.5 74.9 75.8 71.9 78.0 99.6 64.2 71.4 72.2 72.3 74.6 95.4 64.2 64.3 64.3 86.6 73.1 78.0 95.2 90.8 144.1 47.0 116.6 118.6 123.0 133.5 34.1 44.6 116.6 124.0 119.0 133.5 84.7 73.4

locationb ce,co,f

j,i,ce,co,f j,i,ce,co,f ce,co,f j,i

ce,co,f

ce,co,f

ce,co,f

ce,co,f

j,i

f

f

ce,co,f



Article


metabolites

α-L-Araf-(1→3)

β-D-Xylp-(1→4)d

α-D-Xylp

β-D-Xylp

55 56

moieties 4CH 5CH 5′CH 1CH 2CH 3CH 4CH 5CH 5′CH 1CH 2CH 3CH 4CH 5CH 5′CH 1CH 2CH 3CH 4CH 5CH 5′CH 1CH 2CH 3CH 4CH 5CH 1CH

U1 U2


δ 13C (ppm)

4.14(m) nd nd 5.24(d) 4.17(dd) 4.00(t) 4.22(m) 3.70(dd) 3.83(dd) 5.24(d) 3.24(dd) 3.58(t) 3.80(m) nd nd 4.58(d) 3.54(dd) 3.72(t) nd 3.77(dd) 3.85(dd) 5.20(d) 3.50(dd) 3.66(t) nd 3.99(dd) 4.60(d) 2.06(s) 8.00(s)

88.4 nd nd 115.8 84.3 78.1 91.7 64.7 64.7 115.8 77.6 74.2 74.7 nd nd 99.9 75.0 74.9 nd 61.2 61.2 95.6 78.8 83.0 nd 67.6 103.6 30.1 141.0

locationb

ce,co,f

ce,co,f

ce,co,f

ce,co,f

co,f j

a

s, singlet; d, double; t, triplet; q, quartet; m, multiplet; dd, double of doubles; nd, not determined. bj, jejunal content; i, ileal content; ce, cecal content; co, colonic content; f, fece. cA2X2: α-L-Araf-(1→2)-[α-L-Araf-(1→3)]-β-D-Xylp-(1→4)-α/β-D-Xylp (the last D-Xylp was in both α and β forms). dAraf, arabinofuranosyl; Xylp, xylopyranosyl.

Figure 2. PCA scores plots obtained from NMR data for the intestinal contents from 12-week old rats (left) and 15-week old rats (right). (A) jejunal content (■), (B) ileal content (●), (C) cecal content (◆), (D) colonic content (*), and (E) fece (▲).

intestinal contents in two adjacent compartments were displayed in the corresponding color-coded coefficient plots (Figure 3A−D) and also tabulated in Table 2. Compared with jejunal content, ileal content contained significantly more formate but less amino acids, taurine, lactate, creatine, choline, uracil, uridine, urocanate, inosine and bile acids. The cecal content had more SCFAs, α-ketoisovalerate and adenine but much less lactate, creatine, choline, taurine, urocanate, formate and amino acids than the ileal content. Furthermore, colonic content contained less amino acids, succinate, SCFAs, α-ketoisocaproate, α-ketoisovalerate, urocanate, uracil and adenine than the cecal content. Moreover, feces contained more SCFAs, taurine, succinate, 3-hydroxyphenyla-

contents and large intestinal contents (Figure S3, Supporting Information). To obtain the metabolic variations associated with different intestinal compartments, pairwise OPLS-DA was performed between data obtained from adjacent compartments for the 12 weeks old rats (Figure 3) and the 15 weeks old rats (Figure S4, Supporting Information). The model quality indicators (R2X and Q2 Table 2) clearly showed that the extracts obtained from adjacent compartments were distinctive in terms of their metabolite compositions. Such was further supported with the results from the model evaluation with CVANOVA (p < 0.05) for OPLS-DA models (Figures 3 and S4, Supporting Information). The metabolites with statistically significant contributions to differentiation between the 1404



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Figure 3. OPLS-DA scores plots (left) and coefficient plots (right) derived from 1H NMR spectra showing the discrimination between adjacent intestinal contents. These models were evaluated with CV-ANOVA for (A) jejunal content vs ileal content with p = 4.78 × 10−5, (B) ileal content vs cecal content with p = 7.03 × 10−6, (C) cecal content vs colonic content with p = 1.09 × 10−4, and (D) colonic content vs fecal extract with p = 5.31 × 10−7. Keys to metabolites assignment are given in Table 1.

Age-associated Metabolic Variations in the Gastrointestinal Contents

cetate, xylose, arabinose, galactose and A2X2 but less trimethylamine (TMA), methanol, urocanate and bile acids than the colonic content. Colonic content contained highest level of methanol among all intestinal contents. The relative amount of metabolites from the intestinal contents varied markedly with the topographical locations descending intestinal tract for the 12 weeks old rats (Figure 4). Similar results were observed for the 15 weeks rats (Figure S5, Supporting Information) and the significance of metabolite changes associated with different intestinal compartments was shown in Figure 3 and Table 2. The levels of amino acids, lactate, creatine, choline and taurine clearly showed a drastic decrease from jejunal to ileal contents (Figure 4A−B); their level decreases continued from ileal to cecal contents though to much less extent. While a small increase was observed for taurine levels from colonic content to feces (Figure 4B), no significant differences were observed for the levels of choline, lactate creatine and most of amino acids for the cecal, colonic contents and feces. The levels of SCFAs increased drastically from small intestine to the large intestine although lower levels were displayed for them in the colonic content among the large intestine contents (Figure 4C). Furthermore, the levels of uracil and urocanate showed a sharp decrease from jejunal to ileal contents but steady level decline from cecal content to feces (Figure 4D). The levels of bile acids had an about 80% decrease from jejunal to ileal contents with a small decrease from ileal to colonic contents. Their levels had a further decrease of about 10% from colonic content to feces (Figure 4D).

OPLS-DA was also carried out for the metabolic profiles of the same intestinal contents from the 12-week-old and 15-week-old rats. The models were also cross-validated and the significance of group separation were evaluated with CV-ANOVA (p < 0.05) (Figure 5). The significant metabolic changes associated with growth or aging are illustrated in Figure 5A−D and Table 3. No significant difference was observed for the metabolite compositions of the jejunal contents between rats aged 12 weeks old and those of 15 weeks old (R2 = 0.65, Q2 = 0.37, p = 0.14). In contrast, the ileal contents from the 15-week-old rats contained significantly higher levels of amino acids but lower levels of methanol and succinate than those from the 12-weekold rats (Figure 5A). The age-associated variations in metabolic profiles of the large intestinal contents were diverse. The cecal content from the 15-week-old rats contained much more αketoisocaproate, alanine, lactate, succinate, acetate, TMA, uracil and formate than those from the 12-week-old rats (Figure 5B). However, the former had much less α-ketoisovalerate, choline, taurine, fumarate, arabinose, arabinoxylans and malate than the latter. In contrast, the colonic content had significant more amino acids, xylose, arabinose, galactose, arabinoxylans and inosine together with less amount of methanol for the 15 weeks old rats (Figure 5C). In the fecal samples, significantly higher levels of propionate and inosine were observed for rats at the age of 15 weeks together with lower levels of taurine, xylose, arabinose, galactose and arabinoxylans (Figure 5D). 1405



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Table 2. Correlation Coefficients for Metabolites with Significant Differences between Adjacent Intestinal Contents Obtained from 12-Week Old Ratsa metabolites (no.) Isoleucine (3) Leucine (4) Valine (5) Threonine (10) Alanine (11) Lysine (12) Methionine (18) Glutamate (17) Glutamine (19) Aspartate (20) Asparagine (21) Glycine (27) Tyrosine (32) Histidine (36) Phenylalanine (34) Tryptophan (33) Lactate (9) Creatine (23) Choline (24) Taurine (25) Urocanate (35) Bile acid (44) Butyrate (1) Propionate (6) Acetate (15) α-ketoisocaproate (2) α-ketoisovalerate (8) Uracil (30) Uridine (51) Adenine (45) Inosine (46) TMA (22) Methanol (26) α-xylose (29) α-arabinose (48) α-galactose (50) A2X2b(54) Succinate (38) Formate (40) 3-hydroxyphenylacetate (52) 3-hydroxyphenylpropionate (53)

jejunal content vs ileal content R2X = 0.74, Q2= 0.82

ileal content vs cecal content R2X = 0.67, Q2= 0.86

0.90 0.89 0.90 0.91 0.88 0.88 0.88 0.88 0.86 0.86 0.82 0.91 0.89 0.61 0.91 0.85 0.91 0.89 0.92 0.80 0.62 0.76

0.65 0.64 0.63 0.69 0.61 0.62 0.64 0.72 0.76 0.77 0.70 0.74 0.69 0.83 0.69 0.78 0.83 0.76 0.66 −0.85 −0.97 −0.81 −0.84

0.85 0.82

cecal content vs colonic content R2X = 0.60, Q2= 0.77

colonic content vs feces R2X = 0.62, Q2 = 0.89

0.65 0.64 0.77 0.67 0.65 0.62

0.62

0.72 0.74 0.85 0.66 0.87 0.64 0.77

−0.97

0.83

−0.65 0.85

−0.94

−0.70

−0.66 0.65 0.73 −0.73 −0.73

0.83

0.66 −0.70

0.78 0.86 −0.88 −0.87 −0.91 −0.88 −0.73

0.69 −0.85 −0.72

a

The coefficients were from OPLS-DA results; positive and negative signs indicate positive and negative correlation in the concentrations, respectively. The coefficient of 0.602 was used as the cutoff value for the significant difference evaluation. bA2X2: α-L-Araf-(1→2)-[α-L-Araf-(1→3)]β-D-Xylp-(1→4)-α/β-D-Xylp (the last D-Xylp was in both α and β forms).

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DISCUSSION

to be related to the functions of different intestinal compartments and gut microbiota there. The levels of common amino acids in the intestinal contents were reduced markedly from jejunum to ileum with a further decline when reached cecum; the levels for most of the amino acids remained unchanged from cecum to rectum (Figure 4A). This indicated that the absorption of amino acids, for rats, mainly occurred in jejunum and to less extent in ileum. Such absorption was almost completed in the small intestine. This is broadly agreeable with previous findings in terms of the important roles of small intestinal mucosa in nutrient absorption.36 Nevertheless, this study provided direct quantitative evidence for such notions and for many more metabolites than previously reported. The results also indicated that amino

The metabolites detected in the extracts of intestinal contents were mainly originated from four sources, namely epithelial cells of mammalian intestine, endogenous metabolites of gut microbiota, food and its metabolites. Therefore, the biochemical compositions of the intestinal contents carry important information on the states of functioning for the mammalian gastrointestinal tract, gut microbiota and their interactions. The focus of the present investigation is to systematically characterize the detailed metabolite compositions of the rat intestinal contents in different intestinal compartments and the age-associated variations of them, which ought 1406



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Figure 4. Relative concentrations of metabolites with respective to that in jejunal content for 12-week old rats. A−D plots show metabolites having changes to different extents, and metabolites with statistically significant differences are listed in Figure 3 and Table 2.

Figure 5. OPLS-DA scores plots (left) and corresponding coefficient plots (right) showing the discrimination between intestinal contents obtained from rats aged 12 and 15 weeks old. These models were evaluated with CV-ANOVA for (A) ileal content with p = 0.048, (B) cecal content with p = 2.19 × 10−7, (C) colonic content with p = 1.27 × 10−4, and (D) fecal extract with p = 0.043. Keys to metabolites are given in Table 1.

acids in the large intestine were probably endogenous metabolites of gut microbiota since these amino acids from food were mostly reabsorbed in the small intestine. No sugars have been detected in the contents of small intestine including both jejunum and ileum (Figure 1A−B).

Since the jejunal content used in this study has been obtained from the middle part of jejunum, this observation probably suggests that both digestion and absorption of the starchy carbohydrates are efficiently completed in the upper part of jejunum. However, hemicellulosic carbohydrates such as 1407



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Table 3. Correlation Coefficients for Metabolites with Significant Differences in Intestinal Contents between Rats Aged 12 and 15 Weeks Olda metabolite (no.) Isoleucine (3) Leucine (4) Valine (5) Alanine (11) Lysine (12) Glutamate (17) Methionine (18) Aspartate (20) Tyrosine (32) Phenylalanine (34) Propionate (6) Acetate (15) α-ketoisocaproate (2) α-ketoisovalerate (8) Lactate (9) TMA (22) Choline (24) Taurine (25) Methanol (26) α-xylose (29) α-arabinose (48) α-galactose (50) A2X2b (54) Uracil (30) Inosine (46) Fumarate (31) Malate (37) Succinate (38) Formate (40)

ileal content R2X = 0.56, Q2 = 0.47

cecal content R2X = 0.40, Q2 = 0.90

0.78 0.66 0.83 0.77 0.70 0.81 0.76 0.82 0.74 0.83

colonic content R2X = 0.76, Q2 = 0.77

fece R2X = 0.69, Q2 = 0.46

0.66 0.65 0.62 0.68 0.62 0.65 0.74 0.71 0.69 0.67 0.62 0.81 0.91 −0.77 0.90 0.71 −0.83 −0.80

−0.89 −0.78 −0.77 0.69

−0.63

0.86

−0.63 −0.93 0.81 0.73 0.80 0.73

−0.75 −0.80 −0.77 −0.79

0.70

0.68

−0.89 −0.74 0.78 0.97

a

Coefficients were from OPLS-DA results; positive and negative signs indicate positive and negative correlation in the concentrations, respectively. The coefficient of 0.602 was used as the cutoff value for the significant difference evaluation. bA2X2: α-L-Araf-(1→2)-[α-L-Araf-(1→3)]-β-D-Xylp(1→4)-α/β-D-Xylp (the last D-Xylp was in both α and β forms).

It is well-known that short chain fatty acids, such as acetic, propionic and n-butyric acids, are fermentation products of nondigestible dietary fibers by gut microbiota.38 These SCFAs are important energy sources for epithelial cells in the animal intestines and modulating agents for various digestive functions. They also serve as fuel for active ion transportation in the large intestines.39 Here, we observed remarkable level increases for SCFAs in the cecal content compared with ileal content and the levels of SCFAs appeared to be relatively high throughout the large intestine (Figure 4C). This suggests that gut microbiota with capability of fermenting dietary fibers is mainly located in large intestine (especially in cecum), being broadly consistent with previous results.40 Furthermore, gut microbiota are capable of converting lactate into SCFAs,41 which may also contribute partially to the increased levels of SCFAs in cecum. The rapid reduction of the lactate level from jejunum to cecum further supported such notion (Figure 4B). Choline in the intestinal content is mainly from food and can be absorbed and converted into phosphorylcholine42 by mammals or into TMA by gut microbiota.43 The level reduction of choline with amino acids (Figure 4B) was probably associated with the absorption of choline in jejunum and ileum, being agreeable with the kinetic results for choline uptake in the small intestine of neonatal and adult rats.44 Nevertheless, the concurrent level increase of TMA from small

arabinose, xylose, galactose and arabinoxylan were detected exclusively in large intestine and their levels reached maximum in the feces (Figure 3D) with no glucose detected. These carbohydrates were mainly nonstarch sugars and were probably released from hemicellulose in the dietary fiber carbohydrates by bacterial hydrolytic degradations.37 The levels of bile acids showed about 80% decrease from jejunum to ileum and a much smaller decline (about 5%) from ileum to colon (Figure 4D). Another 10% level decrease was evident for bile acids from colon to rectum. This is consistent with the common knowledge that, under normal physiological conditions, 90% bile acids are reabsorbed in gastrointestinal tract via enterohepatic circulation. Although the exact contributions of different intestinal regions to the reabsorption of bile acids remain unknown, our data suggest that more than 80% bile acids are recovered in small intestine while about further 15% such acids are absorbed in large intestine with about 5% bile acids excreted in feces. Furthermore, current textbook knowledge states that bile acids are reabsorbed in the terminal ileum. However, the present data have shown that about 80% bile acids have already been reabsorbed prior to the middle part of ileum. Therefore, it seems more likely that bile acids are efficiently recovered in the regions between distal jejunum and the proximal ileum. 1408



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ocytes were significantly decreased in adult guinea pigs compared to sucklings.53 Taurine level was lower in cecal content obtained from the 15-week-old rats than in those from 12-week-old animals (Figure 5B). Although in vitro experiments have shown a decrease in the taurine absorption capacity of intestinal strips from sucklings to adults for rats and mice,54,55 no in vivo data are currently available for the age-associated changes for adults’ rodents in terms of the taurine absorption in intestine. Our observation probably reflected some associated developmental changes in adulthood. The decline of taurine level in the older rats may also be attributable to the reduced deconjugation of taurine-conjugated bile acids by gut microbiota, which varies with growth and aging. Fifteen-week-old rats had significantly less xylose, arabinose, galactose and arabino-xylans in their fecal samples than the 12week-old animals (Figure 5D). These sugars are probably released from hemicellulose dietary fibers with bacterial degradation, which have been reported in the products from in vitro degradation of the fibers.56 Close inspection showed that such differences were also present in colonic contents for these two age groups of rats (Figure 5C). Although the biological significance of such differences remains to be fully understood, these differences may result from the age-related differences in gut microbiota (or microbial ecology) possessing different hemicellulose digestibility. In fact, age-related changes in gut microbiota are well documented. Generally, newborns acquire intestinal microbes from their mother and surrounding environment during development via suckling, nursing and weaning, which is a dynamic process.17 For example, during the first few weeks, E. coli, Lactobacillus and Streptococcus are the dominant microbes in rats57 and after weaning, anaerobic bacteria, such as Bacteroidaceae and Lactobacilli, become the dominant microbes.58 Recent studies in man indicate shifts in the composition of the intestinal microbiota in the population of advanced age group.59 The reduction in numbers and diversity of many beneficial or protective anaerobes, such as bacteroides and bifidobacteria, in conjunction with shifts in the dominant bacterial species, such as lactobacilli, eubacteria, enterobacteria, streptococci and yeasts, and proteolytic bacteria such as fusobacteria, propionibacteria and clostridia was observed in some elderly people.59 In conclusion, NMR analysis revealed unique metabolite compositional signatures for the contents of jejunum, ileum, cecum, colon and feces from adult rats. The variations of these metabolite profiles of intestinal contents were dependent on the topographical locations of the gastrointestinal tract and animal age. These differences were associated with the combination of intestinal functions such as nutrient absorptions and activities of gut microbiota in different intestinal compartments. This work provided much more details in the gastrointestinal regions where reabsorption was functioning for different metabolites including these from foods and endogenous metabolisms. The results also demonstrated the metabolic analysis of intestinal contents as a powerful tool for investigating the interactions between mammals and their gut microbiota. It is foreseeable that monitoring the metabolite changes in intestinal contents probably has important potentials for disease diagnosis and health managements via modulation of gut microbiota with medical and dietary interventions.

intestine to large intestine (Figure 3B, Table 2) is also indicative of choline-to-TMA conversion mediated by gut microbiota. Creatine in intestinal content may also be from dietary sources which normally account for more than 50% of all creatine in mammal circulations. Its remarkable level decline from jejunum to cecum is consistent with its concurrent absorption with glucose and lactate in the rat small intestine.45 Literature works have also reported that creatine absorption can occur in both ileum and colon tissues ex vivo.46 However, our results appear to suggest that jejunum and perhaps proximal ileum are the major regions for such creatine absorption. Taurine is a multifunctional metabolite with important roles in neuronal development and modulation, protection against oxidative stress and cellular osmoregulation.47 In the gastrointestinal tract, taurine conjugated bile acids are present3,48 functioning as emulsifiers to assist the absorption of dietary fat. At the same time, these conjugated bile acids can be deconjugated by gut microbiota.3 Therefore, taurine detected in the jejunal and ileal contents is attributable partially to such deconjugation. The marked level decline of taurine from jejunum to cecum is probably related to its efficient reabsorption by small intestine49 and/or the taurine-to-sulfate conversion50 by gut microbes. It is important to note that taurine in small intestine can function as an absorption promoter for some drugs and preventive factors against aspirin-induced gastric mucosal lesions in rats.51 It has also been reported that poor intestinal absorption of taurine is associated with diabetes and can cause retinal, cardiac, neural, and immune dysfunctions.52 Therefore, the above results have indicated that the metabolite compositions of intestinal contents have unique signatures for different compartments under normal physiological conditions. Dysfunction of any regions in gastrointestinal tract will probably lead to metabolite profile changes for contents of the concerned intestinal regions. It is thus conceivable that high throughput NMR analysis of the metabolite composition in feces may be of significant importance for detecting more diseases than already reported23−25 and probably for prognosis as well. It is likely that the metabolite compositional analysis of intestinal contents can also provide important information on the functions (or dysfunction) of gastrointestinal tract at a specific topographical locations although such analysis is more invasive. Metabolic Variations of Intestinal Contents Related to Growth

We also observed some metabolic changes of intestinal contents associated with aging/growth for adult rats although only limited age window (12−15 weeks old) was covered. Noticeable differences were observed for ileal, cecal, colonic contents and feces between these two age groups whereas the jejunal content showed no significant differences for the metabolite composition. Both the ileal and cecal contents from 15 weeks old rats contained higher levels of amino acids than those from 12 weeks old rats (Figure 5A−B). This implies that the older rats have slower or less efficient absorption of amino acids, which is broadly consistent with previous findings53 probably due to decreased transportation of amino acids with the increase of rat age. Such observation is also in broad agreement with previous findings that influxes of dipeptides, glycylglycine and glycyl-L-leucine from mucosa to the enter1409



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ASSOCIATED CONTENT

S Supporting Information *

Supplemental figures and tables. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Fax: +86-27-87199291. Tel: +86-27-87198430. E-mail: [email protected]. Fax: +8627-87199291. Tel: +86-27-87197143.

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ACKNOWLEDGMENTS We acknowledge the financial supports from the Ministry of Science and Technology of China (2009CB118804 and 2010CB912501), National Natural Science Foundation of China (20825520, 20775087, 20921004, and 21175149) and Chinese Academy of Sciences (KJCX2-YW-W11). We also thank Dr. Hang Zhu of Wuhan Institute of Physics and Mathematics for developing MATLAB scripts used for colorcoded OPLS-DA coefficient plots, which was originally downloaded from http://www.mathworks.com/matlabcentral/ fileexchange.

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