Gut Microbiota Composition Modifies Fecal Metabolic Profiles in Mice

In order to reveal the metabolic relationship between host and microbiome, we monitored recovery of the gut microbiota composition and fecal profiles ...
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Gut Microbiota Composition Modifies Fecal Metabolic Profiles in Mice Ying Zhao,†,‡ Junfang Wu,‡ Jia V. Li,§ Ning-Yi Zhou,∥ Huiru Tang,*,‡ and Yulan Wang*,‡ †

School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, PR China Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Centre for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan, 430071, PR China § Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW72AZ, U.K. ∥ Key Laboratory of Agricultural and Environmental Microbiology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, 430071, PR China ‡

S Supporting Information *

ABSTRACT: The gut microbiome is known to be extensively involved in human health and disease. In order to reveal the metabolic relationship between host and microbiome, we monitored recovery of the gut microbiota composition and fecal profiles of mice after gentamicin and/or ceftriaxone treatments. This was performed by employing 1H nuclear magnetic resonance (NMR)-based metabonomics and denaturing gradient gel electrophoresis (DGGE) fingerprint of gut microbiota. The common features of fecal metabolites postantibiotic treatment include decreased levels of short chain fatty acids (SCFAs), amino acids and primary bile acids and increased oligosaccharides, D-pinitol, choline and secondary bile acids (deoxycholic acid). This suggests suppressed bacterial fermentation, protein degradation and enhanced gut microbial modification of bile acids. Barnesiella, Prevotella, and Alistipes levels were shown to decrease as a result of the antibiotic treatment, whereas levels of Bacteroides, Enterococcus and Erysipelotrichaceae incertae sedis, and Mycoplasma increased after gentamicin and ceftriaxone treatment. In addition, there was a strong correlation between fecal profiles and levels of Bacteroides, Barnesiella, Alistipes and Prevotella. The integration of metabonomics and gut microbiota profiling provides important information on the changes of gut microbiota and their impact on fecal profiles during the recovery after antibiotic treatment. The correlation between gut microbiota and fecal metabolites provides important information on the function of bacteria, which in turn could be important in optimizing therapeutic strategies, and developing potential microbiota-based disease preventions and therapeutic interventions. KEYWORDS: gut microbiota, antibiotics, NMR, PCR-DGGE, fecal extracts, metabonomics

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dynamic,4 which is driven by host genome,5 age,6 nutrition,7 life-style, disease,8 and therapeutic interventions (e.g., antibiotic use9 and surgery10). Unbalanced microbial colonies may disturb the physiological homeostasis, leading to various diseases such as colon cancer,11 inflammatory bowel disease (IBD),12 irritable bowel syndrome (IBS),13 obesity,14−16 diabetes,17 cardiovascular disease,18 autism19 and allergic asthma.20 To probe this host−microbial interplay within the context of health and disease, animal models with different microbial manipulations were used. These include germ-free (GF) rodents,21 mice transplanted with human baby microbiota,22 mice with a partial microbial knockout,18,23−26 microbial modification using prebiotics,27 probiotics28,29 and dietary

INTRODUCTION A large number of microbiota inhabit the mammalian gut, and their symbiotic and mutualistic relationship with the host (host−bacteria and bacteria−bacteria interactions) determine a super complex and dynamic ecosystem. Bacteroidetes, Firmicutes, and approximately 500 other species are the dominant phyla in the mammalian gut microbial community, which codevelop with the host throughout their lifetime and remarkably influence host health and disease status. A shift in the gut microbial composition can stimulate specific diseaseprone activity (dysbiosis)1 or disease-protective activity (probiosis).2 For example, Lactobacillus reuteri strains can produce thiamine to benefit the host,2 while Bif idobacteria may inhibit potential pathogen colonization by competing with the nutrients and binding sites on the mucosa.3 The composition of the gut microbial community is incredibly complex and © XXXX American Chemical Society

Received: March 24, 2013

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interventions, as well as obese rodents.30 In the absence of gut microbiota, the GF models do not exhibit key phenotypic features that involve the gut microbial process. Conversely, gut microbiota transplantation to GF or antibiotics-treated animals allows us to study the metabolic behavior of specific bacterial groups. For instance, plasma levels of tryptophan is higher in GF mice, whereas indoxyl sulfate, derived from tryptophan via host−microbial cometabolism, is lower in GF mice, as compared with conventional animal models. This pathway has been confirmed by Clostridium sporogenes transplanted GF mice.31 GF animals also exhibit defects in morphology, physiology and immunology,21 and the observed metabolic changes, therefore, could be due to the absence of gut microbiota and/or these intrinsic defects. Shaping the gut microbial composition of conventional animal models using nonenteric absorbable antibiotics given via an oral route serves as an alternative model, which provides complementary information. Antibacterial antibiotics that are often used include vancomycin,25,32 streptomycin and penicillin G,24 enrofloxacin,33,34 ciprofloxacin,35 and imipenem/cilastatin sodium.26 Gentamicin is an effective antibiotic, particularly against Gramnegative bacteria and several strains of Mycoplasma; it inhibits bacterial protein synthesis through irreversible binding to the 30S bacterial ribosome.36 Ceftriaxone is also a widely used injectable β-lactam antibiotic that exhibits potent activity against both Gram-positive and Gram-negative bacteria. In addition, the combination of gentamicin and β-lactam antibiotics demonstrates a strong antibacterial effect against Enterococci.37 Both gentamicin and ceftriaxone are not absorbable by the host following oral administration.36 High-resolution NMR spectroscopy, gas chromatography− mass spectrometry (GC−MS) and liquid chromatography− mass spectrometry, coupled with multivariate data statistical analyses, are widely employed to investigate metabolites in various biological matrixes, such as biofluids and organ tissues.21,24,38−40 However, the bacterial group that is responsible for producing a particular metabolite or its precursor remains a key question. Since the gut microbiota closely interacts with ingested food in the intestine via multiple pathways including fermentation, putrefaction, hydrolysis, and dehydroxylation, the changes of fecal composition and corresponding association with the perturbed gut microbial community should provide information regarding the action of the gut microbiota. In the current study, we treated mice with gentamicin and/or ceftriaxone and monitored changes in fecal composition and gut microbiota using 1H NMR spectroscopy of fecal extracts and 16S rRNA gene polymerase chain reaction (PCR)denaturing gradient gel electrophoresis (DGGE), respectively. In addition, we investigated the correlation between the gut microbial composition and the metabolites in feces under different antibiotic-treated conditions with a view to probe the function of gut microbiota. Exploring these metabolic phenotypes and the gut microbiota functionalities is of particular importance for optimizing therapeutic strategies and developing potential microbiota-based disease preventions and therapeutic interventions.

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Control (Hubei, P. R. China, No.00003651) and housed in an SPF animal facility (No. 00004565) with free access to water and commercial rodent food at Wuhan Institute of Virology (Hubei, P. R. China, No. SYXK (Hubei) 2005_0034). The experiments were carried out in accordance with the National Guidelines for Experimental Animal Welfare (MOST of People’s Republic of China, 2006). The mice were acclimatized for 10 days under controlled environmental conditions (temperature, ∼20 °C; humidity, 40−70%; light−dark cycle, 12−12 h) prior to the start of the experiment. All mice were randomly separated into 5 groups with 12 mice in each group and housed in cages with 6 mice each cage. Antibiotics were administered by oral gavage twice a day (8:00 a.m. and 2:00 p.m.) for 4 days with the following dosage: (1) GL group received a low dose of sulfate gentamicin (80 mg × 2/kg/day, 0.5 mL); (2) GH group received a high dose of sulfate gentamicin (360 mg × 2/kg/day, 0.5 mL); (3) CEF group received ceftriaxone sodium (1000 mg × 2/kg/day, 0.5 mL); (4) COM group received combined antibiotic treatment (sulfate gentamicin: 180 mg × 2/kg/day, 0.25 mL; ceftriaxone sodium: 500 mg × 2/kg/day, 0.25 mL); and (5) the control group (CTR) was treated with saline (0.9% NaCl, 0.5 mL). All the antibiotics were diluted in 0.9% saline. Sample Collection

Fecal samples were collected at 1 day preintervention and 2, 4, 5, 6, 7, 9, 11, 14, 17, 20, 23, and 27 days postintervention. At least 4 pellets of feces were collected from each mouse, transferred into Eppendorf tubes, immediately frozen by liquid nitrogen and stored at −80 °C. Fecal samples were divided into two parts for NMR and microbiological analysis (PCR-DGGE), respectively. Sample Preparation for NMR Spectroscopy

Fecal extracts for NMR spectroscopy was prepared by mixing 50 mg of fecal samples with 500 μL of phosphate buffer (0.1 M K2HPO4/NaH2PO4 = 4/1, pH = 7.4) containing 30% D2O, 0.002% (w/v) of sodium 3-(trimethylsilyl) propionate-2,2,3,3d4 (TSP), 0.03% of Na3N (w/v). After vortex mixing, the samples were subjected to a freeze−thaw cycle 3 times in liquid nitrogen and subsequently homogenized with a tissuelyser (QIAGEN, Hilden, Germany) at 20 Hz, 90 s, followed by centrifugation at 10000g for 10 min at 4 °C. Supernatants were collected, and the remaining pellet was further extracted once as described above. Supernatants obtained from two runs of extraction were combined and centrifuged at 10000g for 10 min at 4 °C. A total of 600 μL of supernatant was transferred into NMR tubes with an outer diameter of 5 mm pending NMR analysis.41 NMR Spectroscopy 1 H NMR spectra of fecal extracts were obtained at 298 K using a Bruker Avance III 600 MHz NMR spectrometer (operating at 600.13 MHz for proton, Bruker Biospin, Germany) equipped with a TCI cryogenic probe.42 The first increment of pulse sequence (recycle delay−90°−t1−90°−tm−90°−acquisition) was recorded with a spoil gradient for water presaturation for all the samples. The recycle delay of 2 s, t1 of 4 μs and the mixing time (tm) of 80 ms were set. The 90° pulse length was adjusted to 10 μs. A total of 64 scans were collected into 32k data points for each spectrum with a spectral width of 20 ppm. To assist metabolite assignment, a range of two-dimensional (2D) NMR experiments (1H−1H correlation spectroscopy (COSY), 1H−1H total correlation spectroscopy (TOCSY), J-

MATERIALS AND METHODS

Animal Model

Sixty female BALB/c mice, aged 7 weeks old with a body weight of 20 ± 2 g, were purchased from the Center for Disease B

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resolved spectroscopy (JRES), 1H−13C heteronuclear single quantum correlation spectroscopy (HSQC), and 1H−13C heteronuclear multiple bond correlation spectroscopy (HMBC) were carried out on the selected fecal extracts using standard methods.42−44 Further assignment of metabolites was also accomplished with the use of statistical total correlation spectroscopy on 1D spectroscopy.45,46

axis denoting as positions of the bands and Y axis as the intensities of the bands using the same software. The data matrix from DGGE was normalized to the sum of the intensity number prior to pattern recognition analysis. Following visual examination, bands that are markedly different in the compared groups were selected and subjected to further identification of the band by sequencing as previously described.50 Briefly, PCR was repeated on the excised DNA bands using primers 357f_GC and 518r. The resulting PCR products were confirmed by agarose gel electrophoresis, ligated with pGEMT Easy Vector (Promega) and then transformed into Escherichia coli DH5α. The inserted DNA of positive clones was amplified using nest approach. The primers for the first PCR were T7 (5′-TAA TAC GAC TCA CTA TAG GG-3′) and SP6 (5′-ATT TAG GTG ACA CTA TAG-3′) and for the second, 357f_GC and 518r. The PCR products were electrophoresed by DGGE to verify the position to original band. The clones migrating to the same position with the original band were sequenced. (Genomics, Wuhan, China). The sequences of the excised DGGE bands were aligned with known nucleotide sequences in the RDP database (Release 10, http://rdp.cme.msu.edu) using the seqmatch option. A phylogenetic tree for 16S rRNA gene sequences was constructed in MEGA (Version 5.05) using the NeighborJoining method. The sequences are deposited in the EMBL Nucleotide Sequence Database with access numbers of HE 866509, HE 866510 and HE 866512−866522.

NMR Data Processing 1

H NMR spectra of fecal extracts were corrected for phase and baseline distortion and referenced manually to the TSP resonance at δ 0.00 using TOPSPIN package (V2.0, Bruker Biospin, Germany). Spectral region δ 0.50−9.50 was integrated into regions with an equal width of 0.002 ppm using AMIX (V3.9.5, Bruker Biospin, Germany). The distorted water regions (δ 4.67−4.98) were removed. The remaining spectral data was normalized by probabilistic quotient normalization (PQN) prior to pattern recognition analysis.47 Fecal DNA Extraction and Microbiological Analysis (PCR-DGGE)

Total bacterial DNA extractions were performed with 180−220 mg fecal material using QIAamp DNA Stool Mini Kit (Qiagen, Dublin, Ireland) according to the manufacturers’ instructions. All extracted DNA samples were kept at −20 °C before further analysis. The variable V3 region of 16S rRNA gene was amplified from samples by using the primers 357f_GC (5′CGC CCG CCG CGC GCG GCG GGC GGG GCG GGG GCA CGG GGG GCC TAC GGG AGG CAG CAG-3′) and 518r (5′-ATT ACC GCG GCT GCT GG-3′).48 The PCR mixtures 100 μL contained 10 μL of PCR buffer (Takara, Japan), 0.5 μL of rTaq polymerase (5 U/μL; Takara, Japan), 1 μL of primers 357f_GC and 518r, 1 μL of dNTP (10 mM; Roche) and 2 μL of template DNA (∼20 ng). To reduce the spurious PCR products, touchdown PCR was performed (Biometra, T1 Thermocycler & T gradient).49 Cycling conditions were 95 °C for 5 min, followed by 10 cycles of 95 °C denaturing for 1 min, 65 °C annealing for 1 min and 72 °C elongation for 1 min, with the annealing temperature decreased by 1 °C every other cycle until a “touchdown” at 55 °C. Then, a total of 20 cycles were performed at 95 °C for 1 min, 55 °C for 1 min and 72 °C for 1 min, and finally, 72 °C for 10 min. PCR product, at 200 base pairs in length, was confirmed by agarose gels electrophoresis, and purified by a purification kit (Omega, USA). DGGE was performed by Dcode Universal Mutation Detection System (Bio-Rad Laboratories Inc., USA). The PCR products of V3 regions were electrophoresed in 8% (w/v) polyacrylamide gels, which contained a linear gradient from 38 to 58% (100% denaturant corresponds to 7 M urea and 40% formamide). Electrophoresis was conducted under constant voltage of 30 V for 30 min and subsequently altered to voltage of 200 V for 240 min at 60 °C in 1 × Tris-acetate-EDTA (TAE) buffer. The gels were stained using silver nitrate and were photographed by a digital camera (Canon, Japan). DGGE image analysis and data output were performed by Quantity One (Version 4.6.2 Bio-Rad laboratories Inc., USA). The background noise was subtracted automatically and followed with lane recognition, which was carried out manually. The band detection was subsequently performed automatically with sensitivity setting of 8.00. All the bands in the different lanes were matched and aligned with reference sample chosen from one of the control group. The bands were digitized according to their pixel intensities and exported as binary data set with X

Multivariate Data Analysis

Multivariate data analysis was performed with SIMCA-P+ software (version 12.0 Umetrics, Sweden). Principal component analysis (PCA) was separately performed to the unit variance (UV)-scaled NMR data and DGGE data to show an overview intrinsic similarity/dissimilarity within each data set. Partial least squares (PLS) and orthogonal projection to latent structure discriminant analysis (OPLS-DA) were constructed using UV-scaled NMR data and DGGE data. PLS model was validated with a permutation test (200 permutations). OPLSDA was validated by both a 7-fold cross-validation and ANOVA of the cross-validated residuals (CV-ANOVA) methods.51 The models were interpreted by the correlation coefficient coded loading plots. The corresponding loadings were back-transformed in Excel52 (Microsoft, USA) and plotted with the colorcoded absolute value of coefficients (|r|) of the variables in MATLAB (The Mathworks Inc.; Natwick, MA, version 7.1). In this study, the cutoff value |r| was based on the discrimination significance (p < 0.05), which was determined according to the test for significance of the Pearson’s product-moment correlation coefficient. Dynamic changes in the metabolites of fecal extracts were calculated relative to the levels of control group at each time point.

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RESULTS

Drug Metabolites

Both sulfate gentamicin and ceftriaxone sodium are soluble in water but poorly absorbed in the intestine.53,54 Therefore, signals derived from sulfate gentamicin (1.37(s), 2.76(s), 2.92(s), 3.50(m), 3.82(m), 4.05(d), 4.26(dd), 5.18(bs)) and ceftriaxone sodium (3.48(d), 3.61(s), 3.75(d), 4.00(s), 4.08(d), 4.39(d), 5.21(d), 5.78(d), 7.00(s)) are detected in the fecal extracts collected on days 2 and 4. As these signals compromise the investigation of antibiotics-induced metabolic changes, C

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Figure 1. Typical 600 MHz 1H NMR spectra of fecal extracts from untreated mice (a), low dose of sulfate gentamicin-treated mice (b), high dose of sulfate gentamicin-treated mice (c), ceftriaxone sodium-treated mice (d), and the antibiotic combination-treated mice (e), at day 6 post treatment (f) the expansion of δ 0.65-0.76; (g) the expansion of δ 4.49-4.68 and δ 5.16-5.34. Key: 1. 5-aminovalerate; 2. 2-(4-hydroxyphenyl)propionate; 3. acetate; 4. hypoxanthine; 5. alanine; 6. asparagine; 7. aspartate; 8. cellobiose; 9. choline; 10. creatine; 11. creatinine; 12. dimethylamine; 13. D-pinitol; 14. formate; 15. fucose; 16. fumarate; 17. glutamine; 18. glycine; 19. histidine; 20. isoleucine; 21. lactate; 22. lactose; 23. L-arginine; 24. leucine; 25. Llysine; 26. L-proline; 27. methionine; 28. N-acetyl-glycoprotein; 29. n-butyrate; 30. phenylalanine; 31. propionate; 32. raffinose; 33. stachyose; 34. succinate; 35. sucrose; 36. taurine; 37. threonine; 38. trimethylamine; 39. tyrosine; 40. uracil; 41. uridine; 42. urocanate; 43. valine; 44. α-arabinose; 45. α-galactose; 46. α-glucose; 47. α-hydroxyisobutyrate; 48. α-ketoisovalerate; 49. α-xylose; 50. β-arabinose; 51. β-galactose; 52. β-glucose; 53. βxylose; 54. 4-hydroxyphenylacetate; 55. U1; 56. U2; 57. U3; 58. tauro-β-muricholic acid (TβMCA); 59.taurocholic acid (TCA); 60 cholic acid (CA); 61. deoxycholic acid; 62. U4; 63. xanthine. *: putative assignment.

Pairwise comparisons are performed between fecal extract spectra obtained from the CTR group and each of the antibiotic-treated group, using OPLS-DA. The R2 and Q2 values for all the sublevel mathematical models are summarized in Table S1 (Supporting Information). One PLS component and one orthogonal component are calculated for all of the models. NMR spectral data was used as the X matrix and classification information as the dummy Y matrix. Both permutation tests and CV-ANOVA are applied to validate the models (Table S2, Supporting Information). Models comparing the GL group with the CTR groups are valid from days 5 to 11 (permutation test passed and the value of CV-ANOVA < 0.05). Models comparing the CEF group with the CTR groups are valid from days 5 to 14, whereas models comparing the GH group with the CTR group are valid for all the time points. The same observation is made for the COM group except for day 27. Nevertheless, some models constructed for the later time points are significant. For example, the GL group shows significant changes on day 23, and models based on CEF vs CTR are significant on day 20. Cross-validated scores plots demonstrate clear separation between CTR and antibiotic-treated groups on day 6 of posttreatment, and a range of prominent metabolic alterations are shown in the corresponding coefficient plots (Figure 2). Metabolites with correlation coefficients higher than 0.602 for the model of GL vs CTR, and 0.553 for the remaining three models, are regarded significantly discriminant on the basis of a

NMR fecal spectral data at these two time points are excluded from further analysis. Immediate Antibiotic-Induced Effects on Fecal Metabolite Profiles

Metabolites are assigned on the basis of previously published work8,55−58 and confirmed with 2D JRES, TOCSY, COSY, HSQC and HMBC spectra (Figure 1). Metabolic profiles of mouse fecal extracts consist of SCFAs, amino acids (AAs), oligosaccharides, phenolic acids, bile acids and other organic acids. These exhibit clear systematic differences between the groups. Higher levels of oligosaccharides are observed in antibiotic-treated groups, together with lower levels of phenolic acids, SCFAs, uracil, and hypoxanthine compared with the CTR group. Dynamic Metabolic Changes in Fecal Samples after Antibiotic Treatment

PCA analysis is performed on all NMR data of fecal extracts collected from the 11 time points (days 0, 5, 6, 7, 9, 11, 14, 17, 20, 23 and 27). The PCA trajectory plot (Figure S1, Supporting Information) shows that fecal metabolic changes undergo a significant shift from day 0 to day 5, continue to drift away from day 5 to day 6, then shift back from day 7 to day 11, and gradually move to the CTR group from day 14 to the end time point (day 27). GH, CEF and COM groups share a similar recovery trajectory, whereas GL group displays a smaller deviation and a quicker recovery. D

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Figure 2. OPLS-DA scores plots (left) and coefficient plots (right) from the 1H NMR spectra of fecal extracts indicating the discrimination between untreated (black) and low dose of sulfate gentamicin-treated mice (red, a), high dose of sulfate gentamicin-treated mice (blue, b), ceftriaxone sodium-treated mice (purple, c), and the antibiotic combination-treated mice (yellow, d) at day 6. Abbreviations: 5-av, 5-aminovalerate; 4-hpaa, 4hydroxyphenylacetate; 4-hppa, 2-(4-hydroxyphenyl)propionate; ace, acetate; ala, alanine; ara, arabinose; asn, asparagine; asp, aspartate; but, nbutyrate; cel, cellobiose*; cho, choline; cre, creatine; crt, creatinine; pin, D-pinitol; for, formate; fuc, fucose; fum, fumarate; glc, glucose; gln, glutamine; gly, glycine; his, histidine; hx, hypoxanthine; lac, lactate; lat, lactose*; arg, arginine; lys, lysine; pro, proline; met, methionine; NAG, Nacetyl-glycoproteins; phe, phenylalanine; prop, propionate; raf, raffinose; sta, stachyose; suc, succinate; sucr, sucrose; thr, threonine; tyr, tyrosine; ura, uracil; uri, uridine; uro, urocanate*; xyl, xylose; α-hib, α-hydroxyisobutyrate; α-kiv, α-ketoisovalerate; TβMCA, tauro-β-muricholic acid; TCA, taurocholic acid; CA, cholic acid; DCA, deoxycholic acid; bcaa, branch chain amino acid (leucine, isoleucine, valine).

95% confidence. The common features observed in all comparative models include higher levels of D-pinitol and secondary bile acid (deoxycholic acid (DCA)) and lower levels of SCFAs, 2-(4-hydroxyphenyl)propionate (4-HPPA), 4hydroxyphenylacetate (4-HPAA), methionine, hydroxyisobutyrate, creatine, creatinine, hypoxanthine, uracil and primary bile acids (e.g., cholic acid (CA); taurocholic acid (TCA) and tauroβ-muricholic acid (TβMCA)). Notably, D-pinitol is not present in the control group. GH, CEF and COM groups manifest similar metabolic perturbations such as increased concentrations of oligosaccharides, asparagine and N-acetyl-glycoprotein (NAG), and decreased levels of branched-chain amino acids (BCAAs), phenylalnine, glucose, tyrosine, glutamine, lysine and aspartate. In contrast to the other groups, aspartate and phenylalnine are observed to be higher in the GL group. Interestingly, choline is only found to be elevated in the GL, CEF and COM groups, whereas the GH group exhibits higher levels of formate and 5-aminovalerate. Succinate and proline levels are higher in GL but lower in CEF and COM treated animals. In order to clearly visualize the time-dependent changes, variations of metabolites in the treated groups compared to the control group are used to generate a heat map (Figure 3). The heat map shows that BCAAs and aromatic amino acids depleted immediately after the treatment, followed by an

increase in GH, CEF and COM groups compared with CTR. The levels of SCFAs, 4-HPPA, 4-HPAA, bile acids, monosaccharides, hypoxanthine, uracil, and, to a less extent, uridine exhibit down regulation over time. Disaccharides (sucrose, cellobiose), trisaccharides (raffinose) and tetrasaccharides (stachyose), D-pinitol, choline and glycine are higher in the treated groups than in the control group at the early time points (