Dietary Modulation of Gut Functional Ecology Studied by Fecal Metabonomics Francois-Pierre J. Martin,*,†,‡ Norbert Sprenger,† Ivan Montoliu,† Serge Rezzi,† Sunil Kochhar,† and Jeremy K. Nicholson*,‡ Nestle´ Research Center, P.O. Box 44, Vers-chez-les-Blanc, CH-1000 Lausanne 26, Switzerland, and Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ, United Kingdom Received June 4, 2010
A major source of intestinal metabolites results from both host and microbial processing of dietary nutrients. 1H NMR-based metabolic profiling of mouse feces was carried out over time in different microbiome mouse models, including conventional (n ) 9), conventionalized (n ) 10), and “humanized” gnotobiotic mice inoculated with a model of human baby microbiota (HBM, n ) 17). HBM mice were supplemented with Lactobacillus paracasei with (n ) 10) and without (n ) 7) prebiotics. Animals not supplemented with prebiotics received a diet enriched in glucose and lactose as placebo. In conventionalized animals, microbial populations and activities converged in term of multivariate mapping toward conventional mice. Both groups decreased bacterial processing of dietary proteins when switching to a diet enriched in glucose and lactose, as described with low levels of 5-aminovalerate, acetate, and propionate and high levels of lysine and arginine. The HBM model differs from conventional and conventionalized microbiota in terms of type, proportion, and metabolic activity of gut bacteria (lower short chain fatty acids (SCFAs), lactate, 5-aminovalerate, and oligosaccharides, higher bile acids and choline). The probiotics supplementation of HBM mice was associated with a specific amino acid pattern that can be linked to L. paracasei proteolytic activities. The combination of L. paracasei with the galactosyl-oligosaccharide prebiotics was related to the enhanced growth of bifidobacteria and lactobacilli, and a specific metabolism of carbohydrates, proteins, and SCFAs. The present study describes how the assessment of metabolic changes in feces may provide information for studying nutrient-microbiota relationships in different microbiome mouse models. Keywords: 1H nuclear magnetic resonance spectroscopy • chemometrics • feces • human microbiota • Lactobacillus paracasei • metabonomics • metabolomics
Introduction The complex association of gut bacterial cells collectively extends the processing of undigested food to the benefit of the host through metabolic capacities not encoded in mammalian genomes.1,2 Recent studies have provided compelling evidence that gut microbiota exerts deep and long-range control over multiple host cell metabolic pathways, impacting on health and nutritional outcomes.3-6 In mammals, the gastrointestinal tract contains a wide array of microbial communities that mainly reside in the large intestine, and the cecum for rodents, where they represent up to 60% of the total fecal mass.2,7 In particular, the colon is one of the most populated sections of the gastrointestinal tract, with high density of bacterial cells of up to 1013 per mL.8 The members of the gut microbiota consortium * To whom correspondence should be addressed. Dr. F.-P. Martin (
[email protected]), Tel: + 41 (0) 21 785 8771. Fax: +41 (0) 21 785 9486. Prof. J. Nicholson (
[email protected]), Tel: +44 (0)20 7594 3195. Fax: +44 (0) 20 7594 3226. † Nestle´ Research Center. ‡ Imperial College London.
5284 Journal of Proteome Research 2010, 9, 5284–5295 Published on Web 08/31/2010
are diverse and provide the host with unique capacities for dietary energy recovery, generating digestible carbohydrates, short chain fatty acids (SCFAs), amino acids and vitamins, and protection against infectious diseases.9 The main human intestinal bacteria, which are predominantly species of the Bacteroides, Clostridium, Lactobacillus, Eubacterium, Faecalibacterium and Bifidobacterium groups, coexist in a dynamic ecological equilibrium together with various yeasts and other microorganisms.10 Moreover, the complex microbial community varies with the host’s age, diet, and health status, and differs in composition from the stomach to the colon, where the competition for space and nutrients in the large bowel contributes to the microbial composition of this internal ecosystem. In particular, through the production of antimicrobial compounds, volatile fatty acids, and chemically modified bile acids, gut bacteria create a local environment that is generally unfavorable for growth of many enteric pathogens.11,12 At present, metabolic insights into gut microbiota metabolism remain limited due to the inaccessibility of the intestinal habitat and gut microbiota complexity.8 Previous investigations 10.1021/pr100554m
2010 American Chemical Society
Gut Functional Ecology Studied by Fecal Metabonomics of gut microbial populations and metabolism aimed at monitoring specific end-products of bacterial metabolism, such as amino acids, bile acids, or SCFAs.13-15 The measurement of the gut microbial populations and metabolism is generally confined to fecal samples, which are generally limited due to the elevated colonic absorption of bacterial metabolites.16 However, such measurable outcomes provide some essential insightssyet limitedsinto a restrained range of microbial species and activities within the colon. An increasing interest for the potential of gut microbiota to influence human health has led to investigations deciphering the depth of the relationship between the gut microbiota and nutrients and their impact on the host metabolism. Systems biology approaches may provide insights into the role of mammalian gut microbial metabolic interactions in individual susceptibility to health and disease outcomes. Metabonomics generates multivariate information on a wide range of molecules in various biological matrices while retaining the ability to extract specific biochemical information.17 Over the past decades, the practice of metabonomics has evolved from diagnosis and identification of biomarkers for a medical condition toward deciphering the metabolic predisposition and response to different individual dietary modulations.18-20 Recent studies have shown that 1H NMR-based metabolic analysis of fecal extracts can provide insights into interspecies metabolic differences,21 metabolic response to nutritional intervention22 and carry diagnostic information for diseases, including Crohn’s disease and ulcerative colitis.23 Combination of metabonomic analysis of urine and feces were also employed to assess the relative contribution of the gut microbiome to host metabolism using antibiotic-induced gut microbial modulation.24 In addition, we have recently described the metabolic effects of inoculating germfree mice with a nonadapted microbiota (human-derived bacteria) or a conventional microbiota at system and compartment levels.25,26 Here, we have applied a 1H NMR-based metabonomic approach to further our knowledge about gut microbial metabolism by assessing timedependent fecal metabolic changes in different microbiome mouse models and in response to nutritional interventions. We monitored the fecal metabolic changes at different time points during microbial establishment collected from four groups of animals, including conventional and conventionalized mice, gnotobiotic animals inoculated with a model of human baby microbiota (HBM) and supplemented with Lactobacillus paracasei with and without prebiotics. Here, we discuss the variation of fecal metabolome in relation to (i) the presence of a nonadapted microbiota (HBM) and the previously reported host metabolic changes25,26 and (ii) dietary-specific modulation of microbial populations.
Materials and Methods Gut Microbial Model. The model of human baby microbiota (HBM) is comprised of a total of 7 bacterial strains, isolated from stool of a 20 day old female baby who was naturally delivered and breast-fed, namely Escherichia coli, Bifidobacterium breve and Bifidobacterium longum, Staphylococcus epidermidis and Staphylococcus aureus, Clostridium perfringens, and Bacteroides distasonis. Bacterial cell mixtures contain approximately 1010 cells/mL for each strain and were kept in frozen aliquots until inoculation. Lactobacillus paracasei NCC2461 probiotic was obtained from the Nestle´ Culture Collection (Lausanne, Switzerland), grown in Man, Rogosa and Sharpe (MRS; Chemie Brunschwig, Switzerland) medium,
research articles concentrated and resuspended in fresh MRS to 4 × 109 colonies forming unit (CFU)/mL. Aliquots of 1 mL were frozen and each day a fresh defrosted aliquot was introduced in the isolator and mixed with the saline drinking water. Animal Handling Procedure. All animal studies were carried out under appropriate national guidelines at the Nestle´ Research Center (Lausanne, Switzerland) and were part of a larger study previously published.26 A total of 36 C3H female mice (9 conventional and 27 germ-free animals, Charles River, France), aged 6 weeks, were fed with a standard semisynthetic germfree rodent diet (Provimi Kliba AG, Switzerland).27 The group of conventional mice was kept as control (n ) 9). One group of germ-free mice was conventionalized by removal from their germfree-free isolator and exposure to normal environment for a period of 4 weeks (n ) 10). A second group of germ-free mice received a single dose of HBM by oral gavage (n ) 17). At 8 weeks of age, HBM mice were given L. paracasei probiotic bacteria in MRS culture medium (108 CFU/mL) daily for a period of 2 weeks and were separated into two groups. Conventional and conventionalized animals received a saline drink ad libitum containing MRS culture medium as placebo for probiotic intervention. From 8 to 10 weeks of age, conventional, conventionalized and one group of HBM mice supplemented with probiotics (n ) 7, defined as probiotic treatment) were fed with a basal mix diet containing 2.5% of glucoselactose mix (1.25% each). A second group of HBM mice supplemented with probiotics was fed with a diet containing 3 g per 100 g diet of an in-house preparation of galactooligosaccharides for a period of 2 weeks (n ) 10, defined as synbiotic treatment). The in-house preparation of prebiotics contains 80% of commercially available galactosyl-oligosaccharides (VivinalGOS, Borculo Domo Ingredients, Netherlands)28 and 20% of a proprietary mixture containing other galactosyl-oligosaccharide structures.19 The control diet was supplemented with lactose and glucose to control for the lactose and glucose that were brought into the experimental diets by the used galactosyloligosaccharide preparations. Sample Collection. Two fecal pellets were collected from the animals at the age of 8, 9, and 10 weeks (time point (TP) 0, 1 and 2) at the same time in the morning. Microbial analysis was immediately carried out on fresh feces as described previously,29 while a second stool sample was preserved at -80 °C prior to NMR spectroscopic analysis. At time point 0, fecal samples were collected prior to changes of diet and nutritional intervention. At time point 2, stool samples were collected before removal of the animals from their cages and euthanasia. Microbial Profiling of Fecal Contents. Microbiological tests were carried out on the fecal pellets as described previously.29 Briefly, for each mouse, a fresh fecal pellet was homogenized individually in 0.5 mL Ringer solution (Oxoid, U.K.) supplemented with 0.05% (w/v) L-Cystein (HCl) and different dilutions of the bacterial solution were plated on selective and semiselective media for the enumeration of specific micro-organisms: Bifidobacteria on Eugon Tomato medium (Chemie Brunschwig, Switzerland), Lactobacillus on MRS (Chemie Brunschwig, Switzerland) with antibiotics (phosphomycin, sulfamethoxazole and trimethoprim) (Sigma, Switzerland), C. perfringens on NN-agar medium (Chemie Brunschwig, Switzerland), Enterobacteriaceae on Drigalski medium (BioRad, Switzerland), and Bacteroides on Shaedler Neo Vanco medium (BioMe´rieux, France). The bacterial cultures of Enterobacteriaceae were incubated at 37 °C under aerobic conditions for 24 h and those of BifidobacJournal of Proteome Research • Vol. 9, No. 10, 2010 5285
research articles teria, Lactobacillus, Bacteroides and Clostridium under anaerobic conditions for 48 h. The fecal data obtained from the conventional, conventionalized and HBM + L. paracasei mice at TP2 have previously been published.26 Sample Preparation and 1H NMR Spectroscopic Analysis. Fecal pellets were homogenized in 650 µL of a phosphate buffer solution containing 90:10 D2O/H2O (v/v) as a field frequency lock and sodium 3-(trimethylsilyl)propionate-2,2,3,3-d4 (TSP) as a chemical shift reference. The homogenates were sonicated at ambient temperature (298 K) for 30 min to destroy bacterial cells and then centrifuged at 6000× g for 20 min. The supernatants were removed and centrifuged at 6000× g for 10 min. Aliquots of 550 µL were then pipetted into 5 mm NMR tubes. All 1H NMR spectra were recorded on a Bruker DRX 600 NMR spectrometer (Bruker Biospin, Rheinstetten, Germany) operating at 600.11 MHz for 1H NMR observation and equipped with a Bruker 5 mm TXI triple resonance probe at 298 K. A standard NMR spectrum was acquired with a standard onedimensional pulse sequence with water suppression using a relaxation delay of 2 s and a mixing time of 100 ms. Standard one-dimensional NMR spectra provide a general representation of the total biochemical composition of the fecal extracts. A total of 128 transients were collected into 32000 data points for each spectrum with a spectral width of 20 ppm. Free induction decays were multiplied by an exponential function equivalent to a 0.3 Hz line-broadening factor prior to Fourier transformation. 1H NMR spectra were manually phased and baseline-corrected by using the software package Topspin 2.0 (Bruker Biospin, Rheinstetten, Germany). The 1H NMR spectra were referenced to the methyl resonance of TSP at δ 0.0. The assignment of the peaks to specific metabolites was achieved using an internal library of compounds, the literature,4,19,21,30 and confirmed by standard two-dimensional (2D) 1 H-1H correlation spectroscopy (COSY),31 total correlation spectroscopy (TOCSY),32 and 1H-13C heteronuclear single quantum correlation (HSQC),33 on selected samples. Multivariate Statistical Data Analysis. The spectra were converted into 22000 data points over the range of δ 0.2-10.0 using an in-house developed MATLAB (The MathWorks Inc., Natick, MA) routine excluding the water residue signal between δ 4.50-5.19. The spectra were normalized to a constant total sum of all intensities within the specified range prior to multivariate data analyses. Chemometric analysis was performed using the SIMCA-P+ (version 12, Umetrics AB, Umeå, Sweden) software package and in-house developed MATLAB (The MathWorks Inc.) routines on unit-variance scaled NMR variables (i.e., each variable divided by its standard deviation) and mean centered, and log transformed microbial data. Initial data analyses were conducted using Principal Component Analysis (PCA),34 in order to assess metabolic/microbial similarities between samples. Data were visualized by means of principal component scores, where each point represents an individual metabolic/microbial profile. NMR and microbial variables, for example, metabolic concentrations or colony forming unit (CFU), responsible for the differences between samples in the scores plot can be extracted from the corresponding loadings plot, where each coordinate represents a single NMR/microbial signal. In addition, Projection to Latent Structure (PLS)35 and Orthogonal Projection to Latent Structure (O-PLS)36 methods were applied to maximize the discrimination between sample groups focusing on differences according to group and timedependent metabolic variations. O-PLS-DA provides a way to 5286
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Martin et al. filter out metabolic/microbial information that is not correlated to the predefined classes. Influential variables that are therefore correlated to the group separation are identified using the variable coefficients according to a previously published method.37 The weight of a variable in the discrimination is given by the square of its correlation coefficient (r2), which is color coded from zero in blue to high values (0.7-0.9) in red. To test the validity of the model against overfitting, the crossvalidation parameter (Q2Y) and the goodness of fit (R2X) were computed and the 7-fold cross validation method was used. Microbial counts in the feces were also analyzed using a twotailed Mann-Whitney U test.
Results Nutritional and Temporal Changes of the Fecal Microbial Composition. Microbiological tests were carried out on fecal samples to assess the stage of gut bacterial development at three time points (TP 0, 1 and 2) in conventional conventionalized, and HBM supplemented mice. The fecal data obtained from the conventional, conventionalized and HBM + L. paracasei mice at TP 2 have previously been published.26 Data are presented as mean ( SEM of log10 of CFU given per gram of wet weight of feces obtained from the different groups in Table 1 and graphically displayed in Figure 1. To understand the global nutritional and time-related changes of fecal microbial composition, PCA was performed on mean centered data (log10 of CFU). A total of two principal components was calculated, PCs 1 and 2 explain 46 and 30% of the total variance, respectively. The scores along PC1 and PC2 illustrate the variations in the microbial space overtime and the influence of nutritional interventions (Figure 2A, B). For clarity, two-dimensional representation of the scores mean trajectory is given in Figure 2C. Here each coordinate represents an average of scores for all animals in a group at a particular time point, and the bars denote the standard deviation of the scores. Fecal Microbiota Remains Relatively Stable Overtime in Conventional and Conventionalized Mice. The metabolic trajectory summarized the overall stability of the microbiota in conventional and conventionalized animals, even if samples at TP 2 tended to separate from others, a change driven by increased levels of Staphylococcus spp. However, at TP 0, conventionalized animals had less marked changes in lactobacilli, which was attenuated at TP 1 and normalized at TP 2, whereas enterobacteria and bacteroides were higher after two weeks (Table 1). These changes may be associated with switch in the diet (increased content in glucose and lactose) and more relatively to age-associated microbial establishment. Characterization and Dietary-Specific Modulation of Fecal Bacteria in HBM Mice. The conventional and conventionalized mice formed a distinct cluster from mice inoculated with a nonadapted microbiota (HBM) along PC1, as noted with different bacterial composition and proportion. HBM mice showed higher numbers of bacteria from Staphylococcus spp., enterobacteria group, and Clostridium perfringens along PC1, the latter being not detected in conventional and conventionalized animals. HBM mice showed a clear segregation along PC2 describing the effects of probiotic and synbiotic intervention on populations of bifidobacteria and lactobacilli. At TP 0, HBM mice did not contain any Lactobacillus species. After one and two weeks of daily probiotic supplementation, lactobacilli established quickly and their number was similar
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Gut Functional Ecology Studied by Fecal Metabonomics a
Table 1. Temporal Changes in Microbial Species Counts in Mouse Feces groups/log10 CFU
Conventional (n ) 10)
Conventionalized (n ) 10)
HBM + L. paracasei (n ) 7)
HBM + synbiotic (n ) 10)
time in weeks
enterobacteriae
Staphylococcus
Clostridium
Bifidobacteria
Bacteroides
Lactobacillus
0 1 2 0 1 2 0 1 2 0 1 2
5.3 ( 0.3 5.7 ( 0.8 6.5 ( 1.0 7.0 ( 1.8 6.1 ( 1.3 7.8 ( 0.7b 9.1 ( 0.2b 8.5 ( 0.7b 9.1 ( 0.4b 8.6 ( 0.5b 8.9 ( 0.3b 8.8 ( 0.3b
3.8 ( 0.8 3.4 ( 0.5 4.8 ( 0.7c,d 3.4 ( 0.7 3.9 ( 1.4 4.7 ( 1.0d 6.9 ( 0.4b 6.9 ( 0.4b 5.8 ( 0.4b,c,d 6.6 ( 0.5b 6.6 ( 0.3b 6.0 ( 0.3b,d
