A top‐down systems biology view of microbiome‐mammalian metabolic interactions in a mouse model
2007; Springer Nature; Volume: 3; Issue: 1 Linguagem: Inglês
10.1038/msb4100153
ISSN1744-4292
AutoresFrançois‐Pierre Martin, Marc‐Emmanuel Dumas, Yulan Wang, Cristina Legido‐Quigley, Ivan Kok Seng Yap, Huiru Tang, Séverine Zirah, Gerard M. Murphy, Olivier Cloarec, John C. Lindon, Norbert Sprenger, Laurent B. Fay, Sunil Kochhar, Peter van Bladeren, Elaine Holmes, Jeremy K. Nicholson,
Tópico(s)Diet and metabolism studies
ResumoArticle22 May 2007Open Access A top-down systems biology view of microbiome-mammalian metabolic interactions in a mouse model François-Pierre J Martin François-Pierre J Martin Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Nestlé Research Center, Vers-chez-les-Blanc, Lausanne, Switzerland Search for more papers by this author Marc-Emmanuel Dumas Marc-Emmanuel Dumas Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author Yulan Wang Yulan Wang Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author Cristina Legido-Quigley Cristina Legido-Quigley Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author Ivan K S Yap Ivan K S Yap Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author Huiru Tang Huiru Tang Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UKPresent address: 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, PR China Search for more papers by this author Séverine Zirah Séverine Zirah Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UKPresent address: Regulations, Development and Molecular Diversity, National Museum of Natural History, Paris 75005, France Search for more papers by this author Gerard M Murphy Gerard M Murphy Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author Olivier Cloarec Olivier Cloarec Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author John C Lindon John C Lindon Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author Norbert Sprenger Norbert Sprenger Nestlé Research Center, Vers-chez-les-Blanc, Lausanne, Switzerland Search for more papers by this author Laurent B Fay Laurent B Fay Nestlé Research Center, Vers-chez-les-Blanc, Lausanne, Switzerland Search for more papers by this author Sunil Kochhar Sunil Kochhar Nestlé Research Center, Vers-chez-les-Blanc, Lausanne, Switzerland Search for more papers by this author Peter van Bladeren Peter van Bladeren Nestlé Research Center, Vers-chez-les-Blanc, Lausanne, Switzerland Search for more papers by this author Elaine Holmes Elaine Holmes Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author Jeremy K Nicholson Corresponding Author Jeremy K Nicholson Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author François-Pierre J Martin François-Pierre J Martin Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Nestlé Research Center, Vers-chez-les-Blanc, Lausanne, Switzerland Search for more papers by this author Marc-Emmanuel Dumas Marc-Emmanuel Dumas Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author Yulan Wang Yulan Wang Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author Cristina Legido-Quigley Cristina Legido-Quigley Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author Ivan K S Yap Ivan K S Yap Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author Huiru Tang Huiru Tang Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UKPresent address: 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, PR China Search for more papers by this author Séverine Zirah Séverine Zirah Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UKPresent address: Regulations, Development and Molecular Diversity, National Museum of Natural History, Paris 75005, France Search for more papers by this author Gerard M Murphy Gerard M Murphy Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author Olivier Cloarec Olivier Cloarec Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author John C Lindon John C Lindon Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author Norbert Sprenger Norbert Sprenger Nestlé Research Center, Vers-chez-les-Blanc, Lausanne, Switzerland Search for more papers by this author Laurent B Fay Laurent B Fay Nestlé Research Center, Vers-chez-les-Blanc, Lausanne, Switzerland Search for more papers by this author Sunil Kochhar Sunil Kochhar Nestlé Research Center, Vers-chez-les-Blanc, Lausanne, Switzerland Search for more papers by this author Peter van Bladeren Peter van Bladeren Nestlé Research Center, Vers-chez-les-Blanc, Lausanne, Switzerland Search for more papers by this author Elaine Holmes Elaine Holmes Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author Jeremy K Nicholson Corresponding Author Jeremy K Nicholson Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK Search for more papers by this author Author Information François-Pierre J Martin1,2, Marc-Emmanuel Dumas1, Yulan Wang1, Cristina Legido-Quigley1, Ivan K S Yap1, Huiru Tang1, Séverine Zirah1, Gerard M Murphy1, Olivier Cloarec1, John C Lindon1, Norbert Sprenger2, Laurent B Fay2, Sunil Kochhar2, Peter van Bladeren2, Elaine Holmes1 and Jeremy K Nicholson 1 1Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, South Kensington, London, UK 2Nestlé Research Center, Vers-chez-les-Blanc, Lausanne, Switzerland *Corresponding author. Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, Exhibition road, South Kensington, London SW7 2AZ, UK. Tel.: +44 20 7594 3195; Fax: +44 20 7594 3226; E-mail: [email protected] Molecular Systems Biology (2007)3:112https://doi.org/10.1038/msb4100153 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Figures & Info Symbiotic gut microorganisms (microbiome) interact closely with the mammalian host's metabolism and are important determinants of human health. Here, we decipher the complex metabolic effects of microbial manipulation, by comparing germfree mice colonized by a human baby flora (HBF) or a normal flora to conventional mice. We perform parallel microbiological profiling, metabolic profiling by 1H nuclear magnetic resonance of liver, plasma, urine and ileal flushes, and targeted profiling of bile acids by ultra performance liquid chromatography–mass spectrometry and short-chain fatty acids in cecum by GC-FID. Top-down multivariate analysis of metabolic profiles reveals a significant association of specific metabotypes with the resident microbiome. We derive a transgenomic graph model showing that HBF flora has a remarkably simple microbiome/metabolome correlation network, impacting directly on the host's ability to metabolize lipids: HBF mice present higher ileal concentrations of tauro-conjugated bile acids, reduced plasma levels of lipoproteins but higher hepatic triglyceride content associated with depletion of glutathione. These data indicate that the microbiome modulates absorption, storage and the energy harvest from the diet at the systems level. Synopsis The symbiotic gut microbiome acts as an extended genome (Lederberg, 2000) and has evolved to exert control on a number of important mammalian metabolic regulatory functions (Backhed et al, 2004; Eckburg et al, 2005; Holmes and Nicholson, 2005; Nicholson et al, 2005; Martin et al, 2006; Nicholson, 2006; Turnbaugh et al, 2006). Gut microbial composition is now known to vary significantly in obese animal (Turnbaugh et al, 2006) and human (Ley et al, 2006) populations, and recent studies have shown that the exact state of the gut microbial ecology and metabolic activities may be of fundamental importance in the control of calorific absorption (Xu et al, 2003; Backhed et al, 2004) and in the development of insulin resistance and non-alcoholic fatty liver disease (Dumas et al, 2006). The integrated mammalian microbial co-metabolism of the bile acid pool is a good example of the complex biochemical interactions between host and resident symbionts, as summarized in Figure 1. Bile acids are crucial for the absorption of dietary fats and lipid-soluble vitamins in the intestine (Staggers et al, 1982) and might have an essential role in regulating obesity or type II diabetes (Houten et al, 2006; Watanabe et al, 2006). Many so-called secondary bile acids (deoxycholic, lithocholic, hyodeoxycholic and ω-muricholic acids) can be regarded as examples of mammalian–microbiotal co-metabolism (Nicholson et al, 2005) and have different metabolic fates via enterohepatic recirculation (Nicholson et al, 2004). The aim of the study is to assess the effects of the induction of a non-adapted microflora in a murine model (human baby flora (HBF)) on the host metabolism by comparison with animals colonized with a natural, conventional gut microflora, the result of a long period of co-evolution. Here, we have integrated the effects of modulation of the microbiome in mouse models on the co-metabolism of the major bile acids and the general metabolic profiles of the animals. We acquired parallel metabolic profiles of plasma, urine, liver, fecal extracts and ileal flushes using nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry to characterize a broad range of metabolites across these multiple compartments and 14 bile acids, which were also specifically measured in ileal flushes. The application of high-resolution 1H NMR spectroscopy of biofluids and tissues coupled with multivariate statistical methods is a well-established tool for differential metabolic pathway profiling (Nicholson et al, 2002; Nicholson et al, 2005; Nicholson and Wilson, 1989; Wang et al, 2005). Hyphenated chromatographic-mass spectrometric methods such as ultra-performance liquid chromatography–mass spectrometry (UPLC–MS)-based profiling techniques have also been recently used effectively for functional genomic discrimination (Plumb et al, 2005; Wilson et al, 2005). We have now developed and enhanced bile acid analysis method using UPLC–MS, which gives superior chromatographic resolution and sensitivity (Wilson et al, 2005; Plumb et al, 2006). We show here a significant association of specific metabotypes obtained from urine, plasma, intact liver tissue and ileal flushes with changes of the gut microbiome in mice colonized naturally with a conventional microflora or with a defined population of microbes in a pathogen-free environment, that is, exposing germ-free mice to normal environment to be naturally re-colonized by bacteria or fed with an HBF. Such exhaustive metabolic profiling revealed that re-conventionalized mice, that is, those germ-free mice allowed to equilibrate to a conventional flora, tend to converge metabolically and ecologically towards conventional mice and a healthy physiology. We also report that the re-colonization of germ-free mice with a non-adapted microflora (HBF) modifies the physiology of the murine host towards a pre-pathologic state (compared to conventional animals) and maintains the gut tract and the liver outside a sustainable mouse ecological equilibrium. Using a transgenomic bipartite graph model highlighting functional relationships between fecal bacteria profiles and bile acid profiles, we show that HBF flora has a remarkably simple microbiome/metabolome correlation network, impacting directly on the host's ability to metabolize fats. The metabolic consequences for the host show an increase in lipid accumulation in liver (despite an apparent decrease in lipoprotein sub-fractions in blood) associated to a higher lipoperoxidation risk. Mice re-colonized with a non-adapted microflora present abnormally high levels of bile acid taurine conjugates in ileal flushes, which increase emulsification and absorption of lipids and could explain increased lipid bioavailability from the gut, the mechanisms being detailed in Figure 8. Metabolic profiling on conventional mice, compared to animals re-colonized with an HBF, showed that the gut microflora are an essential evolutionary driver towards providing more refined control mechanisms on host physiology, which ultimately determine host nutritional status and health. In this case, gut bacteria exert modulation over the host metabolism via reprocessing of signalling molecules, that is, bile acids (Houten et al, 2006; Ozcan et al, 2006; Watanabe et al, 2006). The induction of a gut microflora not adapted to the host, such as that arising because of antibiotics consumption or disruption of the immunological tolerance to gut bacteria, could similarly result in the development of host metabolic deregulations. Our results suggest that controlling the dynamics of the gut microbiome to maintain or re-establish a balanced and well-adapted microflora could help to prevent some microbial-related metabolic disorders, such as hepato-gastrointestinal diseases. Introduction The symbiotic gut microbiome exerts a strong influence on the metabolic phenotype of the mammalian host and participates in extensive microbial–mammalian co-metabolism (Dunne, 2001; Xu and Gordon, 2003; Backhed et al, 2005; Eckburg et al, 2005; Holmes and Nicholson, 2005; Nicholson et al, 2005; Gill et al, 2006; Martin et al, 2006; Nicholson, 2006; Turnbaugh et al, 2006). Gut microbial composition is now known to vary significantly in obese animal (Turnbaugh et al, 2006) and human (Ley et al, 2006) populations, and recent studies have shown that the exact state of the gut microbial ecology and metabolic activities may be of fundamental importance in the control of calorific absorption (Xu et al, 2003; Backhed et al, 2004) and in the development of insulin resistance and nonalcoholic fatty liver disease in high-fat-diet experiments (Dumas et al, 2006). We know that the symbiotic gut microbiome acts as an extended genome (Lederberg, 2000) and has evolved to exert control on a number of important mammalian metabolic regulatory functions (Dunne, 2001; Pereira and Gibson, 2002; Xu and Gordon, 2003; Xu et al, 2003; Backhed et al, 2004, 2005; Holmes and Nicholson, 2005; Nicholson et al, 2005; Dumas et al, 2006; Gill et al, 2006; Martin et al, 2006; Sonnenburg et al, 2006). Gut microbes extensively co-metabolize bile acids and these have mammalian endocrine functions (Houten et al, 2006; Watanabe et al, 2006). The integrated metabolism of the bile acid pools in mammals is a good example of the complex transgenomic biochemical interactions between host and microbiome symbionts as summarized in Figure 1. Bile acids are synthesized from cholesterol in the liver by a multi-enzyme coordinated process (Palmeira and Rolo, 2004) and are crucial for the absorption of dietary fats and lipid-soluble vitamins in the intestine (Staggers et al, 1982). Bile acids also have a role in maintaining the intestinal barrier function to prevent intestinal bacterial overgrowth and translocation (Lorenzo-Zuniga et al, 2003; Ogata et al, 2003), as well as invasion of underlying tissues by enteric bacteria (Ogata et al, 2003). In addition, abnormal bile acid profiles are indicative of various hepato-gastrointestinal diseases, such as dietary fat malabsorption and gallstone formation (Chace, 2001; Ijare et al, 2005) and might have an essential role in regulating obesity or type II diabetes (Houten et al, 2006; Watanabe et al, 2006). Many so-called secondary bile acids (deoxycholic, lithocholic, hyodeoxycholic and ω-muricholic acids) can be regarded as examples of mammalian-microbiotal co-metabolism (Nicholson et al, 2005) and have different metabolic fates via enterohepatic recirculation (Nicholson et al, 2004). This is part of what we term the microbiome–host metabolic axis, which we define here for the first time as 'the multi-way exchange and co-metabolism of compounds between the host organism and the gut microbiome resulting in transgenomically regulated secondary metabolites, which have biological activity in both host and microbial compartments'. Understanding the effects of bacterial metabolism on the balance of bile acids in enterohepatic recirculation is a major challenge owing to the implications of microbiota in fat absorption, lipid metabolism, drug therapeutic or toxic effects as well as direct effects within the gastrointestinal tract and its contents (Van et al, 2002). In this regard, recent advances in microbial and metabolic profiling now enable the multi-compartment study of bile acids and their effect on intermediary metabolism. Figure 1.Metabolism and synthesis of the major bile acids in mouse. Key: Color denotes family of bile acids, that is, primary bile acids synthesized in liver in black, secondary bile acids synthesized in liver and processed by microflora in green and tertiary bile acids synthesized in liver, processed by microflora and re-metabolized in liver through enterohepatic recirculation in orange. Conjugation with taurine and glycine is colored in blue and red respectively. Key intermediates in the pathway are colored in gray. Download figure Download PowerPoint The application of high-resolution 1H nuclear magnetic resonance (NMR) spectroscopy of biofluids and tissues coupled with multivariate statistical methods is a well-established tool for untargeted metabolic profiling (Nicholson and Wilson, 1989; Nicholson et al, 2002, 2005; Wang et al, 2005). Hyphenated chromatographic-mass spectrometric methods such as ultra-performance liquid chromatography–mass spectrometry (UPLC–MS)-based profiling techniques have also been recently used effectively for functional genomic discrimination (Plumb et al, 2005; Wilson et al, 2005). Lately, LC–MS has been shown to be a sensitive and quantitative method of analysis for identification of major bile acids in urine and plasma (Mims and Hercules, 2003, 2004). Historically, bile acid analysis has been hindered by difficulties in separating many structurally similar compounds and isomers in a highly complex matrix. We have now developed and show here for the first time an enhanced bile-acid-targeted profiling method using UPLC–MS, which gives superior chromatographic resolution and sensitivity (Wilson et al, 2005; Plumb et al, 2006) and greatly aids the study of bile acid metabolism. In the present study, the complex metabolic response of the host to perturbation of its enteric microbiota at the systems level by integration of metabolic data from many compartments was investigated. The aim of the study is to assess the effects of the induction of a nonadapted microflora in a murine model (human baby flora (HBF)) on the host metabolism by comparison with animals colonized with a natural gut microflora (conventional), the result of a long period of co-evolution. An additional group of germfree mice, naturally re-conventionalized, was used as a control to investigate the specific effects related to the re-establishment of a flora in previously germfree mice. Here, we have integrated the effects of modulation of the microbiome in mouse models on major bile acids co-metabolism through targeted metabolic profiling, and characterized the consequences of this intervention at the systems level through parallel untargeted metabolic profiling, multivariate and graph modeling. Here, we show a significant association of specific metabotypes and changes of the gut microbiome between mice colonized naturally with a conventional microflora or colonized with a defined microbial population derived from human host in a pathogen-free environment, that is, HBF. This study brings further evidence that gut microflora is adapted to each host and that gut microflora influences the host's systemic lipid metabolism and energy balance through modification of bile acid metabolism and eventually impacts on the host's health. Results Gut bacterial composition and fecal content of short-chain fatty acids Microbiological profiles were used to assess the stage of bacterial development during re-conventionalization and colonization with an HBF. The terminal composition of the fecal microbiota is detailed in Table I. Using a two-tailed Mann–Whitney test, we showed that after 32 days of natural re-conventionalization, the mice have similar microbial composition in feces as conventional animals, although their levels of Bacteroides and Enterobacteria were higher (Table I). The HBF mice had markedly different gut microbiota characterized by an important population of Clostridium and higher levels of Bacteroides and Enterobacteria as those observed in re-conventionalized mice when compared to conventional mice (Table I). Table 1. Microbial species counts in mouse feces at the end of the experiment Groups/log10 CFU Conventional (N=10) Re-conventional (N=10) HBF-L. paracasei (N=7) Lactobacilli 6.4±1.5 5.9±2.5 7.1±0.4 Enterobacteria 6.5±1.0 7.8±0.7** 9.1±0.4***a Bifidobacteria 7.0±1.2 7.8±1.4 7.5±0.7 Staphylococcus 4.8±0.7 4.7±1.0 5.8±0.4 C. perfringens <2 <2 7.0±0.9***a Bacteroides 8.3±0.4 9.1±0.6** 9.5±0.9*** Log10 of CFU (colony-forming unit) given per gram of wet weight of feces. Data are presented as mean±s.d. The average values for the re-colonized group were compared to the conventional animals: *significant difference at 95% confidence level, **significant difference at 99% confidence level, ***significant difference at 99.9% confidence level. a Significant difference at 99.9% confidence level was observed when compared to the re-conventionalized animals. The fecal content of individual animals was investigated using gas-chromatography–flame-ionization-detection (GC–FID) to identify and quantify some major bacterial short-chain fatty acids (SCFAs), namely acetate, propionate, isobutyrate, n-butyrate, n-valerate and isovalerate. Although lactate changes were expected, they were not in fact measured. The results presented in Table II are given in μmol/g of dry fecal content, and as mean±s.d. for each group of mice, and were assessed using a two-tailed Mann–Whitney test. The production of SCFAs by the gut microbiota of re-conventionalized mice was similar to that of conventional mice, except that the latter has higher levels of acetate. These SCFAs were found in lower concentrations and in different proportions in HBF mice (Table II). In particular, a higher proportion of propionate (24.5±4%) and a lower proportion of n-butyrate (4.2±3.4%) were observed in HBF mice (controls were 10.2±1.2 and 11.1±3.4%, respectively). Table 2. Short-chain fatty acid content in the cecum from the different groups SCFAs\microbiota Acetate Propionate Isobutyrate Butyrate Isovalerate Valerate Conventional 179.1±43.5 (73.7±3.5) 24.4±5.3 (10.2±1.2) 3.1±0.5 (1.3±0.2) 27.1±11.7 (11.1±4.3) 4.7±1.3 (2±0.4) 4.2±1 (1.7±0.4) Re-conventional 136.6±51.4* (67±4.2**) 26.5±6.4 (14.5±3.7***) 3.3±0.8 (1.7±0.4**) 27.5±13 (12.1±4.3) 4.6±1.2 (2.5±0.8) 4.5±1.2 (2.2±0.5*) HBF-L. paracasei 34.4±9.9*** (65.9±3.1***) 12.8±4*** (24.5±4***) 0.6±0.1*** (1.2±0.3**) 1.9±0.9*** (4.2±3.4***) 1.7±0.9*** (3.3±1.4***) 0.4±0.1*** (0.9±0.5**) Data are presented in μmol/g of dry feces and are presented as means±s.d. The relative composition in short-chain fatty acids in percentage of total content. The average values for the re-colonized group were compared to the conventional animals: *significant difference at 95% confidence level, **significant difference at 99% confidence level, ***significant difference at 99.9% confidence level. 1H NMR spectroscopy of mouse plasma, urine, liver and ileal flushes Examples of typical 1H NMR spectra of urine, 1H CPMG NMR spectra of liver and plasma from an HBF mouse, and 1H NMR spectra of ileal flushes from HBF and conventional mice are shown in Figure 2 and were assigned according to literature (Nicholson et al, 1995; Tugnoli et al, 2004; Wang et al, 2005), and confirmed by selected 1H-1H correlation spectroscopy (COSY) (Hurd, 1990) and 1H-1H total correlation spectroscopy (TOCSY) (Bax and Davis, 1985) two-dimensional (2D) NMR experiments on single sample as well as selective statistical total correlation spectroscopy (STOCSY) (Cloarec et al, 2005a) experiments on multiple samples. In particular, NMR spectroscopy detects a wide range of amino acids, metabolism intermediates, organic acids, membrane components, saturated and unsaturated triglycerides and fatty acids, ketone bodies, N-acetyl and O-acetyl-glycoproteins (Table III). Indoleacetylglycine (IAG), phenylacetylglycine (PAG), trimethylamine (TMA), trimethylamine-N-oxide (TMAO) and dimethylglycine (DMG) were also identified in urine. Using NMR, five bile acid species were assigned unambiguously in ileal flushes, that is, β-muricholic (βMCA), tauro-β-muricholic (TβMCA), cholic (CA), taurocholic (TCA) and tauroursodeoxicholic (TUDCA) acids. 1H NMR spectra of pure compound showed that the 1H NMR chemical shifts of C-18, C-19 and C-21 methyl groups were characteristic for the determination of individual bile acid levels. However, this method is of limited use for probing complex mixtures and for differentiating conjugated from unconjugated forms of bile acids in such mixtures. Figure 2.Typical 600 MHz 1H NMR spectra. 1H CPMG NMR spectra of plasma (A) and intact liver tissue (B), and 1H NMR spectrum of urine (C) from an HBF mouse, and 1H NMR spectra of ileal flushes from HBF (D) and conventional mice (E). The spectra of the urine and ileal flushes were magnified 6 and 10 times respectively in the aromatic region (δ 5.2–8.5) compared to the aliphatic region (δ 0.7–4.5). The liver regions δ 5.2–5.4 and 5.4–8.5 were magnified 4 and 8 times respectively. The plasma regions δ 5.2–5.4 and 5.4–8.5 were magnified 4 and 10 times respectively. Keys to the figures are given in Table III. Download figure Download PowerPoint Table 3. Table of assignment of the metabolites in plasma, liver, urine and ileal flushes Key Metabolites Moieties δ 1H (p.p.m.) and multiplicity 1 Isoleucine αCH, βCH, γCH3, δCH3 3.65(d), 1.95(m), 0.99(t), 1.02(d) 2 Leucine αCH, βCH2, δCH3, δCH3 3.72(t), 1.96(m), 0.91(d), 0.94(d) 3 Valine αCH, βCH, γCH3 3.6(d), 2.26(m), 0.98(d), 1.04(d) 4 Ethanol CH2, βCH3 3.65(q), 1.18(t) 5 3-D-hydroxybutyrate CH, CH2, γCH3, CH2 4.16(dt), 2.41(dd), 1.20(d), 2.3
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