Probiotic modulation of symbiotic gut microbial–host metabolic interactions in a humanized microbiome mouse model
2008; Springer Nature; Volume: 4; Issue: 1 Linguagem: Inglês
10.1038/msb4100190
ISSN1744-4292
AutoresFrançois‐Pierre Martin, Yulan Wang, Norbert Sprenger, Ivan Kok Seng Yap, Torbjörn Lundstedt, Per M. Lek, Serge Rezzi, Ziad Ramadan, Peter van Bladeren, Laurent B. Fay, Sunil Kochhar, John C. Lindon, Elaine Holmes, Jeremy K. Nicholson,
Tópico(s)Neuroendocrine regulation and behavior
ResumoArticle15 January 2008Open Access Probiotic modulation of symbiotic gut microbial–host metabolic interactions in a humanized microbiome mouse model Francois-Pierre J Martin Francois-Pierre J Martin Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, London, UK Nestlé Research Center, Lausanne, Switzerland 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, London, UK Search for more papers by this author Norbert Sprenger Norbert Sprenger Nestlé Research Center, Lausanne, Switzerland 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, London, UK Search for more papers by this author Torbjörn Lundstedt Torbjörn Lundstedt AcurePharmaAB, Uppsala, Sweden Department of Medicinal Chemistry, BMC, Uppsala University, Uppsala, Sweden Search for more papers by this author Per Lek Per Lek AcurePharmaAB, Uppsala, Sweden Search for more papers by this author Serge Rezzi Serge Rezzi Nestlé Research Center, Lausanne, Switzerland Search for more papers by this author Ziad Ramadan Ziad Ramadan Nestlé Research Center, Lausanne, Switzerland Search for more papers by this author Peter van Bladeren Peter van Bladeren Nestlé Research Center, Lausanne, Switzerland Search for more papers by this author Laurent B Fay Laurent B Fay Nestlé Research Center, Lausanne, Switzerland Search for more papers by this author Sunil Kochhar Sunil Kochhar Nestlé Research Center, Lausanne, Switzerland 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, London, UK 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, 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, London, UK Search for more papers by this author Francois-Pierre J Martin Francois-Pierre J Martin Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, London, UK Nestlé Research Center, Lausanne, Switzerland 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, London, UK Search for more papers by this author Norbert Sprenger Norbert Sprenger Nestlé Research Center, Lausanne, Switzerland 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, London, UK Search for more papers by this author Torbjörn Lundstedt Torbjörn Lundstedt AcurePharmaAB, Uppsala, Sweden Department of Medicinal Chemistry, BMC, Uppsala University, Uppsala, Sweden Search for more papers by this author Per Lek Per Lek AcurePharmaAB, Uppsala, Sweden Search for more papers by this author Serge Rezzi Serge Rezzi Nestlé Research Center, Lausanne, Switzerland Search for more papers by this author Ziad Ramadan Ziad Ramadan Nestlé Research Center, Lausanne, Switzerland Search for more papers by this author Peter van Bladeren Peter van Bladeren Nestlé Research Center, Lausanne, Switzerland Search for more papers by this author Laurent B Fay Laurent B Fay Nestlé Research Center, Lausanne, Switzerland Search for more papers by this author Sunil Kochhar Sunil Kochhar Nestlé Research Center, Lausanne, Switzerland 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, London, UK 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, 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, London, UK Search for more papers by this author Author Information Francois-Pierre J Martin1,2, Yulan Wang1, Norbert Sprenger2, Ivan K S Yap1, Torbjörn Lundstedt3,4, Per Lek3, Serge Rezzi2, Ziad Ramadan2, Peter van Bladeren2, Laurent B Fay2, Sunil Kochhar2, John C Lindon1, 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, London, UK 2Nestlé Research Center, Lausanne, Switzerland 3AcurePharmaAB, Uppsala, Sweden 4Department of Medicinal Chemistry, BMC, Uppsala University, Uppsala, Sweden *Corresponding author. Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington Campus, London SW7 2AZ, UK. Tel.: +44 20 7594 3195; Fax: +44 20 7594 3226; E-mail: [email protected] Molecular Systems Biology (2008)4:157https://doi.org/10.1038/msb4100190 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Figures & Info The transgenomic metabolic effects of exposure to either Lactobacillus paracasei or Lactobacillus rhamnosus probiotics have been measured and mapped in humanized extended genome mice (germ-free mice colonized with human baby flora). Statistical analysis of the compartmental fluctuations in diverse metabolic compartments, including biofluids, tissue and cecal short-chain fatty acids (SCFAs) in relation to microbial population modulation generated a novel top-down systems biology view of the host response to probiotic intervention. Probiotic exposure exerted microbiome modification and resulted in altered hepatic lipid metabolism coupled with lowered plasma lipoprotein levels and apparent stimulated glycolysis. Probiotic treatments also altered a diverse range of pathways outcomes, including amino-acid metabolism, methylamines and SCFAs. The novel application of hierarchical-principal component analysis allowed visualization of multicompartmental transgenomic metabolic interactions that could also be resolved at the compartment and pathway level. These integrated system investigations demonstrate the potential of metabolic profiling as a top-down systems biology driver for investigating the mechanistic basis of probiotic action and the therapeutic surveillance of the gut microbial activity related to dietary supplementation of probiotics. Synopsis The gut microbiome–mammalian 'Superorganism' (Lederberg, 2000) represents the highest level of biological evolutionary development in which there is extensive 'transgenomic' modulation of metabolism and physiology, which is a characteristic of true symbiosis. By definition, superorganisms contain multiple cell types and the coevolved interacting genomes can only be effectively studied as an in vivo unit in situ using top-down systems biology approaches (Nicholson, 2006; Martin et al, 2007a). Interest in the impact of gut microbial activity on human health is expanding rapidly and many mammalian–microbial associations, both positive and negative, have been reported (Dunne, 2001; Verdu et al, 2004; Nicholson et al, 2005; Gill et al, 2006; Ley et al, 2006). As the microbiome interacts strongly with the host to determine the metabolic phenotype (Holmes and Nicholson, 2005; Gavaghan McKee et al, 2006) and metabolic phenotype influences outcomes of drug interventions (Nicholson et al, 2004; Clayton et al, 2006) there is clearly an important role of understanding these interactions as part of personalized healthcare solutions (Nicholson, 2006). Probiotics, most commonly Lactobacillus and Bifidobacteria, is one of the current approaches used to modulate the balance of the intestinal microflora in a beneficial way (Collins and Gibson, 1999). However, the functional effects of probiotic interventions cannot be fully assessed without probing the biochemistry of the host at multiple compartmental levels and we propose that top-down systems biology provides an ideal approach to further understanding in this field. The microbiota observed in human baby flora mice has a number of similarities with that found in formula-fed neonates (Mackie et al, 1999) that makes it be a well-adapted and simplified model to assess probiotics impact on gut microbial functional ecosystem (in particular on metabolism of Bifidobacteria and potential pathogens) and subsequent effects on host metabolism. Metabolic profiling using high-density data generating spectroscopic techniques, in combination with multivariate mathematical modelling is a tool that is well suited to generating metabolic profiles that encapsulate the top-down system response of an organism to a stressor or intervention (Nicholson and Wilson, 2003). Recently, metabolic profiling strategies have been successfully applied to investigating the effects of the gut microflora on mammalian metabolism (Martin et al, 2007a), including probiotic treatment on germ-free mice (Martin et al, 2007b), modulation of Trichinella spiralis-induced gut disorders (Martin et al, 2006) and mechanisms of insulin-resistance (Dumas et al, 2006). In the current study, both 1H nuclear magnetic resonance spectroscopy and ultra performance liquid chromatography-mass spectrometry analysis have been applied to characterize the global metabolic responses of humanized microbiome mice subsequently exposed to placebo, Lactobacillus paracasei or Lactobacillus rhamnosus supplementation. Correlation of the response across multiple biofluids and tissue, using plasma, urine, fecal extracts, liver tissues and ileal flushes as the biological matrices for the detection of dietary intervention, generates a top-down systems biology view of the response to probiotics intervention. Significant associations between host metabolic phenotypes and a nutritionally modified gut-microbiota strongly supports the idea that changes across a whole range of metabolic pathways are the products of extended genome perturbations that can be oriented using probiotic supplementation, and which may play a role in host metabolic health. Here, we show that probiotics supplementation of humanized mice resulted in a decrease in the plasma concentrations of VLDL and low density lipoproteins (LDL), and increased triglyceride concentrations (Figure 1), through inducing changes in the enterohepatic recirculation of bile acids, which were shown to lower cholesterol and systemic levels of blood lipids (Pereira and Gibson, 2002). In particular, the Lactobacillus supplementation resulted in decreased fecal excretion of bile acids (Figure 1C), that may be caused by accumulation of bile acids in Lactobacillus probiotics (Kurdi et al, 2000). Moreover, probiotic-specific modulation of the ileal concentrations of UDCA and CDCA (Figure 2; Table IV) may also contribute to modulate the synthesis and secretion of VLDL into the blood (Lin et al, 1996; Watanabe et al, 2004). Moreover, it is reported that Lactobacillus hydrolyzes soy oil to conjugated linoleic acid efficiently (Xu et al, 2005), which could also contribute in the observed reduction of plasma lipoprotein concentrations (Fukushima et al, 1996, 1997; Al-Othman, 2000). Correlation analysis derived from bile acid and fecal flora profiles offers a unique approach to capture subtle variations in bile acid composition that may be directly related to changes in gut microbial levels, and that may be induced by accumulation of bile acids in Lactobacillus probiotics for instance. These different correlative patterns further characterize the microbial–mammalian transgenomic metabolic interactions, whereby probiotics-induced modulation of the gut microbial functional ecosystem results in different bile acid composition (Figure 2) and enterohepatic recirculation. Gut-bacterial regulation of choline metabolism could also contribute to determine host lipid metabolism. Interestingly, L. rhamnosus supplementation contributes to higher intestinal absorption of free choline and elevated production of methylamines, whereas L. paracasei consumption may result in increasing bacterial consumption of choline for cholesterol assimilation (Rasic, 1992) and phospholipid metabolism (Jenkins and Courtney, 2003; Taranto et al, 2003; Kankaanpaa et al, 2004) rather than for methylamine metabolism. Furthermore, probiotics supplementation was shown to modulate energy recovery from the diet through different amino-acid metabolism as observed with increased urinary excretion of phenolic and indolic compounds, increased levels of some amino acids in feces and higher bacterial production of short-chain fatty acids, as outlined in Figure 6. These different bacterial metabolisms resulted in different mammalian energy metabolism as observed with changes in gluconeogenesis, glycogenolysis and anaerobic glycolysis. We have also presented the application of hierarchical-principal component analysis (H-PCA) as a way forward to study perturbation of metabolic profiles triggered by symbiotic microbiota at a 'global system' level by analyzing simultaneously several metabolite pools from different biofluids and tissues. The H-PCA model also efficiently summarized the intercorrelated changes related to higher systemic glycolysis in plasma, urine and liver matrices, that is, reduced ketone body formation, anaerobic glycolysis, tricarboxylic cycle perturbation and amino-acid catabolism. Such observations might lead to describing multiorgan metabolic perturbations. These integrated system investigations demonstrate the potential of metabolic profiling as a top-down systems biology driver for investigating the mechanistic basis of probiotic action and the therapeutic surveillance of the gut microbial activity related to dietary supplementation of probiotics and their health consequences. Introduction The gut microbiome–mammalian 'Superorganism' (Lederberg, 2000) represents a level of biological evolutionary development in which there is extensive 'transgenomic' modulation of metabolism and physiology that is a characteristic of true symbiosis. By definition, superorganisms contain multiple cell types, and the coevolved interacting genomes can only be effectively studied as an in vivo unit in situ using top-down systems biology approaches (Nicholson, 2006; Martin et al, 2007a). Interest in the impact of gut microbial activity on human health is expanding rapidly and many mammalian–microbial associations, both positive and negative, have been reported (Dunne, 2001; Verdu et al, 2004; Nicholson et al, 2005; Gill et al, 2006; Ley et al, 2006). Mammalian–microbial symbiosis can play a strong role in the metabolism of endogenous and exogenous compounds and can also be influential in the etiology and development of several diseases, for example insulin resistance (Dumas et al, 2006), Crohn's disease (Gupta et al, 2000; Marchesi et al, 2007), irritable bowel syndrome (Sartor, 2004; Martin et al, 2006), food allergies (Bjorksten et al, 2001), gastritis and peptic ulcers (Warren, 2000; Marshall, 2003), obesity (Ley et al, 2006; Turnbaugh et al, 2006), cardiovascular disease (Pereira and Gibson, 2002) and gastrointestinal cancers (Dunne, 2001). Activities of the diverse gut microbiota can be highly specific and it has been reported that the establishment of Bifidobacteria is important for the development of the immune system and for maintaining gut function (Blum and Schiffrin, 2003; Salminen et al, 2005; Ouwehand, 2007). In particular, elevated counts in Bifidobacterium with reduced Escherichia coli, streptococci, Bacteroides and clostridia counts in breast-fed babies compared to formula-fed neonates may result in the lower incidence of infections, morbidity and mortality in breast-fed infants (Dai et al, 2000; Kunz et al, 2000). As the microbiome interacts strongly with the host to determine the metabolic phenotype (Holmes and Nicholson, 2005; Gavaghan McKee et al, 2006) and metabolic phenotype influences outcomes of drug interventions (Nicholson et al, 2004; Clayton et al, 2006), there is clearly an important role of understanding these interactions as part of personalized healthcare solutions (Nicholson, 2006). One of the current approaches used to modulate the balance of intestinal microflora is based on oral administration of probiotics. A probiotic is generally defined as a 'live microbial feed supplement which beneficially affects the host animal by improving its intestinal microbial balance' (Fuller, 2004). The gastrointestinal system is populated by potentially pathogenic bacteria that are capable of degrading proteins (putrefaction), releasing ammonia, amines and indoles, which in high concentrations can be toxic to humans (Cummings and Bingham, 1987). Probiotic supplementation aims at replacing or reducing the number of potentially harmful E. coli and Clostridia in the intestine by enriching the populations of gut microbiota that ferment carbohydrates and that have little proteolytic activity. Probiotics, most commonly Lactobacillus and Bifidobacteria, can be used to modulate the balance of the intestinal microflora in a beneficial way (Collins and Gibson, 1999). Although Lactobacilli do not predominate among the intestinal microflora, their resistance to acid conditions and bile salts toxicity results in their ubiquitous presence throughout the gut (Corcoran et al, 2005), hence they can exert metabolic effects at many levels. Fermented dairy products containing Lactobacillus have traditionally been used to modulate the microbial ecology (Dunne, 2001). In particular, L. paracasei was shown to modulate the intestinal physiology, to prevent infection of pathogenic bacteria (Sarker et al, 2005), to stimulate the immune system (Ibnou-Zekri et al, 2003), and to normalize gastrointestinal disorders (Martin et al, 2006). L. rhamnosus is also a significant probiotic strain with proven health benefits and therapeutic applications in the treatment of diarrhea (Szynanski et al, 2006), irritable bowel syndrome (Kajander et al, 2005), atopic eczema (Corcoran et al, 2005) and the prevention of urinary tract infections (Reid and Bruce, 2006). However, the functional effects of probiotic interventions cannot be fully assessed without probing the biochemistry of the host at multiple compartmental levels, and we propose that top-down systems biology provides an ideal approach to further understanding in this field. The microbiota observed in human baby flora (HBF) mice have a number of similarities with that found in formula-fed neonates (Mackie et al, 1999), which makes it to be a well-adapted and simplified model to assess probiotics impact on gut microbial functional ecosystems (in particular on metabolism of Bifidobacteria and potential pathogens) and subsequent effects on host metabolism. Metabolic profiling using high-density data generating spectroscopic techniques, in combination with multivariate mathematical modelling is a tool which is well suited to generate metabolic profiles that encapsulate the top-down system response of an organism to a stressor or intervention (Nicholson and Wilson, 2003). Multivariate metabolic profiling offers a practical approach to measuring the metabolic endpoints that link directly to whole system activity and which are determined by both host genetic and environmental factors (Nicholson et al, 2005). Recently, metabolic profiling strategies have been successfully applied to characterizing the metabolic consequences of nutritional intervention (Rezzi et al, 2007; Wang et al, 2007) the effects of the gut microflora on mammalian metabolism (Martin et al, 2006, 2007a, 2007b) and mechanisms of insulin-resistance (Dumas et al, 2006). In the current study, 1H nuclear magnetic resonance (NMR) spectroscopy and targeted ultra performance liquid chromatography-mass spectrometry (UPLC-MS) analysis have been applied to characterize the global metabolic responses of humanized microbiome mice subsequently exposed to placebo, Lactobacillus paracasei or Lactobacillus rhamnosus supplementation. Correlation of the response across multiple biofluids and tissue, using plasma, urine, fecal extracts, liver tissues and ileal flushes as the biological matrices for the detection of dietary intervention, generates a top-down systems biology view of the response to probiotics intervention. Results Gut bacterial composition Microbiological analyses were performed on fecal samples to assess the growth of the HBF in germ-free mice and to ascertain the effects of probiotics on the development of gut bacteria. The measured terminal composition of the fecal microbiota is detailed in Table I, where the statistically significant differences between the various groups were calculated using a two-tailed Mann–Whitney test. The bacterial populations of Bifidobacteria longum and Staphylococcus aureus were reduced after introduction of both probiotics. Additionally, unique effects of L. rhamnosus supplementation caused decreased populations of Bifidobacterium breve, Staphylococcus epidermidis and Clostridium perfringens but an increase of E. coli. Table 1. Microbial species counts in mouse feces at the end of the experiment Groups/log10 CFU HBF (n=10) HBF+L. paracasei (n=9) HBF+L. rhamnosus (n=9) L. paracasei — 8.5±0.2 — L. rhamnosus — — 7.8±0.2 E. coli 9.2±0.3 9.4±0.3 9.8±0.5** B. breve 9.1±0.2 7.78±2.13 8.7±0.3* B. longum 8.2±0.6 5.6±1.9*** 6.3±0.5*** S. aureus 7.4±0.3 6.3±0.3*** 6.6±0.5*** S. epidermidis 4.8±0.4 4.9±1.2 4.0±0.5** C. perfringens 7.2±0.3 7.0±0.5 5.7±1.0*** Bacteroides 10.3±0.2 10.4±0.2 10.1±0.4 log10 CFU (colony-forming unit) given per gram of wet weight of feces. Data are presented as mean±s.d. Absence of specific bacterial strains in the gut microflora is indicated by "—". The values for the HBF mice supplemented with probiotics were compared to HBF control mice, *, ** and *** indicate a significant difference at 95, 99 and 99.9% confidence levels, respectively. Gut levels of short-chain fatty acids Short-chain fatty acids (SCFAs), namely acetate, propionate, isobutyrate, n-butyrate and isovalerate, were identified and quantified from the cecal content using GC-FID. The results, presented in Table II, are given in μmol per gram of dry fecal material and as mean±s.d. for each group of mice. The production of some of the SCFAs, that is, acetate and butyrate, by the HBF mice supplemented with both of the probiotics was reduced. In addition, increases of the concentrations in isobutyrate and isovalerate were observed in the mice fed with L. paracasei. Table 2. Short-chain fatty acid content in the cecum from the different groups Amounts of SCFAs given in μmol per gram of dry feces for each group Acetate Propionate Isobutyrate Butyrate Isovalerate HBF (n=10) 77.6±17.6 22.3±4.3 0.9±0.2 3±0.6 2.1±0.6 HBF+L. paracasei (n=9) 52.3±23.6*** 22.2±10.8 1.2±0.5*** 1.5±0.8*** 2.7±1.2** HBF+L. rhamnosus (n=9) 40.6±8*** 20.3±2.8 0.8±0.2 2.1±0.4*** 2.1±0.5 Data are presented in μmol per gram of dry feces and are presented as means±s.d. The amounts of SCFAs for the HBF mice supplemented with probiotics were compared to HBF control mice, ** and *** indicate a significant difference at 99 and 99.9% confidence levels, respectively. Analysis of1H NMR spectroscopic data on plasma, urine, liver and fecal extracts A series of pairwise O-PLS-DA models of 1H NMR spectra were performed to extract information on the metabolic effects of probiotic modulation. A statistically significant metabolic phenotype separation between untreated mice and probiotic supplemented animals was observed as reflected by the high value of QY2 for each model (Cloarec et al, 2005b; Table III). The corresponding coefficients describing the most important metabolites in plasma, liver, urine and fecal extracts that contributed to group separation are also listed in Supplementary Table 1. The area normalized intensities (101 a.u.) of representative metabolite signals are given as means±s.d. in Table III. The O-PLS-DA coefficients plots are presented in Figure 1 using a back-scaling transformation and projection to aid biomarker visualization (Cloarec et al, 2005b). The direction of the signals in the plots relative to zero indicates positive or negative covariance with the probiotic-treated class. Each variable is plotted with a color code that indicates its discriminating power as calculated from the correlation matrix thus highlighting biomarker-rich spectral regions. Figure 1.O-PLS-DA coefficient plots derived from 1H MAS NMR CPMG spectra of liver (A, D), 1H NMR CPMG spectra of plasma (B, E), 1H NMR standard spectra of fecal extracts (C, F) and urine (G, H), indicating discrimination between HBF mice fed with probiotics (positive) and HBF control mice (negative). The color code corresponds to the correlation coefficients of the variables with the classes. BAs, Bile acids; DMA, dimethylamine; Glc, glucose; Gln, glutamine; GPC, glycerophosphorylcholine; IAG, indoleacetylglycine; Ileu, isoleucine; Leu, leucine; Lys, lysine; NAG, N-acetylated glycoproteins; NAM, N-acetylated metabolites; Osides, glycosides; PAG, phenylacetylglycine; TBAs, taurine conjugated to bile acids; TMA, trimethylamine; TMAO, trimethylamine-N-oxide; UGLp, unidentified glycolipids. Download figure Download PowerPoint Table 3. Summary of influential metabolites for discriminating NMR spectra of liver, plasma, fecal extracts and urine Metabolites Chemical shift and multiplicity HBF controls HBF+L. paracsei HBF+L. rhamnosus Liver QY2=21%, RX2=44% QY2=41%, RX2=37% Leu 0.92(t) 2.4±0.6 1.7±0.3*** 1.9±0.5* Ileu 0.94 (t) 0.8±0.1 0.6±0.05*** 0.7±0.2 Lactate 1.32(d) 38.4±5.8 46.2±8.2a 39.2±9.7 Succinate 2.41(s) 0.2±0.1 1.0±0.6** 0.7±0.3* MA 2.61(s) 0.1±0.06 0.04±0.002** 0.08±0.05 TMA 2.91 (s) 0.2±0.04 0.07±0.03*** 0.2±0.09 TMAO 3.27(s) 10.3±2.2 13.1±3.7 18.5±8.0** Gln 2.44(m) 0.4±0.1 0.3±0.1* 0.3±0.1 Glycogen 5.38–5.45 3.4±1.9 1.5±0.6* 3.2±1.9 Plasma QY2=44%, RX2=50% QY2=51%, RX2=32% Lipoproteins 0.84 (m) 13.7±1.8 10.1±0.9*** 9.8±4.2** Citrate 2.65(d) 1.4±0.3 0.9±0.4** 1.1±0.2** Choline 3.2(s) 11.6±2.6 16.2±5.7* 20.5±3.8*** GPC 3.22(s) 44.1±4.6 57.3±12.5** 68.1±11.2*** Glyceryls 3.91(m) 2.0±0.3 2.5±0.4** 2.7±0.4** Feces QY2=90%, RX2=48% QY2=89%, RX2=49% Caprylate 1.27(m) 2.5±0.1 3.5±0.2*** 2.4±0.1 Lys 3.00(m) 3.2±0.8 5.0±0.3*** 4.9±1.2** Osides 5.42(m) 0.9±0.09 1.2±0.1*** 1.4±0.1*** Bile acids 0.72(s) 3.1±0.9 1.8±0.7** 2.0±0.6* Ethanol 1.18(t) 2.5±0.1 2.0±0.08*** 1.9±0.09*** Choline 3.20(s) 48.0±19.5 11.3±4.1*** 20.3±10.9*** NAM 2.06(m) 7.1±1.0 5.4±0.3*** 5.5±0.3*** Acetate 1.91(s) 58.7±34.2 27.0±9.2** 32.9±12.9* U1 3.71(s) 9.2±0.5 7.3±0.3*** 8.2±0.5*** Urine QY2=91%, RX2=47% QY2=59%, RX2=46% IAG 7.55(d) 0.1±0.03 0.6±0.2*** 0.4±0.2** PAG 7.37(m) 0.8±0.1 1.5±0.3*** 1.2±0.4* Tryptamine 7.70(d) 0.1±0.04 0.4±0.1*** 0.2±0.1** UGLp 1.27(m) 1.7±0.1 2.7±0.4*** 1.7±0.2 Glycero-metabolites 4.04 (m) 1.7±0.1 2.2±0.2*** 1.9±0.2* NAG 2.04(s) 3.5±0.2 4.3±0.2*** 3.8±0.5 Butyrate 0.90(t) 6.9±0.8 5.2±0.7** 5.2±0.9** α-keto-isocaproate 0.94(d) 13.8±4.6 6.1±2.3** 7.9±2.1** Propionate 1.05(t) 0.9±0.2 0.8±0.04* 0.8±0.1 3-hydroxy-isovalerate 1.24(s) 3.0±0.4 2.1±0.4** 2.6±0.2 Citrate 2.55(d) 10.8±7.6 1.6±0.6** 2.2±0.9* Creatine 3.92(s) 5.7±2.1 4.5±1.5 3.5±0.3** Citrulline 1.88(m) 3.8±0.5 3.3±0.4 3.0±0.4** O-PLS models were generated for comparing probiotics treated to HBF control mice using one predictive and two orthogonal components, RX2 value shows how much of the variation is explained, QY2 value represents the predictability of the models, and relates to its statistical validity. Data are presented as area normalized intensities (101 a.u.) of representative metabolite signals as means±s.d. The values for the HBF mice supplemented with probiotics were compared to HBF control mice, a, *, ** and *** indicate a significant difference at 90, 95, 99 and 99.9% confidence levels, respectively. s, singlet; d, doublet; t, triplet; q, quartet; m, multiplet; dd, doublet of doublets. For metabolite abbreviations, refer to the key in Figure 1, MA: methylamine. Liver metabolic profiles Livers of mice fed with L. paracasei showed relative decreases in dimethylamine (DMA), trimethylamine (TMA), leucine, isoleucine, glutamine, and glycogen and increased levels of succinate and lactate (Figure 1A). Mice supplemented with L. rhamnosus showed relative decreases in leucine and isoleucine and relative increases in succinate, TMA and trimethylamine-N-oxide (TMAO) in the liver compared to controls (Figure 1D).
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