Probiotic modulation of symbiotic gut microbial–host metabolic interactions in a humanized microbiome mouse model

Francois-Pierre J Martin^1,2, Yulan Wang¹, Norbert Sprenger², Ivan K S Yap¹, Torbjörn Lundstedt^3,4, Per Lek³, Serge Rezzi², Ziad Ramadan², Peter van Bladeren², Laurent B Fay², Sunil Kochhar², John C Lindon¹, Elaine Holmes¹ & Jeremy K Nicholson¹

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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).

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.

Full figure and legend (589K)Figures & Tables index

Figure 2

O-PLS-DA coefficient plots derived from the bile acid composition obtained by UPLC-MS analysis of ileal flushes, which indicate discrimination between HBF control mice (negative) and HBF mice treated with probiotics (positive), (A) L. paracasei and (B) L. rhamnosus. The color code corresponds to the correlation coefficients of the variables. One predictive and one orthogonal component were calculated; the respective Q_Y² and R_X² are (76.4, 52.2%) and (50.3, 51.2%).

Full figure and legend (150K)Figures & Tables index

Table 4: Bile acids composition in gut flushes for the different microbiota

Full tableFigures & Tables index

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.

Figure 6

Gut-microbiota–mammalian cometabolism of methylamines and aromatic amino acids. DMG, dimethylglycine; IA, indoleacetate; MA, methylamine; PA, phenylacetate (see Figure 1).

Full figure and legend (184K)Figures & Tables index

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.

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