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

Abstract

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.

Figures

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.

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 QY2 and RX2 are (76.4, 52.2%) and (50.3, 51.2%).

Figure 3

Schematic overview of H-PCA modelling: (Gunnarsson et al, 2003). In the sublevel, each block of data XB is modelled locally by a PCA model. Each block is summarized by one or more loading vectors pb and score vectors tp (‘super variables'), which can be combined to form a new data matrix than can then be modelled using PCA, which generates the ‘super scores' tT and the ‘super loadings' pT. All conventional PCA statistics and diagnostics are retained.

Figure 4

H-PCA scores (A) and loadings (B) plots for the two first components derived from scores of separate PCA constructed separately for the metabolic data from each individual biological matrix from HBF mice (▪), HBF-L. paracasei mice (⧫) and HBF-L. rhamnosus mice (○). These PCA models explained 94% (bile acid, C), 70% (plasma, D), 80% (liver, E), 41% (urine, F) and 61% (feces, data not shown) of the total variation in the data, respectively. The systematic variation from each of the block/compartment is summarized by its score vectors denoted Pi (plasma PCs 1–4), Li (liver PCs 1–3), Ui (urine PCs 1–3), Bi (bile acid PCs 1–3) and Fi (fecal PCs 1–3), which can be combined to form a new data matrix than can then be modelled using PCA. The individual PCA loadings were color coded according to their contribution to the H-PCA model in red (principal component 1) and in blue (principal component 2). The model has been calculated from Pareto scaled data using two cross-validated PCs, R2X=60%. Ala, alanine; see Figure 1.

Figure 5

Integration of bile acid and fecal flora correlations. The bipartite graphs were derived from correlations between fecal flora and bile acids in each group: HBF mice (A), HBF mice supplemented with L. paracasei (B) or L. rhamnosus (C). The cut-off value of 0.5 was applied to the absolute value of the coefficient ∣r∣ for displaying the correlations between fecal flora and bile acids. Bile acids and fecal bacteria correspond to blue ellipse nodes and green rectangle nodes, respectively. Edges are coded according to correlation value: positive and negative correlations are respectively displayed in blue and in red. aMA, α-muricholic acid; Ba, Bacteroides; Bb, B. breve; Bl, B. longum; bMA, β-muricholic acid; CA, cholic acid; CDCA, chenodeoxycholic acid; Cp, C. perfringens; Ec, E. coli; GCA, glycocholic acid; La, Lactobacillus probiotics; Sa, S. aureus; Se, S. epidermidis; TCA, taurocholic acid; TCDCA, taurochenodeoxycholic acid; TMCA, tauro-β-muricholic acid; TUDCA, tauroursocholic acid; UDCA, ursocholic acid. * and *** indicate a statistically significant correlation at 95 and 99.9% confidence levels, respectively.

Figure 6

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