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• W2142500268 abstract "Article16 October 2015Open Access The gut microbiota modulates host amino acid and glutathione metabolism in mice Adil Mardinoglu Corresponding Author Adil Mardinoglu Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Saeed Shoaie Saeed Shoaie Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden Search for more papers by this author Mattias Bergentall Mattias Bergentall Department of Molecular and Clinical Medicine, Wallenberg Laboratory, University of Gothenburg, Gothenburg, Sweden Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology and Enteroendocrinology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Pouyan Ghaffari Pouyan Ghaffari Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden Search for more papers by this author Cheng Zhang Cheng Zhang Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Erik Larsson Erik Larsson Department of Molecular and Clinical Medicine, Wallenberg Laboratory, University of Gothenburg, Gothenburg, Sweden Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology and Enteroendocrinology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Fredrik Bäckhed Fredrik Bäckhed Department of Molecular and Clinical Medicine, Wallenberg Laboratory, University of Gothenburg, Gothenburg, Sweden Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology and Enteroendocrinology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Jens Nielsen Jens Nielsen Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Adil Mardinoglu Corresponding Author Adil Mardinoglu Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Saeed Shoaie Saeed Shoaie Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden Search for more papers by this author Mattias Bergentall Mattias Bergentall Department of Molecular and Clinical Medicine, Wallenberg Laboratory, University of Gothenburg, Gothenburg, Sweden Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology and Enteroendocrinology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Pouyan Ghaffari Pouyan Ghaffari Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden Search for more papers by this author Cheng Zhang Cheng Zhang Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Erik Larsson Erik Larsson Department of Molecular and Clinical Medicine, Wallenberg Laboratory, University of Gothenburg, Gothenburg, Sweden Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology and Enteroendocrinology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Fredrik Bäckhed Fredrik Bäckhed Department of Molecular and Clinical Medicine, Wallenberg Laboratory, University of Gothenburg, Gothenburg, Sweden Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology and Enteroendocrinology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author Jens Nielsen Jens Nielsen Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden Search for more papers by this author Author Information Adil Mardinoglu 1,2,‡, Saeed Shoaie1,‡, Mattias Bergentall3,4, Pouyan Ghaffari1, Cheng Zhang2, Erik Larsson3,4, Fredrik Bäckhed3,4 and Jens Nielsen1,2 1Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden 2Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden 3Department of Molecular and Clinical Medicine, Wallenberg Laboratory, University of Gothenburg, Gothenburg, Sweden 4Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology and Enteroendocrinology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark ‡These authors contributed equally to this work *Corresponding author. Tel: +46 31 772 3140; Fax: +46 31 772 3801; E-mail: [email protected] Molecular Systems Biology (2015)11:834https://doi.org/10.15252/msb.20156487 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Figures & Info Abstract The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. Synopsis Tissue-specific genome-scale metabolic models (GEMs), transcriptomic and metabolomic analyses reveal global metabolic differences between conventionally raised and germ-free mice and show that the gut microbiota affects host amino acid and glutathione metabolism. A generic Mouse Metabolic Reaction GEM (MMR) is created using the mouse orthologs of human genes in HMR2. Tissue-specific GEMs for 28 mouse tissues are reconstructed and applied for the analysis of global gene expression data. Microbial-induced metabolic differences in the small intestine are simulated using the relative metabolic differences (RMetD) method. The model predictions are validated by measuring amino acid levels in the portal vein. Introduction The human gut harbors a vast ensemble of bacteria that have profound effects on host physiology (Huttenhower et al, 2012). Complex disorders including obesity (Ley et al, 2006; Turnbaugh et al, 2009), type 2 diabetes (T2D) (Qin et al, 2012; Karlsson et al, 2013), atherosclerosis (Wang et al, 2011b; Karlsson et al, 2012), and non-alcoholic fatty liver disease (NAFLD) (Henao-Mejia et al, 2012) as well as the opposite end of the spectrum, for example, malnutrition (Smith et al, 2013; Subramanian et al, 2014), have been associated with dysbiosis in the human gut microbiota. To gain mechanistic insights into the contribution of specific microbial populations to the progression of such disorders, germ-free (GF) animals (e.g. mice and rats) have been adopted for studying the association of the gut microbiota with disease pathogenesis (Ridaura et al, 2013). Comparisons between GF and conventionally raised (CONV-R) mice are often used for studying the effect of gut microbiota on host physiology (Wostmann, 1981; Stappenbeck et al, 2002; Claus et al, 2008; Slack et al, 2009; El Aidy et al, 2013). Moreover, Larsson et al (2012) studied the response of the host induced by microbiota along the length of the gut in CONV-R and GF C57Bl6/J mice and provided a detailed description for tissue-specific host transcriptional responses. Global metabolic differences of cells/tissues between different clinical conditions can be revealed through the use of genome-scale metabolic models (GEMs) (Mardinoglu & Nielsen, 2012, 2015; Yizhak et al, 2013, 2014a,b; Bordbar et al, 2014; Shoaie & Nielsen, 2014; O'Brien et al, 2015; Varemo et al, 2015; Zhang et al, 2015). GEMs include the known metabolism-related reactions and associated genes in a particular cell and tissue and serve as an excellent scaffold for the integration of omics data (e.g. proteomics, transcriptomics, and metabolomics) for increasing our understanding of the relationship between genotype and phenotype (Mardinoglu et al, 2013b). To date, simulation-ready cell-/tissue-specific GEMs (Gille et al, 2010; Karlstaedt et al, 2012; Mardinoglu et al, 2013a, 2014a) and automatically reconstructed GEMs (Jerby et al, 2010; Agren et al, 2012; Wang et al, 2012; Yizhak et al, 2014a; Uhlen et al, 2015) have been used for studying the metabolism of cells/tissues in health and disease states. In order to examine the gut microbiota-induced transcriptional responses of the host metabolism, we performed microarray analysis of liver as well as epididymal and subcutaneous white adipose tissues (WATs) obtained from both CONV-R and GF mice, and analyzed the global gene expression profile of these tissues together with the previously published gene expression profiles of duodenum, jejunum, ileum, and colon tissues. We created a generic mouse metabolic reaction (MMR) GEM and generated tissue-specific mouse GEMs primarily based on proteomics data. We investigated the metabolic differences between CONV-R and GF mice using global gene expression profiling of the host tissues and the network topology provided by the tissue GEMs, and validated our predictions by generating metabolomics data for these two sets of mice. Finally, we revealed the metabolic differences between the small intestine of CONV-R and GF mice accounting for the content of the chow diet as well as the relative gene expression differences using relative metabolic differences (RMetD) method. Results Global transcriptional profiles of CONV-R and GF mice CONV-R and GF C57Bl6/J male mice were fed autoclaved chow diet ad libitum and then euthanized at 12–14 weeks of age (Larsson et al, 2012). We isolated RNA from liver as well as epididymal and subcutaneous WATs obtained from CONV-R and GF mice and performed global transcriptome analysis (Fig 1A). Small intestine and colon have been previously removed from the same two sets of mice, and small intestine has been divided into eight whereas the colon into three equal-sized segments (Larsson et al, 2012). Global transcriptome analysis was performed for the first (duodenum), fifth (jejunum), and eighth (ileum) segments of the small intestine and the proximal piece of the colon (Fig 1A). Figure 1. Global gene expression profiling of tissues obtained from CONV-R and GF mice Liver as well as epididymal and subcutaneous WATs was obtained from both CONV-R and GF mice, and global gene expression profiling was generated using microarrays. Transcriptomics data for these three tissues were analyzed together with the previously published gene expression profiling of duodenum, jejunum, ileum, and colon tissues. Gene expression data for each tissue were normalized independently of other tissues, and significantly (Q-value < 0.05) differentially expressed probe sets and metabolic genes in Mouse Metabolic Reaction database were presented in each analyzed tissue. The overlap between the significantly (Q-value < 0.05) and differentially expressed metabolic genes in duodenum, jejunum, ileum, colon, and liver is presented. The significantly (Q-value < 0.05) and differentially expressed metabolic genes, Nnt, and Entpd4, as well as the reactions associated with Nnt, are presented. Red and blue arrows indicate the significantly higher (Q-value < 0.05) and lower expression of the metabolic genes in CONV-R mice compared to GF mice, respectively. Download figure Download PowerPoint We performed principal component analysis (PCA) of the transcription profiles for the tissues separately and observed a clear separation between the CONV-R and GF mice for duodenum, jejunum, ileum, colon, and liver, whereas no separation was found for both WATs (Fig EV1). We identified significantly differentially expressed probe sets and genes in MMR, from here on referred as metabolic genes, by comparing gene expression profiles of tissues obtained from CONV-R versus GF mice (Fig 1B, Dataset EV1). During the identification of the significantly (Q-value < 0.05) differentially expressed probe sets and metabolic genes, we adjusted P-values using the false discovery rate (FDR) method and calculated Q-values. We found that ileum tissue had the largest number of differentially expressed metabolic genes between CONV-R and GF mice, and it was followed by duodenum, jejunum, colon, and liver tissues (Fig 1B). It should also be noted that we only detected two significantly differentially expressed metabolic genes between the subcutaneous WAT whereas no differentially expressed metabolic genes between the epididymal WAT. Click here to expand this figure. Figure Figure EV1. Gene expression profiling of CONV-R and GF mice tissuesGlobal gene expression profile of seven different tissues including liver, epididymal and subcutaneous fat, duodenum, jejunum, ileum, and colon tissues has been generated, and each tissue sample is normalized independently. Principal component analysis (PCA) of transcription profiles on each tissue is presented. Download figure Download PowerPoint Comparing the differentially expressed metabolic genes between duodenum, jejunum, ileum, colon, and liver tissues of CONV-R and GF mice (Fig 1C), we found that the expression of the nicotinamide nucleotide transhydrogenase (Nnt) gene is higher and ectonucleoside triphosphate diphosphohydrolase 4 (Entpd4) is lower in all five tissues of CONV-R mice compared with GF mice (Fig 1D). Strikingly, we found that Nnt and Entpd4 are also the only differentially expressed genes in the subcutaneous WAT of the CONV-R mice compared with GF mice and followed the same directional changes in the subcutaneous WAT as in all other five analyzed tissues. Entpd4 was initially named human Golgi UDPase, and it hydrolyzes nucleoside diphosphates. UDP is the best substrate for this enzyme, and its ADP activity is insignificant. Nnt is required for regular mitochondrial function, and it uses energy from the mitochondrial proton gradient to transfer reducing equivalents from NADH to NADPH. The resulting NADPH is used for driving macromolecular biosynthesis as well as for the reduction of glutathione (GSH) (Fig 1D). Here, we focused on the metabolic differences associated with Nnt due to its well-known metabolic function. Creation of MMR and reconstruction of mouse tissue-specific GEMs We constructed MMR by using the mouse orthologs of human genes in HMR2 (Mardinoglu et al, 2014a) (Fig 2A), and the resulting generic model includes 8,140 metabolism-related reactions, 3,579 associated metabolic genes to those reactions, and 5,992 metabolites in eight different subcellular compartments. Previously, stable isotope labeling with amino acids (SILAC)-based proteomics was generated to analyze the expression of 7,349 proteins in 28 different major C57BL/6 mouse tissues (Geiger et al, 2013) and these data cover 2,030 of the protein-coding genes in MMR (Fig 2B, Dataset EV2). We reconstructed tissue-specific GEMs for 28 mouse tissues by using proteomics data, MMR, and the tINIT algorithm (Agren et al, 2014) (see 4). The tINIT algorithm allows for the reconstruction of functional GEMs based on global proteomics data as well as user-defined metabolic tasks, which the resulting model should be able to perform. During the reconstruction of the models, we complemented 56 metabolic tasks (functions) (Agren et al, 2014), which are known to occur in all cells/tissues. Figure 2. Creation of MMR and generation of tissue-specific GEMs Mouse Metabolic Reaction database (MMR) was created using the mouse orthologs of human genes based on Human Metabolic Reaction database 2.0 (HMR2). The expression level of the 2,032 proteins used in the generation of the 28 tissue-specific mice models is presented. Bar plots represent the distribution of tissue-specific reactions, metabolites, genes, and metabolites across the 28 mouse tissue GEMs. Filled circles depict average distance of each tissue GEMs compared to others. Average distance, calculated based on Hamming distance method, indicates required alteration to transform one tissue model to the other based on the reactions and metabolites and genes. For instance, 478 changes in gene profile are required for intertransformation of GEM for lung and stomach, from which 401 changes in genes correspond to transformation of lung to stomach and 77 changes in genes correspond to transformation of stomach to lung. Filled circles represent the heterogeneity degree of 28 mouse tissues and 83 healthy human cell types. Heterogeneity values are projected on the left hand side axis. There is a fall, ˜0.06 degree, in heterogeneity of mice models compared to human modes based on genes and a lower decrease, ˜0.03, based on the reactions. However, comparing mouse tissues to human cells revealed higher heterogeneity based on the metabolites, in contrast to the reaction and genes. Average Hamming distance of GEMs for mouse tissues and human cell types are projected on the right hand axis. Mouse tissues have relatively less, 40–60%, inter-model distance compared to human cells based on the reactions and genes. However, the trend is reversed with around 50% increased inter-model distance for metabolites. In general, mouse tissue-specific GEMs show gain of heterogeneity based on metabolites and loss of heterogeneity based on genes. Download figure Download PowerPoint The number of reactions, metabolites, and genes incorporated in the models are presented in Dataset EV3. A total of 5,813 reactions, 4,574 metabolites, and 1,838 genes were shared across the tissue-specific GEMs of which 2,750 (47.3%) reactions, 3,001 (65.6%) metabolites, and 669 (36.4%) genes were common to all tissue-specific GEMs. We found that 322 reactions, 134 metabolites, and 120 genes were incorporated into only one specific GEM (Fig 2C). By pairwise comparison of GEMs, we found that each model has an average of 765 reactions (Dataset EV4), 430 metabolites (Dataset EV5), and 342 genes (Dataset EV6) different from other tissues where the muscle tissue was the one with the highest average difference (Fig 2C). We analyzed the heterogeneity of the mouse tissue-specific GEMs (Fig 2D) in terms of incorporated reactions, genes, and metabolites by calculating the heterogeneity degree of each model. The heterogeneity degree allowed us to capture the divergence between metabolic networks based on their constituent parameters including reactions, metabolites, and genes, and it was calculated using the average and maximum Hamming distance of the models (Ghaffari et al, 2015). Moreover, we analyzed the heterogeneity of recently reconstructed human cell-specific GEMs (Agren et al, 2014) that have been reconstructed based on antibody-based proteomics data in the Human Protein Atlas (www.proteinatlas.org) (Uhlen et al, 2010, 2015; Kampf et al, 2014b). On average, mouse tissue-specific GEMs showed an average heterogeneity degree of 0.77 for reactions, 0.72 for metabolites, and 0.78 for genes, whereas human cell-specific GEMs had an average heterogeneity degree of 0.8 for reactions, 0.7 for metabolites, and 0.84 for genes (Fig 2D). Compared with the human cell-specific GEMs, the mouse tissue-specific GEMs had a slightly higher metabolic uniformity and lower heterogeneity based on the incorporated genes and reactions, but they had a slightly higher heterogeneity based on the incorporated metabolites into the models. We next incorporated the significantly differentially expressed genes between CONV-R and GF mouse tissues and generated four functional GEMs for liver (iMouseLiver), adipose (iMouseAdipose), colon (iMouseColon), and small intestine (iMouseSmallintestine), with the latter reconstructed by merging the GEMs for duodenum, jejunum, and ileum tissues. During manual evaluation of the GEMs, previously published functional human cell-type GEMs for hepatocytes in liver tissue (Mardinoglu et al, 2014a) and adipocytes in adipose tissue (Mardinoglu et al, 2013a, 2014b) were also used to include known biological functions to the GEMs. The number of the incorporated reactions, metabolites, and genes in the four functional annotated tissue-specific GEMs as well as in the draft GEMs is provided in Dataset EV3. MMR as well as the all mouse tissue-specific models are publicly available in systems biology markup language (SBML) format at the Human Metabolic Atlas portal (www.metabolicatlas.org) (Pornputtapong et al, 2015), at the BioModels database and as Computer Code EV1. Decreased glutathione synthesis in the small intestine of CONV-R mice We compared the gene expression profiling in the small intestine segments (duodenum, jejunum and ileum) of CONV-R and GF mice, and examined the changes in the expression of the genes interacting with Nnt using the network structure provided by iMouseSmallintestine (Fig 1D). We found that the expression of glutathione reductase (Gsr) which uses NADPH as an electron donor to reduce glutathione disulfide (GSSG) to GSH was also significantly higher (Q-value < 0.05) in all three small intestine segments of CONV-R mice compared to GF mice (Fig 3A, Dataset EV1). GSH plays a key role in reducing oxidative stress, and it can be synthesized within the cells from glutamate, cysteine, and glycine through the use of glutamate-cysteine ligase catalytic subunit (Gclc), glutamate-cysteine ligase modifier subunit (Gclm), and glutathione synthetase (Gss). We found that the expression of Gclc is significantly lower (Q-value < 0.05) in jejunum and ileum, and the expressions of Gclm and Gss are significantly lower (Q-value < 0.05) in the ileum of CONV-R mice compared to GF mice (Fig 3A). Based on gene expression data, we observed that decreased de novo synthesis of GSH in the small intestine of CONV-R mice was compensated by higher expression of Nnt and Gsr compared to GF mice. Figure 3. Metabolic differences in the small intestine A. Metabolic genes as well as the associated reactions involved in the formation of glutathione (GSH) are presented. B, C. Significant differences associated with (B) glycine and (C) glutamine are shown. Red and blue arrows indicate the significantly (Q-value < 0.05) higher and lower expression of the metabolic genes in CONV-R mice compared to GF mice, respectively. D. The levels of glycine, glutamine, and cysteine used in the de novo synthesis of the GSH are measured in the hepatic portal vein that conducts blood from the gastrointestinal tract to the liver tissue. *Q-value < 0.05. Download figure Download PowerPoint The decreased synthesis of the GSH in the small intestine segments of CONV-R mice may be due to the limited availability of the substrates including glutamate, cysteine, and glycine. Hence, we examined the expression of the enzymes involved in the synthesis and catabolism of these amino acids by differentially expressed genes in the small intestine (Dataset EV7) and using the network topology provided by iMouseSmallintestine. We integrated differentially expressed genes in duodenum, jejunum, and ileum using the lowest Q-value for the genes and associated fold changes for studying the metabolic differences between the small intestine of CONV-R and GF mice (Dataset EV7). Hereby, we identified significantly (Q-value < 0.05) differentially expressed genes linked to biosynthesis of glycine (Fig 3B) and glutamate (Fig 3C) and found that there are metabolic differences in the utilization of these AAs between CONV-R and GF mice. In contrast, we did not detect any significant change in the expression of the genes linked to cysteine except Gclc and Gclm (Dataset EV7). In healthy animals, AAs in the small intestine are released in the plasma and utilized by other peripheral tissues (e.g. liver, adipose, and muscle tissues). Considering the down-regulation of genes associated with de novo synthesis of GSH and of glutamate and glycine required for GSH biosynthesis in the small intestine in CONV-R mice, we hypothesized that the plasma level of glutamate and glycine secreted from the small intestine in CONV-R mice may also be lower compared with GF mice. Hence, we measured the level of these two AAs in the hepatic portal vein (PV), which conducts blood from the gastrointestinal tract to the liver, of CONV-R and GF mice, and found that the PV level of glycine was significantly lower (ANOVA test, Q-value < 0.05) and glutamate is slightly lower in CONV-R mice compared to GF mice (Fig 3D, Dataset EV8). We also measured the level of cysteine, likewise used for biosynthesis of GSH, in the PV of CONV-R and GF mice (Fig 3D) but found no differences, in agreement with the fact that there were no significant changes in the expression of genes encoding enzymes required for cysteine utilization (Dataset EV7). We also searched for global metabolic differences in the small intestine by mapping the gene expression data (Dataset EV7) to the network topology provided by the small intestine GEM using the reporter subnetworks algorithm (Patil & Nielsen, 2005). Hereby, we found that there are major metabolic differences around 17 other AAs (Dataset EV9). We thus next measured the level of these 17 AAs in the PV and found that the levels of arginine, asparagine, histidine, isoleucine, leucine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, and valine were significantly lower and the level of glutamine was significantly higher in CONV-R mice compared to GF mice. We did not detect any significant changes in the level of alanine, aspartate, and lysine (Fig 4A, Dataset EV8). Our results indicate that the gut microbiota alter AA metabolism of the host. Figure 4. The level of the AAs and N-acetylated AAs in the hepatic portal vein The level of the significantly (Q-value < 0.05) changed amino acids (AAs) including arginine, asparagine, glutamine, histidine, isoleucine, leucine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, valine, and glutamine as well as the non-significantly changed AAs including alanine, aspartate, and lysine are measured in the hepatic portal vein of CONV-R and GF mice. Reactions involved in the hydrolysis of N-acetylated AAs to acetate and a free AA as well as their catalyzing enzymes, aminoacylases, ACY1, ACY2 (ASPA), and ACY3 are presented. The level of the N-acetylated AAs in hepatic portal vein of CONV-R and GF mice are shown in the volcano plot. Download figure Download PowerPoint Metabolic differences between the liver tissues of CONV-R and GF mice We found that the expression of Nnt was significantly higher and Entpd4 was significantly lower in the liver tissue of CONV-R compared to GF mice (Fig 5A) and validated the expression of these genes by quantitative reverse transcription PCR (RT–PCR) methods (Fig 5B). We found that glutathione S-transferase pi 1 (Gstp1), which has a role in GSH metabolism, metabolism of xenobiotics by cytochrome P450, and drug metabolism, is significantly higher in CONV-R mice compared to GF mice. Notably, Claus et al (2008) measured the liver tissue level of GSSG, which is used as a substrate for the reaction catalyzed by the Gsr in CONV-R and GF mice by employing a high-resolution 1H NMR spectroscopic approach, and reported that the liver tissue level of GSSG was significantly higher in CONV-R mice. Hence, we hypothesized that higher Nnt expression in CONV-R mice might be the response of liver to the lower level of glycine required for the de novo synthesis of the GSH. Strikingly, Claus et al (2008) has also measured the glycine level in the liver tissue of CONV-R and GF mice, and found that the level of glycine is lower in CONV-R mice compared to GF mice. We also measured the PV level of serine, which can be taken up by the liver and converted to glycine, and found that the level of serine was also significantly lower in CONV-R mice compared to GF mice (Fig 4A). Figure 5. Metabolic differences in the liver tissue The significantly (Q-value < 0.05) and differentially expressed metabolic genes in liver tissue of CONV-R mice compared to GF mice are mapped to the functional GEM for mice liver tissue. Red and blue arrows indicate the significantly (Q-value < 0.05) higher and lower expression of the metabolic genes in CONV-R mice compared to GF mice, respect" @default.
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• W2142500268 title "The gut microbiota modulates host amino acid and glutathione metabolism in mice" @default.
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