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The gut microbiota modulates host amino acid and glutathione metabolism in mice.

Mardinoglu A, Shoaie S, Bergentall M, Ghaffari P, Zhang C, Larsson E, Bäckhed F, Nielsen J - Mol. Syst. Biol. (2015)

Bottom Line: 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).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.Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden adilm@scilifelab.se.

No MeSH data available.


Related in: MedlinePlus

Creation of MMR and generation of tissue-specific GEMsMouse 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.
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fig02: Creation of MMR and generation of tissue-specific GEMsMouse 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.

Mentions: We constructed MMR by using the mouse orthologs of human genes in HMR2 (Mardinoglu et al, 2014a) (Fig2A), 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 (Fig2B, 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 Materials and Methods). 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.


The gut microbiota modulates host amino acid and glutathione metabolism in mice.

Mardinoglu A, Shoaie S, Bergentall M, Ghaffari P, Zhang C, Larsson E, Bäckhed F, Nielsen J - Mol. Syst. Biol. (2015)

Creation of MMR and generation of tissue-specific GEMsMouse 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.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4631205&req=5

fig02: Creation of MMR and generation of tissue-specific GEMsMouse 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.
Mentions: We constructed MMR by using the mouse orthologs of human genes in HMR2 (Mardinoglu et al, 2014a) (Fig2A), 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 (Fig2B, 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 Materials and Methods). 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.

Bottom Line: 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).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.Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden adilm@scilifelab.se.

No MeSH data available.


Related in: MedlinePlus