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Metabolome progression during early gut microbial colonization of gnotobiotic mice.

Marcobal A, Yusufaly T, Higginbottom S, Snyder M, Sonnenburg JL, Mias GI - Sci Rep (2015)

Bottom Line: High-throughput mass spectrometry profiling of urine samples revealed dynamic changes in the metabolome makeup, associated with the gut bacterial colonization, enabled by our adaptation of non-linear time-series analysis to urine metabolomics data.Results demonstrate both gradual and punctuated changes in metabolite production and that early colonization events profoundly impact the nature of small molecules circulating in the host.The identified small molecules are implicated in amino acid and carbohydrate metabolic processes, and offer insights into the dynamic changes occurring during the colonization process, using high-throughput longitudinal methodology.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA.

ABSTRACT
The microbiome has been implicated directly in host health, especially host metabolic processes and development of immune responses. These are particularly important in infants where the gut first begins being colonized, and such processes may be modeled in mice. In this investigation we follow longitudinally the urine metabolome of ex-germ-free mice, which are colonized with two bacterial species, Bacteroides thetaiotaomicron and Bifidobacterium longum. High-throughput mass spectrometry profiling of urine samples revealed dynamic changes in the metabolome makeup, associated with the gut bacterial colonization, enabled by our adaptation of non-linear time-series analysis to urine metabolomics data. Results demonstrate both gradual and punctuated changes in metabolite production and that early colonization events profoundly impact the nature of small molecules circulating in the host. The identified small molecules are implicated in amino acid and carbohydrate metabolic processes, and offer insights into the dynamic changes occurring during the colonization process, using high-throughput longitudinal methodology.

No MeSH data available.


Related in: MedlinePlus

Temporal Trends and Associated Networks.On the left the hierarchical clustering per classification (Autocorrelated, Spike Maxima and Spike Minima) is shown. For each trend molecules with unique KEGG ID72 or identified through MS standards were used in QIAGEN’s IPA Ingenuity pathway construction (IPA®, QIAGEN Redwood City, www.qiagen.com/ingenuity). The network displayed on the right (network score 16, “Cell-mediated Immune Response, Inflammatory Response, Gastrointestinal Disease”) includes 5 molecules, each shown aligned horizontally with its corresponding temporal trend and identified with its group on the left. Significant functions and canonical pathway results that include more than two of the metabolites or network components respectively are also included. See also Supplementary Table S4 for detailed network composition and functional analysis and Supplementary Fig. S3.
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f3: Temporal Trends and Associated Networks.On the left the hierarchical clustering per classification (Autocorrelated, Spike Maxima and Spike Minima) is shown. For each trend molecules with unique KEGG ID72 or identified through MS standards were used in QIAGEN’s IPA Ingenuity pathway construction (IPA®, QIAGEN Redwood City, www.qiagen.com/ingenuity). The network displayed on the right (network score 16, “Cell-mediated Immune Response, Inflammatory Response, Gastrointestinal Disease”) includes 5 molecules, each shown aligned horizontally with its corresponding temporal trend and identified with its group on the left. Significant functions and canonical pathway results that include more than two of the metabolites or network components respectively are also included. See also Supplementary Table S4 for detailed network composition and functional analysis and Supplementary Fig. S3.

Mentions: The classification analyses of the experimental urine metabolome data assigned a total of 576 molecules to the different time trend classes (334 autocorrelated, 106 spike maxima, 136 spike minima). 45 of these molecules were considered to be high interest identifications based on their uniqueness of mass, or identity verification through the use of standards using follow-up mass spectrometry experiments (Table 1, Supplementary Table S1 for full data). Hierarchical clustering within each temporal class revealed distinct trends in the metabolite compositions, corresponding to the colonization of the GF mice (see Methods) (Fig. 3, left). The autocorrelated trends revealed two distinct groups, showing contrasting trends – one increasing constantly following colonization (A2 in Fig. 3, which included validated compounds such as tyramine, L-homocysteine, and estriol), with the other decreasing constantly (clustering group A5 in Fig. 3, including validated compounds such as 5 hydroxy-L-tryptophan and N-acetyl-L-methionine). The spike maxima and minima also displayed various trends, with the most prominent spike occurring on Day 5 (e.g. L-phenylalanine in clustering group Min 4, Fig. 3).


Metabolome progression during early gut microbial colonization of gnotobiotic mice.

Marcobal A, Yusufaly T, Higginbottom S, Snyder M, Sonnenburg JL, Mias GI - Sci Rep (2015)

Temporal Trends and Associated Networks.On the left the hierarchical clustering per classification (Autocorrelated, Spike Maxima and Spike Minima) is shown. For each trend molecules with unique KEGG ID72 or identified through MS standards were used in QIAGEN’s IPA Ingenuity pathway construction (IPA®, QIAGEN Redwood City, www.qiagen.com/ingenuity). The network displayed on the right (network score 16, “Cell-mediated Immune Response, Inflammatory Response, Gastrointestinal Disease”) includes 5 molecules, each shown aligned horizontally with its corresponding temporal trend and identified with its group on the left. Significant functions and canonical pathway results that include more than two of the metabolites or network components respectively are also included. See also Supplementary Table S4 for detailed network composition and functional analysis and Supplementary Fig. S3.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4484351&req=5

f3: Temporal Trends and Associated Networks.On the left the hierarchical clustering per classification (Autocorrelated, Spike Maxima and Spike Minima) is shown. For each trend molecules with unique KEGG ID72 or identified through MS standards were used in QIAGEN’s IPA Ingenuity pathway construction (IPA®, QIAGEN Redwood City, www.qiagen.com/ingenuity). The network displayed on the right (network score 16, “Cell-mediated Immune Response, Inflammatory Response, Gastrointestinal Disease”) includes 5 molecules, each shown aligned horizontally with its corresponding temporal trend and identified with its group on the left. Significant functions and canonical pathway results that include more than two of the metabolites or network components respectively are also included. See also Supplementary Table S4 for detailed network composition and functional analysis and Supplementary Fig. S3.
Mentions: The classification analyses of the experimental urine metabolome data assigned a total of 576 molecules to the different time trend classes (334 autocorrelated, 106 spike maxima, 136 spike minima). 45 of these molecules were considered to be high interest identifications based on their uniqueness of mass, or identity verification through the use of standards using follow-up mass spectrometry experiments (Table 1, Supplementary Table S1 for full data). Hierarchical clustering within each temporal class revealed distinct trends in the metabolite compositions, corresponding to the colonization of the GF mice (see Methods) (Fig. 3, left). The autocorrelated trends revealed two distinct groups, showing contrasting trends – one increasing constantly following colonization (A2 in Fig. 3, which included validated compounds such as tyramine, L-homocysteine, and estriol), with the other decreasing constantly (clustering group A5 in Fig. 3, including validated compounds such as 5 hydroxy-L-tryptophan and N-acetyl-L-methionine). The spike maxima and minima also displayed various trends, with the most prominent spike occurring on Day 5 (e.g. L-phenylalanine in clustering group Min 4, Fig. 3).

Bottom Line: High-throughput mass spectrometry profiling of urine samples revealed dynamic changes in the metabolome makeup, associated with the gut bacterial colonization, enabled by our adaptation of non-linear time-series analysis to urine metabolomics data.Results demonstrate both gradual and punctuated changes in metabolite production and that early colonization events profoundly impact the nature of small molecules circulating in the host.The identified small molecules are implicated in amino acid and carbohydrate metabolic processes, and offer insights into the dynamic changes occurring during the colonization process, using high-throughput longitudinal methodology.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA.

ABSTRACT
The microbiome has been implicated directly in host health, especially host metabolic processes and development of immune responses. These are particularly important in infants where the gut first begins being colonized, and such processes may be modeled in mice. In this investigation we follow longitudinally the urine metabolome of ex-germ-free mice, which are colonized with two bacterial species, Bacteroides thetaiotaomicron and Bifidobacterium longum. High-throughput mass spectrometry profiling of urine samples revealed dynamic changes in the metabolome makeup, associated with the gut bacterial colonization, enabled by our adaptation of non-linear time-series analysis to urine metabolomics data. Results demonstrate both gradual and punctuated changes in metabolite production and that early colonization events profoundly impact the nature of small molecules circulating in the host. The identified small molecules are implicated in amino acid and carbohydrate metabolic processes, and offer insights into the dynamic changes occurring during the colonization process, using high-throughput longitudinal methodology.

No MeSH data available.


Related in: MedlinePlus