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Integrating Multiple Analytical Datasets to Compare Metabolite Profiles of Mouse Colonic-Cecal Contents and Feces.

Zeng H, Grapov D, Jackson MI, Fahrmann J, Fiehn O, Combs GF - Metabolites (2015)

Bottom Line: We detected a total of 270 low molecular weight metabolites in colonic-cecal contents and feces by gas chromatograph, time-of-flight mass spectrometry (GC-TOF) and ultra-high performance liquid chromatography, quadrapole time-of-flight mass spectrometry (UPLC-Q-TOF).Of that number, 251 (93%) were present in both types of specimen, representing almost all known biochemical pathways related to the amino acid, carbohydrate, energy, lipid, membrane transport, nucleotide, genetic information processing, and cancer-related metabolism.A total of 115 metabolites differed significantly in relative abundance between both colonic-cecal contents and feces.

View Article: PubMed Central - PubMed

Affiliation: Grand Forks Human Nutrition Research Center, Agricultural Research Service, United States Department of Agriculture, Grand Forks, ND 58203, USA. huawei.zeng@ars.usda.gov.

ABSTRACT
The pattern of metabolites produced by the gut microbiome comprises a phenotype indicative of the means by which that microbiome affects the gut. We characterized that phenotype in mice by conducting metabolomic analyses of the colonic-cecal contents, comparing that to the metabolite patterns of feces in order to determine the suitability of fecal specimens as proxies for assessing the metabolic impact of the gut microbiome. We detected a total of 270 low molecular weight metabolites in colonic-cecal contents and feces by gas chromatograph, time-of-flight mass spectrometry (GC-TOF) and ultra-high performance liquid chromatography, quadrapole time-of-flight mass spectrometry (UPLC-Q-TOF). Of that number, 251 (93%) were present in both types of specimen, representing almost all known biochemical pathways related to the amino acid, carbohydrate, energy, lipid, membrane transport, nucleotide, genetic information processing, and cancer-related metabolism. A total of 115 metabolites differed significantly in relative abundance between both colonic-cecal contents and feces. These data comprise the first characterization of relationships among metabolites present in the colonic-cecal contents and feces in a healthy mouse model, and shows that feces can be a useful proxy for assessing the pattern of metabolites to which the colonic mucosum is exposed.

No MeSH data available.


The heatmap displays relative increase/decrease of metabolite contents and their similarities between individual samples. In these visualization columns represent samples and rows variables, and hierarchical cluster analysis (HCA) was used to group samples and metabolites based on similarities in auto-scaled values and correlations, respectively. Cluster identities represent differing experimental biological or analytical variability; colors (red, relative increase; blue, relative decrease).
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metabolites-05-00489-f002: The heatmap displays relative increase/decrease of metabolite contents and their similarities between individual samples. In these visualization columns represent samples and rows variables, and hierarchical cluster analysis (HCA) was used to group samples and metabolites based on similarities in auto-scaled values and correlations, respectively. Cluster identities represent differing experimental biological or analytical variability; colors (red, relative increase; blue, relative decrease).

Mentions: Comparisons of the relative abundances of the 270 metabolites found in these specimens are shown as a heat map [17] (Figure 2). As the smaller bracket (at the top of Figure 2) represents the higher similarity in abundance between individual samples, the data show that samples within a given sample type (e.g., cecal contents or feces) shared very similar patterns (Figure 2). The cecal contents and feces also shared an overall similar metabolite profiles with only a few distinct patterns because feces were downstream-products of colonic-cecal contents via colonic fermentation (Figure 2, Table S1). Orthogonal signal correction partial least squares discriminant analysis (O-PLS-DA) [18] was used to develop a multivariate classification model for these two sample types. The two latent variable (X and Y axis) for an O-PLS-DA model (Figure 3), can be used to evaluate within- and between-group similarities between samples, and the smaller values of latent the more similar [6,18]. As latent variables of metabolites in cecal contents were smaller than that of feces (Figure 3), it suggests that inter individual variance data within cecal content group are less different than that of feces samples (Figure 3) [19]. This suggests that future metabolite profiling studies would require fewer samples of colonic-cecal contents than that of feces within a treatment group.


Integrating Multiple Analytical Datasets to Compare Metabolite Profiles of Mouse Colonic-Cecal Contents and Feces.

Zeng H, Grapov D, Jackson MI, Fahrmann J, Fiehn O, Combs GF - Metabolites (2015)

The heatmap displays relative increase/decrease of metabolite contents and their similarities between individual samples. In these visualization columns represent samples and rows variables, and hierarchical cluster analysis (HCA) was used to group samples and metabolites based on similarities in auto-scaled values and correlations, respectively. Cluster identities represent differing experimental biological or analytical variability; colors (red, relative increase; blue, relative decrease).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4588808&req=5

metabolites-05-00489-f002: The heatmap displays relative increase/decrease of metabolite contents and their similarities between individual samples. In these visualization columns represent samples and rows variables, and hierarchical cluster analysis (HCA) was used to group samples and metabolites based on similarities in auto-scaled values and correlations, respectively. Cluster identities represent differing experimental biological or analytical variability; colors (red, relative increase; blue, relative decrease).
Mentions: Comparisons of the relative abundances of the 270 metabolites found in these specimens are shown as a heat map [17] (Figure 2). As the smaller bracket (at the top of Figure 2) represents the higher similarity in abundance between individual samples, the data show that samples within a given sample type (e.g., cecal contents or feces) shared very similar patterns (Figure 2). The cecal contents and feces also shared an overall similar metabolite profiles with only a few distinct patterns because feces were downstream-products of colonic-cecal contents via colonic fermentation (Figure 2, Table S1). Orthogonal signal correction partial least squares discriminant analysis (O-PLS-DA) [18] was used to develop a multivariate classification model for these two sample types. The two latent variable (X and Y axis) for an O-PLS-DA model (Figure 3), can be used to evaluate within- and between-group similarities between samples, and the smaller values of latent the more similar [6,18]. As latent variables of metabolites in cecal contents were smaller than that of feces (Figure 3), it suggests that inter individual variance data within cecal content group are less different than that of feces samples (Figure 3) [19]. This suggests that future metabolite profiling studies would require fewer samples of colonic-cecal contents than that of feces within a treatment group.

Bottom Line: We detected a total of 270 low molecular weight metabolites in colonic-cecal contents and feces by gas chromatograph, time-of-flight mass spectrometry (GC-TOF) and ultra-high performance liquid chromatography, quadrapole time-of-flight mass spectrometry (UPLC-Q-TOF).Of that number, 251 (93%) were present in both types of specimen, representing almost all known biochemical pathways related to the amino acid, carbohydrate, energy, lipid, membrane transport, nucleotide, genetic information processing, and cancer-related metabolism.A total of 115 metabolites differed significantly in relative abundance between both colonic-cecal contents and feces.

View Article: PubMed Central - PubMed

Affiliation: Grand Forks Human Nutrition Research Center, Agricultural Research Service, United States Department of Agriculture, Grand Forks, ND 58203, USA. huawei.zeng@ars.usda.gov.

ABSTRACT
The pattern of metabolites produced by the gut microbiome comprises a phenotype indicative of the means by which that microbiome affects the gut. We characterized that phenotype in mice by conducting metabolomic analyses of the colonic-cecal contents, comparing that to the metabolite patterns of feces in order to determine the suitability of fecal specimens as proxies for assessing the metabolic impact of the gut microbiome. We detected a total of 270 low molecular weight metabolites in colonic-cecal contents and feces by gas chromatograph, time-of-flight mass spectrometry (GC-TOF) and ultra-high performance liquid chromatography, quadrapole time-of-flight mass spectrometry (UPLC-Q-TOF). Of that number, 251 (93%) were present in both types of specimen, representing almost all known biochemical pathways related to the amino acid, carbohydrate, energy, lipid, membrane transport, nucleotide, genetic information processing, and cancer-related metabolism. A total of 115 metabolites differed significantly in relative abundance between both colonic-cecal contents and feces. These data comprise the first characterization of relationships among metabolites present in the colonic-cecal contents and feces in a healthy mouse model, and shows that feces can be a useful proxy for assessing the pattern of metabolites to which the colonic mucosum is exposed.

No MeSH data available.