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Metabolomics reveals metabolic biomarkers of Crohn's disease.

Jansson J, Willing B, Lucio M, Fekete A, Dicksved J, Halfvarson J, Tysk C, Schmitt-Kopplin P - PLoS ONE (2009)

Bottom Line: Pathways with differentiating metabolites included those involved in the metabolism and or synthesis of amino acids, fatty acids, bile acids and arachidonic acid.Several metabolites were positively or negatively correlated to the disease phenotype and to specific microbes previously characterized in the same samples.Our data reveal novel differentiating metabolites for CD that may provide diagnostic biomarkers and/or monitoring tools as well as insight into potential targets for disease therapy and prevention.

View Article: PubMed Central - PubMed

Affiliation: Ecology Department, Division of Earth Sciences, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.

ABSTRACT
The causes and etiology of Crohn's disease (CD) are currently unknown although both host genetics and environmental factors play a role. Here we used non-targeted metabolic profiling to determine the contribution of metabolites produced by the gut microbiota towards disease status of the host. Ion Cyclotron Resonance Fourier Transform Mass Spectrometry (ICR-FT/MS) was used to discern the masses of thousands of metabolites in fecal samples collected from 17 identical twin pairs, including healthy individuals and those with CD. Pathways with differentiating metabolites included those involved in the metabolism and or synthesis of amino acids, fatty acids, bile acids and arachidonic acid. Several metabolites were positively or negatively correlated to the disease phenotype and to specific microbes previously characterized in the same samples. Our data reveal novel differentiating metabolites for CD that may provide diagnostic biomarkers and/or monitoring tools as well as insight into potential targets for disease therapy and prevention.

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Related in: MedlinePlus

Similarity plot (using Jaccard's index) of (A) microbial composition based on binary T-RFLP data and (B) ICR-FT/MS data, respectively, from fecal samples of individuals with ICD (blue), CCD (red) and healthy individuals (green).Individuals were numbered according to Table S1, and as previously defined (11). Boxes indicate twin pairs that share the most similar metabolic and microbial profiles. Note: metaproteome data from the same fecal samples for individuals 6a and 6b have recently been published [33].
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pone-0006386-g003: Similarity plot (using Jaccard's index) of (A) microbial composition based on binary T-RFLP data and (B) ICR-FT/MS data, respectively, from fecal samples of individuals with ICD (blue), CCD (red) and healthy individuals (green).Individuals were numbered according to Table S1, and as previously defined (11). Boxes indicate twin pairs that share the most similar metabolic and microbial profiles. Note: metaproteome data from the same fecal samples for individuals 6a and 6b have recently been published [33].

Mentions: Manhattan distances were calculated from the metabolic profiles of all individuals to determine whether the gut metabolomes of twins were more similar to each other than to unrelated individuals. The inter-twin similarity (mean±SE) of healthy twins (0.513±0.035) and concordant twins (0.431±0.070) was greater (P<0.001) than that of discordant twins (0.276±0.048). This coincides with the reduced similarity of microbial profiles previously observed in the discordant twins [17]. A correlation between the metabolic and microbial distance matrices (r = 0.348, P<0.001) coincides with findings from Li et al. [5] correlating fecal microbial profiles to urinary metabolites, indicating a contribution of bacteria to overall metabolic profiles in the human host. We observed a stronger correlation between metabolic and microbial similarities when making within twin comparisons (r = 0.748, P<0.001) strengthening the hypothesis that genetics plays a role in the formation and maintenance of the intestinal microbiome (Fig. 3). The most striking observation from the cluster analysis (Fig. 3) was the similar division of clusters according to the disease phenotype for both the microbial and metabolite data reinforcing the link between microbial community structure, function and disease.


Metabolomics reveals metabolic biomarkers of Crohn's disease.

Jansson J, Willing B, Lucio M, Fekete A, Dicksved J, Halfvarson J, Tysk C, Schmitt-Kopplin P - PLoS ONE (2009)

Similarity plot (using Jaccard's index) of (A) microbial composition based on binary T-RFLP data and (B) ICR-FT/MS data, respectively, from fecal samples of individuals with ICD (blue), CCD (red) and healthy individuals (green).Individuals were numbered according to Table S1, and as previously defined (11). Boxes indicate twin pairs that share the most similar metabolic and microbial profiles. Note: metaproteome data from the same fecal samples for individuals 6a and 6b have recently been published [33].
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2713417&req=5

pone-0006386-g003: Similarity plot (using Jaccard's index) of (A) microbial composition based on binary T-RFLP data and (B) ICR-FT/MS data, respectively, from fecal samples of individuals with ICD (blue), CCD (red) and healthy individuals (green).Individuals were numbered according to Table S1, and as previously defined (11). Boxes indicate twin pairs that share the most similar metabolic and microbial profiles. Note: metaproteome data from the same fecal samples for individuals 6a and 6b have recently been published [33].
Mentions: Manhattan distances were calculated from the metabolic profiles of all individuals to determine whether the gut metabolomes of twins were more similar to each other than to unrelated individuals. The inter-twin similarity (mean±SE) of healthy twins (0.513±0.035) and concordant twins (0.431±0.070) was greater (P<0.001) than that of discordant twins (0.276±0.048). This coincides with the reduced similarity of microbial profiles previously observed in the discordant twins [17]. A correlation between the metabolic and microbial distance matrices (r = 0.348, P<0.001) coincides with findings from Li et al. [5] correlating fecal microbial profiles to urinary metabolites, indicating a contribution of bacteria to overall metabolic profiles in the human host. We observed a stronger correlation between metabolic and microbial similarities when making within twin comparisons (r = 0.748, P<0.001) strengthening the hypothesis that genetics plays a role in the formation and maintenance of the intestinal microbiome (Fig. 3). The most striking observation from the cluster analysis (Fig. 3) was the similar division of clusters according to the disease phenotype for both the microbial and metabolite data reinforcing the link between microbial community structure, function and disease.

Bottom Line: Pathways with differentiating metabolites included those involved in the metabolism and or synthesis of amino acids, fatty acids, bile acids and arachidonic acid.Several metabolites were positively or negatively correlated to the disease phenotype and to specific microbes previously characterized in the same samples.Our data reveal novel differentiating metabolites for CD that may provide diagnostic biomarkers and/or monitoring tools as well as insight into potential targets for disease therapy and prevention.

View Article: PubMed Central - PubMed

Affiliation: Ecology Department, Division of Earth Sciences, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.

ABSTRACT
The causes and etiology of Crohn's disease (CD) are currently unknown although both host genetics and environmental factors play a role. Here we used non-targeted metabolic profiling to determine the contribution of metabolites produced by the gut microbiota towards disease status of the host. Ion Cyclotron Resonance Fourier Transform Mass Spectrometry (ICR-FT/MS) was used to discern the masses of thousands of metabolites in fecal samples collected from 17 identical twin pairs, including healthy individuals and those with CD. Pathways with differentiating metabolites included those involved in the metabolism and or synthesis of amino acids, fatty acids, bile acids and arachidonic acid. Several metabolites were positively or negatively correlated to the disease phenotype and to specific microbes previously characterized in the same samples. Our data reveal novel differentiating metabolites for CD that may provide diagnostic biomarkers and/or monitoring tools as well as insight into potential targets for disease therapy and prevention.

Show MeSH
Related in: MedlinePlus