Limits...
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.

Show MeSH

Related in: MedlinePlus

(A) Score and loading scatter plot of PLS analysis (Q2(cum) = 0.96, R2(Y) = 0.95).(blue • = ICD, red • = CCD and green • = Healthy). The masses with the highest regression coefficients were considered as discriminant. Coordinates on the figure axes are ×108. (B) Example of a differentiating metabolite for ICD (assigned at m/z of 391.2853) that is up regulated in the ICD group but the structure is unknown. (C) Mass at m/z of 407.2802 corresponding to 3α, 7α, or 12α-trihydroxy-5β-cholanate within the bile acid biosynthesis pathway. The intensities in B and C were normalized.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2713417&req=5

pone-0006386-g001: (A) Score and loading scatter plot of PLS analysis (Q2(cum) = 0.96, R2(Y) = 0.95).(blue • = ICD, red • = CCD and green • = Healthy). The masses with the highest regression coefficients were considered as discriminant. Coordinates on the figure axes are ×108. (B) Example of a differentiating metabolite for ICD (assigned at m/z of 391.2853) that is up regulated in the ICD group but the structure is unknown. (C) Mass at m/z of 407.2802 corresponding to 3α, 7α, or 12α-trihydroxy-5β-cholanate within the bile acid biosynthesis pathway. The intensities in B and C were normalized.

Mentions: Using a partial least squares (PLS) statistical approach on corrected mass data the separation between disease phenotypes was even more pronounced than when using the PCA model, with a clear separation of individuals with ICD from those with CCD and from healthy individuals (Fig. 1A) and some examples of differentiating metabolites are shown in Figure 1B. This differentiation according to disease phenotype that was seen using both the PCA and PLS approaches provides further support to the recent hypothesis that ICD and CCD are different disease phenotypes of CD. The outlier with CCD was the youngest of our Crohn's patients (born 1986) and had only had the disease for 4 years at the time of sampling, whereas all the others have had the disease for >10 years.


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)

(A) Score and loading scatter plot of PLS analysis (Q2(cum) = 0.96, R2(Y) = 0.95).(blue • = ICD, red • = CCD and green • = Healthy). The masses with the highest regression coefficients were considered as discriminant. Coordinates on the figure axes are ×108. (B) Example of a differentiating metabolite for ICD (assigned at m/z of 391.2853) that is up regulated in the ICD group but the structure is unknown. (C) Mass at m/z of 407.2802 corresponding to 3α, 7α, or 12α-trihydroxy-5β-cholanate within the bile acid biosynthesis pathway. The intensities in B and C were normalized.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0006386-g001: (A) Score and loading scatter plot of PLS analysis (Q2(cum) = 0.96, R2(Y) = 0.95).(blue • = ICD, red • = CCD and green • = Healthy). The masses with the highest regression coefficients were considered as discriminant. Coordinates on the figure axes are ×108. (B) Example of a differentiating metabolite for ICD (assigned at m/z of 391.2853) that is up regulated in the ICD group but the structure is unknown. (C) Mass at m/z of 407.2802 corresponding to 3α, 7α, or 12α-trihydroxy-5β-cholanate within the bile acid biosynthesis pathway. The intensities in B and C were normalized.
Mentions: Using a partial least squares (PLS) statistical approach on corrected mass data the separation between disease phenotypes was even more pronounced than when using the PCA model, with a clear separation of individuals with ICD from those with CCD and from healthy individuals (Fig. 1A) and some examples of differentiating metabolites are shown in Figure 1B. This differentiation according to disease phenotype that was seen using both the PCA and PLS approaches provides further support to the recent hypothesis that ICD and CCD are different disease phenotypes of CD. The outlier with CCD was the youngest of our Crohn's patients (born 1986) and had only had the disease for 4 years at the time of sampling, whereas all the others have had the disease for >10 years.

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