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Between Metabolite Relationships: an essential aspect of metabolic change.

Jansen JJ, Szymańska E, Hoefsloot HC, Jacobs DM, Strassburg K, Smilde AK - Metabolomics (2011)

Bottom Line: Component analysis methods in current 'standard' use for metabolomics, such as Principal Component Analysis (PCA), do not focus on changes in these relations.In the second study-a human nutritional intervention study of green tea extract-standard data analysis tools did not reveal any metabolic change, although the BMRs were considerably affected.The presented results show that BMRs can be easily implemented in a wide variety of metabolomic studies.

View Article: PubMed Central - PubMed

ABSTRACT
Not only the levels of individual metabolites, but also the relations between the levels of different metabolites may indicate (experimentally induced) changes in a biological system. Component analysis methods in current 'standard' use for metabolomics, such as Principal Component Analysis (PCA), do not focus on changes in these relations. We therefore propose the concept of 'Between Metabolite Relationships' (BMRs): common changes in the covariance (or correlation) between all metabolites in an organism. Such structural changes may indicate metabolic change brought about by experimental manipulation but which are lost with standard data analysis methods. These BMRs can be analysed by the INdividual Differences SCALing (INDSCAL) method. First the BMR quantification is described and subsequently the INDSCAL method. Finally, two studies illustrate the power and the applicability of BMRs in metabolomics. The first study is about the induced plant response of cabbage to herbivory, of which BMRs are a considerable part. In the second study-a human nutritional intervention study of green tea extract-standard data analysis tools did not reveal any metabolic change, although the BMRs were considerably affected. The presented results show that BMRs can be easily implemented in a wide variety of metabolomic studies. They provide a new source of information to describe biological systems in a way that fits flawlessly into the next generation of systems biology questions, dealing with personalized responses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0316-1) contains supplementary material, which is available to authorized users.

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Three paradigms to observe metabolic differences between two groups: a Level difference of an individual metabolite (e.g. ANOVA), b Level difference in a combination of, i.e. a component of more metabolites (e.g. PLS), c Changes in the combined relationship between metabolites (INDSCAL)
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Fig1: Three paradigms to observe metabolic differences between two groups: a Level difference of an individual metabolite (e.g. ANOVA), b Level difference in a combination of, i.e. a component of more metabolites (e.g. PLS), c Changes in the combined relationship between metabolites (INDSCAL)

Mentions: Recent advances in ‘omics’-research brought the study of BMRs closer, because metabolomics emerges more and more as a system-wide approach to observe metabolism (Bino et al. 2004; Fiehn 2002; Hall 2006). The data of a metabolomics study usually consists of a list of numerous metabolites, of which the levels are given for every measured sample (e.g. individual and/or time-point). Of prime interest to metabolomics studies may be to find the in- or decrease of specific metabolite levels between different groups of individuals (e.g. before and after an experimental perturbation) (Fig. 1a). However, this paradigm holds a major shortcoming for the system-wide view provided by metabolomics analyses, because it may disregard metabolite combinations that show interesting variation where the individual metabolites do not.Fig. 1


Between Metabolite Relationships: an essential aspect of metabolic change.

Jansen JJ, Szymańska E, Hoefsloot HC, Jacobs DM, Strassburg K, Smilde AK - Metabolomics (2011)

Three paradigms to observe metabolic differences between two groups: a Level difference of an individual metabolite (e.g. ANOVA), b Level difference in a combination of, i.e. a component of more metabolites (e.g. PLS), c Changes in the combined relationship between metabolites (INDSCAL)
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3351608&req=5

Fig1: Three paradigms to observe metabolic differences between two groups: a Level difference of an individual metabolite (e.g. ANOVA), b Level difference in a combination of, i.e. a component of more metabolites (e.g. PLS), c Changes in the combined relationship between metabolites (INDSCAL)
Mentions: Recent advances in ‘omics’-research brought the study of BMRs closer, because metabolomics emerges more and more as a system-wide approach to observe metabolism (Bino et al. 2004; Fiehn 2002; Hall 2006). The data of a metabolomics study usually consists of a list of numerous metabolites, of which the levels are given for every measured sample (e.g. individual and/or time-point). Of prime interest to metabolomics studies may be to find the in- or decrease of specific metabolite levels between different groups of individuals (e.g. before and after an experimental perturbation) (Fig. 1a). However, this paradigm holds a major shortcoming for the system-wide view provided by metabolomics analyses, because it may disregard metabolite combinations that show interesting variation where the individual metabolites do not.Fig. 1

Bottom Line: Component analysis methods in current 'standard' use for metabolomics, such as Principal Component Analysis (PCA), do not focus on changes in these relations.In the second study-a human nutritional intervention study of green tea extract-standard data analysis tools did not reveal any metabolic change, although the BMRs were considerably affected.The presented results show that BMRs can be easily implemented in a wide variety of metabolomic studies.

View Article: PubMed Central - PubMed

ABSTRACT
Not only the levels of individual metabolites, but also the relations between the levels of different metabolites may indicate (experimentally induced) changes in a biological system. Component analysis methods in current 'standard' use for metabolomics, such as Principal Component Analysis (PCA), do not focus on changes in these relations. We therefore propose the concept of 'Between Metabolite Relationships' (BMRs): common changes in the covariance (or correlation) between all metabolites in an organism. Such structural changes may indicate metabolic change brought about by experimental manipulation but which are lost with standard data analysis methods. These BMRs can be analysed by the INdividual Differences SCALing (INDSCAL) method. First the BMR quantification is described and subsequently the INDSCAL method. Finally, two studies illustrate the power and the applicability of BMRs in metabolomics. The first study is about the induced plant response of cabbage to herbivory, of which BMRs are a considerable part. In the second study-a human nutritional intervention study of green tea extract-standard data analysis tools did not reveal any metabolic change, although the BMRs were considerably affected. The presented results show that BMRs can be easily implemented in a wide variety of metabolomic studies. They provide a new source of information to describe biological systems in a way that fits flawlessly into the next generation of systems biology questions, dealing with personalized responses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0316-1) contains supplementary material, which is available to authorized users.

No MeSH data available.


Related in: MedlinePlus