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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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Related in: MedlinePlus

INDSCAL model of plant data set. a Group scores for component 1 vs. component 2, b Group scores for component 2 vs. component 3, circles refer to control group (CON), squares to root herbivory (RJA) and crosses to shoot herbivory (SJA). Loading are presented as heatplots separately for each component: c loadings for component 1, d loadings for component 2 and e loadings for component 3
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Fig2: INDSCAL model of plant data set. a Group scores for component 1 vs. component 2, b Group scores for component 2 vs. component 3, circles refer to control group (CON), squares to root herbivory (RJA) and crosses to shoot herbivory (SJA). Loading are presented as heatplots separately for each component: c loadings for component 1, d loadings for component 2 and e loadings for component 3

Mentions: A 4-component INDSCAL model explains a highly unstable amount of information (see Table 1), leading to three-components. This INDSCAL model (Fig. 2) corresponds very well to the PCA results. The first component explains a BMR in the SJA plants that increases from absence to an enormous contribution 14 days after harvest and explains the high NEO with low GBC levels (see Fig. 2c). The positive relation between PRO and GBN, expected to be high specifically high 1 day after RJA is indeed present in the second INDSCAL component (Fig. 2d). The component is also important 14 days after SJA, which after further inspection of the large confidence interval on the PCA scores. The third INDSCAL component describes the consistently larger variation in NEO and in GBC related to the natural variation between the different SJA (1–14 days) and RJA plants (1–7 days) described by the first PCA component (Fig. 2e). In this data set, the qualitatively observed BMRs in the earlier PCA model could be quantified in the INDSCAL model.Table 1


Between Metabolite Relationships: an essential aspect of metabolic change.

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

INDSCAL model of plant data set. a Group scores for component 1 vs. component 2, b Group scores for component 2 vs. component 3, circles refer to control group (CON), squares to root herbivory (RJA) and crosses to shoot herbivory (SJA). Loading are presented as heatplots separately for each component: c loadings for component 1, d loadings for component 2 and e loadings for component 3
© Copyright Policy
Related In: Results  -  Collection

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

Fig2: INDSCAL model of plant data set. a Group scores for component 1 vs. component 2, b Group scores for component 2 vs. component 3, circles refer to control group (CON), squares to root herbivory (RJA) and crosses to shoot herbivory (SJA). Loading are presented as heatplots separately for each component: c loadings for component 1, d loadings for component 2 and e loadings for component 3
Mentions: A 4-component INDSCAL model explains a highly unstable amount of information (see Table 1), leading to three-components. This INDSCAL model (Fig. 2) corresponds very well to the PCA results. The first component explains a BMR in the SJA plants that increases from absence to an enormous contribution 14 days after harvest and explains the high NEO with low GBC levels (see Fig. 2c). The positive relation between PRO and GBN, expected to be high specifically high 1 day after RJA is indeed present in the second INDSCAL component (Fig. 2d). The component is also important 14 days after SJA, which after further inspection of the large confidence interval on the PCA scores. The third INDSCAL component describes the consistently larger variation in NEO and in GBC related to the natural variation between the different SJA (1–14 days) and RJA plants (1–7 days) described by the first PCA component (Fig. 2e). In this data set, the qualitatively observed BMRs in the earlier PCA model could be quantified in the INDSCAL model.Table 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