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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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Variance and covariance of selected metabolites. a Variance of TG29 and b covariance between TG28 and TG29; BL baseline group, GTE catechin-enriched green tea extract group, placebo placebo group, significantly different: **P < 0.05 and ***P < 0.01
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Fig4: Variance and covariance of selected metabolites. a Variance of TG29 and b covariance between TG28 and TG29; BL baseline group, GTE catechin-enriched green tea extract group, placebo placebo group, significantly different: **P < 0.05 and ***P < 0.01

Mentions: The effect of GTE on the plasma lipids is clearly visible in both INDSCAL model representations in Fig. 3. It is mainly associated with relations between a very small subset of lipids. Most important are the triacylglycerols TG28-29 and TG41-42. Figure 4a shows that the variance of TG29 (those for TGs 28, 41 and 42 are comparable and not shown) is significantly affected by GTE compared to the control and the BL groups, although the mean group levels of these metabolites did not change (Supplementary Fig. 4). The Pearson correlation coefficients for these lipids did not change between GTE and placebo (see Supplementary Table 5), but the covariances did (Fig. 4b; Supplementary Fig. 4). The barplot in Fig. 4b corresponds closely to the INDSCAL scores (Fig. 3a). The INDSCAL results and the (co)variance plots show that the effect of GTE manifested itself by a systematic increase of the covariance between TG28 and TG29 during the entire study period and an additional increase of the covariance between TG41 and TG42 during the first 4 weeks of intervention, described by the second INDSCAL component. The last is related to a large inter-individual difference in time and magnitude of response at the beginning of the intervention.Fig. 4


Between Metabolite Relationships: an essential aspect of metabolic change.

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

Variance and covariance of selected metabolites. a Variance of TG29 and b covariance between TG28 and TG29; BL baseline group, GTE catechin-enriched green tea extract group, placebo placebo group, significantly different: **P < 0.05 and ***P < 0.01
© Copyright Policy
Related In: Results  -  Collection

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

Fig4: Variance and covariance of selected metabolites. a Variance of TG29 and b covariance between TG28 and TG29; BL baseline group, GTE catechin-enriched green tea extract group, placebo placebo group, significantly different: **P < 0.05 and ***P < 0.01
Mentions: The effect of GTE on the plasma lipids is clearly visible in both INDSCAL model representations in Fig. 3. It is mainly associated with relations between a very small subset of lipids. Most important are the triacylglycerols TG28-29 and TG41-42. Figure 4a shows that the variance of TG29 (those for TGs 28, 41 and 42 are comparable and not shown) is significantly affected by GTE compared to the control and the BL groups, although the mean group levels of these metabolites did not change (Supplementary Fig. 4). The Pearson correlation coefficients for these lipids did not change between GTE and placebo (see Supplementary Table 5), but the covariances did (Fig. 4b; Supplementary Fig. 4). The barplot in Fig. 4b corresponds closely to the INDSCAL scores (Fig. 3a). The INDSCAL results and the (co)variance plots show that the effect of GTE manifested itself by a systematic increase of the covariance between TG28 and TG29 during the entire study period and an additional increase of the covariance between TG41 and TG42 during the first 4 weeks of intervention, described by the second INDSCAL component. The last is related to a large inter-individual difference in time and magnitude of response at the beginning of the intervention.Fig. 4

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