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Molecular phenotyping of a UK population: defining the human serum metabolome.

Dunn WB, Lin W, Broadhurst D, Begley P, Brown M, Zelena E, Vaughan AA, Halsall A, Harding N, Knowles JD, Francis-McIntyre S, Tseng A, Ellis DI, O'Hagan S, Aarons G, Benjamin B, Chew-Graham S, Moseley C, Potter P, Winder CL, Potts C, Thornton P, McWhirter C, Zubair M, Pan M, Burns A, Cruickshank JK, Jayson GC, Purandare N, Wu FC, Finn JD, Haselden JN, Nicholls AW, Wilson ID, Goodacre R, Kell DB - Metabolomics (2014)

Bottom Line: Overall, this is a large scale and non-targeted chromatographic MS-based metabolomics study, using samples from over 1,000 individuals, to provide a comprehensive measurement of their serum metabolomes.This work provides an important baseline or reference dataset for understanding the 'normal' relative concentrations and variation in the human serum metabolome.These may be related to our increasing knowledge of the human metabolic network map.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Engineering and Physical Sciences, School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN UK ; Faculty of Engineering & Physical Sciences, Manchester Centre for Integrative Systems Biology, Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN UK ; Faculty of Medical and Human Sciences, Centre for Endocrinology and Diabetes, Institute of Human Development, The University of Manchester, Manchester, UK ; Centre for Advanced Discovery and Experimental Therapeutics (CADET), Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, M13 9WL UK ; School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.

ABSTRACT

Phenotyping of 1,200 'healthy' adults from the UK has been performed through the investigation of diverse classes of hydrophilic and lipophilic metabolites present in serum by applying a series of chromatography-mass spectrometry platforms. These data were made robust to instrumental drift by numerical correction; this was prerequisite to allow detection of subtle metabolic differences. The variation in observed metabolite relative concentrations between the 1,200 subjects ranged from less than 5 % to more than 200 %. Variations in metabolites could be related to differences in gender, age, BMI, blood pressure, and smoking. Investigations suggest that a sample size of 600 subjects is both necessary and sufficient for robust analysis of these data. Overall, this is a large scale and non-targeted chromatographic MS-based metabolomics study, using samples from over 1,000 individuals, to provide a comprehensive measurement of their serum metabolomes. This work provides an important baseline or reference dataset for understanding the 'normal' relative concentrations and variation in the human serum metabolome. These may be related to our increasing knowledge of the human metabolic network map. Information on the Husermet study is available at http://www.husermet.org/. Importantly, all of the data are made freely available at MetaboLights (http://www.ebi.ac.uk/metabolights/).

No MeSH data available.


Heatmap with dendrogram of correlation network for metabolites detected by GC–MS. The twenty unique metabolites with one or more of the highest correlations are depicted. The lower bar represents the colour code of coefficients from pairwise Pearson’s correlations (Color figure online)
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Fig2: Heatmap with dendrogram of correlation network for metabolites detected by GC–MS. The twenty unique metabolites with one or more of the highest correlations are depicted. The lower bar represents the colour code of coefficients from pairwise Pearson’s correlations (Color figure online)

Mentions: Metabolites do not operate in isolation but through a complex network of interactions, with metabolism being one network, though other networks are observed in biological systems (Camacho et al. 2005), especially through correlation of non-neighbour metabolites indicating their involvement in regulatory pathways [see e.g. Kotze et al. (2013)]. We note also that as reported in Camacho et al. (2005) without clear metabolite linkage, correlations should be treated with caution as correlation does not necessarily equate to causation. To highlight these complex networks we illustrate the 20 metabolites for GC–MS that show the highest pairwise Pearson’s correlations. Where a metabolite was detected as more than one ‘metabolic feature’, only one ‘feature’ has been included in Fig. 2, the feature with the higher correlation coefficient. The data show the expected correlations between leucine and valine (both involved in branched chain amino acid metabolism) and between different fatty acids and glycerol (related to glycerolipid and glycerophospholipid metabolism). However, and unexpectedly, proline was also correlated with leucine and valine, and phosphate with fatty acids. Assessing the UPLC–MS data (Supplementary Fig. 2) we detected expected correlations between fatty acids and oxidized fatty acids, between different sphingolipids, between fatty acids and sphingolipids, between different lyso-glycerophospholipids, between different diacylglycerides and between diacylglycerides and sphingolipids.Fig. 2


Molecular phenotyping of a UK population: defining the human serum metabolome.

Dunn WB, Lin W, Broadhurst D, Begley P, Brown M, Zelena E, Vaughan AA, Halsall A, Harding N, Knowles JD, Francis-McIntyre S, Tseng A, Ellis DI, O'Hagan S, Aarons G, Benjamin B, Chew-Graham S, Moseley C, Potter P, Winder CL, Potts C, Thornton P, McWhirter C, Zubair M, Pan M, Burns A, Cruickshank JK, Jayson GC, Purandare N, Wu FC, Finn JD, Haselden JN, Nicholls AW, Wilson ID, Goodacre R, Kell DB - Metabolomics (2014)

Heatmap with dendrogram of correlation network for metabolites detected by GC–MS. The twenty unique metabolites with one or more of the highest correlations are depicted. The lower bar represents the colour code of coefficients from pairwise Pearson’s correlations (Color figure online)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4289517&req=5

Fig2: Heatmap with dendrogram of correlation network for metabolites detected by GC–MS. The twenty unique metabolites with one or more of the highest correlations are depicted. The lower bar represents the colour code of coefficients from pairwise Pearson’s correlations (Color figure online)
Mentions: Metabolites do not operate in isolation but through a complex network of interactions, with metabolism being one network, though other networks are observed in biological systems (Camacho et al. 2005), especially through correlation of non-neighbour metabolites indicating their involvement in regulatory pathways [see e.g. Kotze et al. (2013)]. We note also that as reported in Camacho et al. (2005) without clear metabolite linkage, correlations should be treated with caution as correlation does not necessarily equate to causation. To highlight these complex networks we illustrate the 20 metabolites for GC–MS that show the highest pairwise Pearson’s correlations. Where a metabolite was detected as more than one ‘metabolic feature’, only one ‘feature’ has been included in Fig. 2, the feature with the higher correlation coefficient. The data show the expected correlations between leucine and valine (both involved in branched chain amino acid metabolism) and between different fatty acids and glycerol (related to glycerolipid and glycerophospholipid metabolism). However, and unexpectedly, proline was also correlated with leucine and valine, and phosphate with fatty acids. Assessing the UPLC–MS data (Supplementary Fig. 2) we detected expected correlations between fatty acids and oxidized fatty acids, between different sphingolipids, between fatty acids and sphingolipids, between different lyso-glycerophospholipids, between different diacylglycerides and between diacylglycerides and sphingolipids.Fig. 2

Bottom Line: Overall, this is a large scale and non-targeted chromatographic MS-based metabolomics study, using samples from over 1,000 individuals, to provide a comprehensive measurement of their serum metabolomes.This work provides an important baseline or reference dataset for understanding the 'normal' relative concentrations and variation in the human serum metabolome.These may be related to our increasing knowledge of the human metabolic network map.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Engineering and Physical Sciences, School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN UK ; Faculty of Engineering & Physical Sciences, Manchester Centre for Integrative Systems Biology, Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN UK ; Faculty of Medical and Human Sciences, Centre for Endocrinology and Diabetes, Institute of Human Development, The University of Manchester, Manchester, UK ; Centre for Advanced Discovery and Experimental Therapeutics (CADET), Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, M13 9WL UK ; School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.

ABSTRACT

Phenotyping of 1,200 'healthy' adults from the UK has been performed through the investigation of diverse classes of hydrophilic and lipophilic metabolites present in serum by applying a series of chromatography-mass spectrometry platforms. These data were made robust to instrumental drift by numerical correction; this was prerequisite to allow detection of subtle metabolic differences. The variation in observed metabolite relative concentrations between the 1,200 subjects ranged from less than 5 % to more than 200 %. Variations in metabolites could be related to differences in gender, age, BMI, blood pressure, and smoking. Investigations suggest that a sample size of 600 subjects is both necessary and sufficient for robust analysis of these data. Overall, this is a large scale and non-targeted chromatographic MS-based metabolomics study, using samples from over 1,000 individuals, to provide a comprehensive measurement of their serum metabolomes. This work provides an important baseline or reference dataset for understanding the 'normal' relative concentrations and variation in the human serum metabolome. These may be related to our increasing knowledge of the human metabolic network map. Information on the Husermet study is available at http://www.husermet.org/. Importantly, all of the data are made freely available at MetaboLights (http://www.ebi.ac.uk/metabolights/).

No MeSH data available.