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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.


The distribution of relative standard deviations defining the inter-subject variability in metabolite relative concentrations for each analytical platform applied, following signal correction. The data are shown as distribution plots. Top plot GC–MS, middle plot UPLC–MS(−), bottom plot UPLC–MS(+)
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Fig1: The distribution of relative standard deviations defining the inter-subject variability in metabolite relative concentrations for each analytical platform applied, following signal correction. The data are shown as distribution plots. Top plot GC–MS, middle plot UPLC–MS(−), bottom plot UPLC–MS(+)

Mentions: The relative concentrations of metabolites were investigated to derive the cumulative variation associated with background/baseline genetic and environmental influences. The distribution of variation associated with inter-subject variability [as calculated as the relative standard deviation (RSD)] for all 1,200 subjects following signal correction) is shown in Fig. 1. The distribution is skewed to lower RSD values; one interpretation of this is that the serum metabolome is comparatively tightly regulated in “healthy” populations (i.e., subjects with no diagnosed disease at the time of sampling). This could reasonably be expected, with a greater variation observed in the human urine metabolome (Bouatra et al. 2013), a biofluid composed of metabolites that are being excreted from the body. Of course, if the inter-subject variability is equivalent to the technical variability measured by replicate analysis of the same quality control (QC) sample then the metabolite feature contains no biological information. For GC–MS, UPLC–MS(+) and UPLC–MS(−), respectively, 7 of 126, 71 of 2,181 and 42 of 2,283 metabolic features were observed to have an inter-subject RSD/QC RSD <1.5; thus the overwhelming majority of metabolite features reported contain biological information and those metabolite features with a value less than 1.5 were removed from further analyses.Fig. 1


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)

The distribution of relative standard deviations defining the inter-subject variability in metabolite relative concentrations for each analytical platform applied, following signal correction. The data are shown as distribution plots. Top plot GC–MS, middle plot UPLC–MS(−), bottom plot UPLC–MS(+)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: The distribution of relative standard deviations defining the inter-subject variability in metabolite relative concentrations for each analytical platform applied, following signal correction. The data are shown as distribution plots. Top plot GC–MS, middle plot UPLC–MS(−), bottom plot UPLC–MS(+)
Mentions: The relative concentrations of metabolites were investigated to derive the cumulative variation associated with background/baseline genetic and environmental influences. The distribution of variation associated with inter-subject variability [as calculated as the relative standard deviation (RSD)] for all 1,200 subjects following signal correction) is shown in Fig. 1. The distribution is skewed to lower RSD values; one interpretation of this is that the serum metabolome is comparatively tightly regulated in “healthy” populations (i.e., subjects with no diagnosed disease at the time of sampling). This could reasonably be expected, with a greater variation observed in the human urine metabolome (Bouatra et al. 2013), a biofluid composed of metabolites that are being excreted from the body. Of course, if the inter-subject variability is equivalent to the technical variability measured by replicate analysis of the same quality control (QC) sample then the metabolite feature contains no biological information. For GC–MS, UPLC–MS(+) and UPLC–MS(−), respectively, 7 of 126, 71 of 2,181 and 42 of 2,283 metabolic features were observed to have an inter-subject RSD/QC RSD <1.5; thus the overwhelming majority of metabolite features reported contain biological information and those metabolite features with a value less than 1.5 were removed from further analyses.Fig. 1

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.