A description of large-scale metabolomics studies: increasing value by combining metabolomics with genome-wide SNP genotyping and transcriptional profiling

Homuth, Georg; Teumer, Alexander; Völker, Uwe; Nauck, Matthias

doi:10.1530/joe-12-0144

Cited by 24 publications

(23 citation statements)

References 39 publications

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“…Thus, it can be concluded that even if not cell-specific, the signals derived from whole blood data still reflect organism-wide processes. This is also in line with previous studies conducted on whole blood transcriptomics or metabolomics data separately [1,30,31]. …”

Section: Introductionsupporting

confidence: 92%

The Human Blood Metabolome-Transcriptome Interface

et al. 2015

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Biological systems consist of multiple organizational levels all densely interacting with each other to ensure function and flexibility of the system. Simultaneous analysis of cross-sectional multi-omics data from large population studies is a powerful tool to comprehensively characterize the underlying molecular mechanisms on a physiological scale. In this study, we systematically analyzed the relationship between fasting serum metabolomics and whole blood transcriptomics data from 712 individuals of the German KORA F4 cohort. Correlation-based analysis identified 1,109 significant associations between 522 transcripts and 114 metabolites summarized in an integrated network, the ‘human blood metabolome-transcriptome interface’ (BMTI). Bidirectional causality analysis using Mendelian randomization did not yield any statistically significant causal associations between transcripts and metabolites. A knowledge-based interpretation and integration with a genome-scale human metabolic reconstruction revealed systematic signatures of signaling, transport and metabolic processes, i.e. metabolic reactions mainly belonging to lipid, energy and amino acid metabolism. Moreover, the construction of a network based on functional categories illustrated the cross-talk between the biological layers at a pathway level. Using a transcription factor binding site enrichment analysis, this pathway cross-talk was further confirmed at a regulatory level. Finally, we demonstrated how the constructed networks can be used to gain novel insights into molecular mechanisms associated to intermediate clinical traits. Overall, our results demonstrate the utility of a multi-omics integrative approach to understand the molecular mechanisms underlying both normal physiology and disease.

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Section: Introductionsupporting

confidence: 92%

The Human Blood Metabolome-Transcriptome Interface

et al. 2015

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“…These studies usually focus on organismal phenotypes [2]–[6]. Recently however, molecular phenotypes, including gene-expression [7], [8] and metabotypes [9]–[14], have also been investigated. Studying the effects of genetic variations on molecular phenotypes is motivated by two characteristics common to the vast majority of GWAS on organismal phenotypes: first, the biological mechanisms underlying the associations are often unknown; and second, the significantly associated loci individually explain only a small fraction of variability of the organismal phenotype, and even cumulatively fall far from explaining the estimated heritability of the phenotype [15].…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Association Study of Metabolic Traits Reveals Novel Gene-Metabolite-Disease Links

et al. 2014

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Metabolic traits are molecular phenotypes that can drive clinical phenotypes and may predict disease progression. Here, we report results from a metabolome- and genome-wide association study on 1H-NMR urine metabolic profiles. The study was conducted within an untargeted approach, employing a novel method for compound identification. From our discovery cohort of 835 Caucasian individuals who participated in the CoLaus study, we identified 139 suggestively significant (P<5×10−8) and independent associations between single nucleotide polymorphisms (SNP) and metabolome features. Fifty-six of these associations replicated in the TasteSensomics cohort, comprising 601 individuals from São Paulo of vastly diverse ethnic background. They correspond to eleven gene-metabolite associations, six of which had been previously identified in the urine metabolome and three in the serum metabolome. Our key novel findings are the associations of two SNPs with NMR spectral signatures pointing to fucose (rs492602, P = 6.9×10−44) and lysine (rs8101881, P = 1.2×10−33), respectively. Fine-mapping of the first locus pinpointed the FUT2 gene, which encodes a fucosyltransferase enzyme and has previously been associated with Crohn's disease. This implicates fucose as a potential prognostic disease marker, for which there is already published evidence from a mouse model. The second SNP lies within the SLC7A9 gene, rare mutations of which have been linked to severe kidney damage. The replication of previous associations and our new discoveries demonstrate the potential of untargeted metabolomics GWAS to robustly identify molecular disease markers.

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“…Future technology development combined with more robust data analysis and bioinformatic tools will help overcome current limitations and fully integrate small molecule biochemistry with systems biology. Because metabolomics is complementary to genomics, transcriptomics, and proteomics, the full integration of these fields is the basis of systems biology, and only their combined application will enable for a full description of a living system at all biomolecular levels (Homuth et al, 2012;Lindon et al, 2006;Richards et al, 2010).…”

Section: Comparison Of Metabolomics With the Other ''Omics''mentioning

confidence: 99%

Postgenomics Diagnostics: Metabolomics Approaches to Human Blood Profiling

Trifonova

Lokhov

Archakov

2013

OMICS: A Journal of Integrative Biology

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We live in exciting times with the prospects of postgenomics diagnostics. Metabolomics is a novel ''omics'' dataintensive science that is accelerating the development of postgenomics diagnostics, particularly with use of accessible peripheral tissue compartments. Metabolomics involves the study of a comprehensive set of low molecular weight substances (metabolites) present in biological systems. The metabolite profiles represent the molecular phenotype of biological systems and reflect the information encoded at the genomic level and implemented at the transcriptomic and proteomic levels. Analysis of the human blood metabolite profile is a universal and highly promising tool for clinical postgenomics applications because it reflects both the endogenous and exogenous (environmental) factors influencing an individual organism. This article presents a critical synthesis and original analysis of both the technical implementation of metabolic profiling of blood and statistical analysis of metabolite profiles for effective disease diagnostics and risk assessment in the present postgenomics era.

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A description of large-scale metabolomics studies: increasing value by combining metabolomics with genome-wide SNP genotyping and transcriptional profiling

Cited by 24 publications

References 39 publications

The Human Blood Metabolome-Transcriptome Interface

The Human Blood Metabolome-Transcriptome Interface

Genome-Wide Association Study of Metabolic Traits Reveals Novel Gene-Metabolite-Disease Links

Postgenomics Diagnostics: Metabolomics Approaches to Human Blood Profiling

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