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Reporter: Aviva Lev-Ari, PhD, RN 

Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information

Jan Krumsiek1, Karsten Suhre1,2, Anne M. Evans3, Matthew W. Mitchell3, Robert P. Mohney3, Michael V. Milburn3, Brigitte Wägele1,4, Werner Römisch-Margl1, Thomas Illig5,6, Jerzy Adamski7,8, Christian Gieger9, Fabian J. Theis1,10, Gabi Kastenmüller1*


1 Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany, 2 Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City, Qatar Foundation, Doha, Qatar, 3 Metabolon, Research Triangle Park, North Carolina, United States of America, 4 Department of Genome-Oriented Bioinformatics, Life and Food Science Center Weihenstephan, Technische Universität München, Freising, Germany, 5 Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany, 6 Biobank of the Hanover Medical School, Hanover Medical School, Hanover, Germany, 7 Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, Neuherberg, Germany, 8 Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising-Weihenstephan, Germany, 9 Institute of Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany, 10 Department of Mathematics, Technische Universität München, Garching, Germany


Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these unknown metabolites is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype–metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.


Recently, genome-wide association studies (GWAS) on metabolic quantitative traits have proven valuable tools to uncover the genetically determined metabolic individuality in the general population [1][5]. Interestingly, a great portion of the genetic loci that were found to significantly associate with levels of specific metabolites are within or in close proximity to metabolic enzymes or transporters with known disease or pharmaceutical relevance. Moreover, compared to GWAS with clinical endpoints the effect sizes of the genotypes are exceptionally high.

The number and type of the metabolic features that went into these GWAS was mainly defined by the metabolomics techniques used: Gieger et al. [1] and Illig et al. [2] used a targeted mass spectrometry (MS)-based approach giving access to the concentrations of 363 and 163 metabolites, respectively. Suhre et al. [3] and Nicholson et al. [4] applied untargeted nuclear magnetic resonance (NMR) based metabolomics techniques, yielding 59 metabolites that had been identified in the spectra prior to the GWAS and 579 manually selected peaks from the spectra, respectively. In Suhre et al. [5], 276 metabolites from an untargeted MS-based approach were analyzed.

While these previous GWAS focused on metabolic features with known identity, untargeted metabolomics approaches additionally provide quantifications of so-called “unknown metabolites”. An unknown metabolite is a small molecule that can reproducibly be detected and quantified in a metabolomics experiment, but whose chemical identity has not been elucidated yet. In an experiment using liquid chromatography (LC) coupled to MS, such an unknown would be defined by a specific retention time, one or multiple masses (e.g. from adducts), and a characteristic fragmentation pattern of the primary ion(s). An unknown observed by NMR spectroscopy would correspond to a pattern in the chemical shifts. Unknowns may constitute previously undocumented small molecules, such as rare xenobiotics or secondary products of metabolism, or they may represent molecules from established pathways which could not be assigned using current libraries of MS fragmentation patterns [6], [7] or NMR reference spectra [8].

The impact of unknown metabolites for biomedical research has been shown in recent metabolomics-based discovery studies of novel biomarkers for diseases and various disease-causing conditions. This includes studies investigating altered metabolite levels in blood for insulin resistance [9], type 2 diabetes [10], and heart disorders [11]. A considerable number of high-ranking hits reported in these biomarker studies represent unknown metabolites. As long as their chemical identities are not clarified the usability of unknown metabolites as functional biomarkers for further investigations and clinical applications is rather limited.

In mass-spectrometry-based metabolomics approaches, the assignment of chemical identity usually involves the interpretation and comparison of experiment-specific parameters, such as accurate masses, isotope distributions, fragmentation patterns, and chromatography retention times [12][14]. Various computer-based methods have been developed to automate this process. For example, Rasche and colleagues [15] elucidated structural information of unknown metabolites in a mass-spectrometry setup using a graph-theoretical approach. Their approach attempts to reconstruct the underlying fragmentation tree based on mass-spectra at varying collision energies. Other authors excluded false candidates for a given unknown by comparing observed and predicted chromatography retention times [16], [17], or by the automatic determination of sum formulas from isotope distributions [18]. Furthermore, Gipson et al. [19] and Weber et al. [20] integrated public metabolic pathway information with correlating peak pairs in order to facilitate metabolite identification. However, these methods might not be applicable for high-throughput metabolomics datasets that have been produced in a fee-for-service manner, since the mass spectra as such might not be readily available.

Approaching the problem from a conceptually different perspective, we here present a novel functional metabolomics method to predict the identities of unknown metabolites using a systems biological framework. By combining high-throughput genotyping data, metabolomics data, and literature-derived metabolic pathway information, we generate testable hypotheses on the metabolite identities based solely on the obtained metabolite quantifications (Figure 1). No further experiment-specific data such as retention times, isotope patterns and fragmentation patterns are required for this analysis.


Figure 1. Data integration workflow for the systematic classification of unknown metabolites.

We combine high-throughput metabolomics and genotyping data in Gaussian graphical models (GGMs) [21] and in genome-wide association studies (GWAS) [5] in order to produce testable predictions of the unknown metabolites’ identities. These hypotheses are then subject to experimental verification by mass-spectrometry. Six such cases have been fully worked through and are presented in Table 3. doi:10.1371/journal.pgen.1003005.g001




We developed and validated a novel integrative approach for the biochemical characterization of “unknown metabolites” from high-throughput metabolomics and genotyping datasets. Our method allows for the functional annotation of previously unidentified metabolites and, as a consequence, enhances the interpretability of metabolomics data in genome-wide association studies and biomarker discovery. For the first time, we systematically evaluated genetic associations of unknown metabolites, thereby discovering seven new loci of metabolic individuality. By classifying a series of unknown metabolites, we gained new insights into the functional interplay between genetic variation and the metabolome both for previously reported and new loci. Furthermore, several of the unknown compounds that we identified as well as their newly associated loci were independently reported in disease-related studies. In the following, we discuss three genetic loci and their associated phenotypes.

COMT and hepatic detoxification

The first example is a recent biomarker study, where Milburn et al. [34] reported an association of X-11593 with hepatic detoxification. In our GWAS, we find a strong association of X-11593 with the COMT locus, which encodes the catechol-O-methyltransferase enzyme. COMT is responsible for the inactivation of catecholamines such as L-dopa and various neuroactive drugs by O-methylation [35]. Following our identification approach, we experimentally confirmed the identity of X-11593 as O-methylascorbate. Notably, O-methylascorbate is a known product of ascorbate (vitamin C) O-methylation by COMT [36], [37]. Thus, our observations establish a link between O-methylascorbate blood levels, common genetic variation in the COMT locus and COMT-mediated liver detoxification processes.

ACE and hypertension

The second example relates to the ACE gene locus, which is a known risk locus for cardiovascular disease, hypertension and kidney failure. The protein encoded by the ACE locus, angiotensin-converting enzyme, is an exopeptidase which cleaves dipeptides from vasoactive oligopeptides, and plays a central role in the blood pressure-controlling renin-angiotensin system [38]. Moreover, the ACE protein is a target for various pharmaceuticals (ACE inhibitors), especially in the treatment of hypertension [39]. In our study, we identified three unknowns as dipeptides (X-14205, X-14208 and X-14478), two of which also associated with the ACE locus. These dipeptides could thus represent novel, interesting biomarkers for the activity of ACE. Moreover, Steffens et al. [11] reported a connection between heart failure and X-11805, which is in close proximity to angiontensin-related peptides in the GGM. This connection might be revisited after a successful identification of X-11805 in a future study.

UGT1A/ACADM and insulin resistance

The third example is an explorative study to detect biomarkers for insulin sensitivity. Gall et al. [9] reported several known metabolites (most prominently α-hydroxybutyrate) as biomarkers for insulin resistance. They also reported a series of unknown metabolites among their top hits. In the present study, we investigated three of these unknowns: X-11793 associates with UGT1A (UDP glucuronosyltransferase 1) and represents a bilirubin-related substance. Moreover, we experimentally validated X-11421 and X-13431, which display a strong association with ACADM (acyl-Coenzyme A dehydrogenase, C-4 to C-12 straight chain), as acylcarnitines containing 10 and 9 carbon atoms, respectively. The identification of these latter two unknown metabolites as medium-chain length acylcarnitines is coherent with reports by Adams et al. [40]. The authors found elevated blood plasma acylcarnitine levels in women with type 2 diabetes. Functionally, they attributed this finding to incomplete β-oxidation. Thus, our identification of X-11421 and X-13431 now suggests incomplete β-oxidation as an explanation for the associations found by Gall et al. and implies that acylcarnitines containing 10 and 9 carbon atoms are potential biomarkers for insulin resistance.


In summary, we integrated high-throughput metabolomics and genotyping data from a large population cohort for elucidating the biochemical identities of unknown metabolites. To this end, we applied metabolomics genome-wide association studies and Gaussian graphical modeling in order to link these unknown metabolites with known metabolic classes and biological processes. For six specific scenarios, we went from systematic hypothesis generation over detailed investigation and identity prediction to direct experimental confirmation. Similar validations may now be undertaken for the remaining predictions that we report in Table S1. Finally, we demonstrated the benefit of our method by discussing several of these newly identified metabolites in the context of existing biomarker discovery studies on liver detoxification, hypertension and insulin resistance.

It is to be noted that our method does not specifically require genotyping data. Even metabolomics measurements alone, analyzed through the GGMs, may provide sufficient information for the classification and even precise identity prediction. The unknowns with GGM evidence but without GWAS hits in Figure 4 as well as the HETE scenario represent examples for this approach.

One limitation of our approach is the requirement for associations with functionally described loci or known metabolites. Certain metabolite groups might thus systematically not be identifiable. For instance, if the identity of a whole class of biochemically related molecules is unknown (which might be due to experimental reasons), then the GGM associations between those compounds will not aid in identity elucidation. The 118 unknown compounds for which we could not derive any classification might represent such cases. Thus, our functionally oriented method should be regarded as a complementary extension to the existing identity determination methods.

Accordingly, our approach can be extended in several directions. It can be combined with method-specific, automated techniques that further exclude sets of metabolites. Previously mentioned methods relying on mass-spectra [15] or chromatographic properties [17] are suitable candidates here. Moreover, the method can be directly transferred to other types of metabolomics datasets not specifically originating from MS experiments, such as NMR-based metabolomics.

Beyond the application to metabolite identification, our study demonstrates the general potential of functional metabolomics in the context of genome-wide association studies. The comprehensive metabolic picture provided by GGMs in combination with GWAS allows for the detailed analysis of metabolic functions, chemical classes, enzyme-metabolite relationships and metabolic pathways.

Author Contributions 

Conceived and designed the experiments: JK KS FJT GK. Performed the experiments: AME MWM RPM MVM. Analyzed the data: JK GK. Contributed reagents/materials/analysis tools: BW WR-M TI JA CG. Wrote the paper: JK KS FJT GK.


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Reporter: Aviva Lev-Ari, PhD, RN


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