Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information
Open accessAbstract
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.
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In both targeted and untargeted methods, even for confidently identified compounds, the biological processes that generated them are often poorly understood.
“In both targeted and untargeted methods, even for confidently identified compounds, the biological processes that generated them are often poorly understood122–125.”
From Measuring biological age using omics data (2022) · cites this paper as [125]
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Self-citationGender-specific pathway differences in the human serum metabolome
Krumsiek, J., Mittelstraß, K., Do, K. T., Stückler, F., Ried, J. S., Adamski, J., Peters, A., Illig, T., Kronenberg, F., Friedrich, N., et al. (2015) · Metabolomics · doi:10.1007/s11306-015-0829-0 · Cited by 293 · see citing works
“Moreover, genetic variation in the GGT1 enzyme induces concentration changes in the gamma-glutamyl dipeptides (Krumsiek et al. 2012).”
BackgroundSelf-citationBasu, S., Duren, W. L., Evans, C. R., Burant, C., Michailidis, G. C., & Karnovsky, A. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx012 · Cited by 267 · see citing works
Metabolite-based genome-wide association studies in plants
Luo, J. (2015) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2015.01.006 · Cited by 263 · see citing works
“Integrating the data from genetic associations, metabolic networks and knowledge-based pathway information allowed deduction of the biochemical identities of 106 unknown metabolites in humans [55].”
MethodLi, C., Stražar, M., Mohamed, A. M., Ávila-Pacheco, J., Walker, R. L., Lebar, T., Zhao, S., Lockart, J., Dame, A., Thurimella, K., et al. (2024) · Cell · doi:10.1016/j.cell.2024.03.014 · Cited by 260 · see citing works
Computational Metabolomics: A Framework for the Million Metabolome
Uppal, K., Walker, D. I., Liu, K., Li, S., Go, Y., & Jones, D. P. (2016) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.6b00179 · Cited by 241 · see citing works
“69 Other studies have used integrative methods based on partial least-squares regression (PLS) to determine correlations between the metabolome and the transcriptome, 70 proteome, 71 and microbiome.”
Pharmacogenomics: Driving Personalized Medicine
Sadée, W., Wang, D., Hartmann, K., & Toland, A. E. (2023) · Pharmacological Reviews · doi:10.1124/pharmrev.122.000810 · Cited by 251 · see citing works
A metabolome-wide association study of kidney function and disease in the general population
Sekula, P., Goek, O.-N., Quaye, L., Barrios, C., Levey, A. S., Römisch-Margl, W., Menni, C., Yet, I., Gieger, C., Inker, L. A., et al. (2016) · Journal of the American Society of Nephrology · doi:10.1681/asn.2014111099 · Cited by 203 · see citing works
Self-citationGenetic variation in metabolic phenotypes: study designs and applications
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Self-citationPetersen, A.-K., Zeilinger, S., Kastenmüller, G., Römisch-Margl, W., Brugger, M., Peters, A., Meisinger, C., Strauch, K., Hengstenberg, C., Pagel, P., et al. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddt430 · Cited by 183 · see citing works
“(15) for non-annotated (unknown) metabolites from the Metabolon platform.”
BackgroundSelf-citationTzoulaki, I., Ebbels, T. M. D., Valdes, A. M., Elliott, P., & Ioannidis, J. P. A. (2014) · American Journal of Epidemiology · doi:10.1093/aje/kwu143 · Cited by 183 · see citing works
“(43) have also provided an alternative strategy that integrates genome-wide association data, Gaussian graphical modeling, and metabolomics to predict the identity of unknown signals.”
Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations
Panyard, D. J., Kim, K. M., Darst, B. F., Deming, Y. K., Zhong, X., Wu, Y., Kang, H., Carlsson, C. M., Johnson, S. C., Asthana, S., et al. (2021) · Communications Biology · doi:10.1038/s42003-020-01583-z · Cited by 179 · see citing works
Revealing disease-associated pathways by network integration of untargeted metabolomics
Pirhaji, L., Milani, P., Leidl, M., Curran, T., Avila-Pacheco, J., Clish, C. B., White, F. M., Saghatelian, A., & Fraenkel, E. (2016) · Nature Methods · doi:10.1038/nmeth.3940 · Cited by 167 · see citing works
Rare and common genetic determinants of metabolic individuality and their effects on human health
Surendran, P., Stewart, I. D., Auyeung, V. P. W., Pietzner, M., Raffler, J., Wörheide, M. A., Li, C., Smith, R. F., Wittemans, L. B. L., Bomba, L., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-02046-0 · Cited by 150 · see citing works
Self-citationAssessing the Causal Effects of Human Serum Metabolites on 5 Major Psychiatric Disorders
Yang, J., Yan, B., Zhao, B., Fan, Y., He, X., Yang, L., Ma, Q., Zheng, J., Wang, W., Bai, L., et al. (2019) · Schizophrenia Bulletin · doi:10.1093/schbul/sbz138 · Cited by 143 · see citing works
Dimethylguanidino valeric acid is a marker of liver fat and predicts diabetes
O’Sullivan, J., Morningstar, J. E., Yang, Q., Zheng, B., Gao, Y. L., Jeanfavre, S., Scott, J. M., Fernandez, C., Zheng, H., O’Connor, S., et al. (2017) · Journal of Clinical Investigation · doi:10.1172/jci95995 · Cited by 136 · see citing works
“A recent multistep approach (37) used a GWAS of known and unknown metabolites and correlation analysis of known metabolites with unknown ones, and then used published metabolic pathway information to guide the construction of networks that displayed these relationships as a function of metabolite class.”
MethodResultInferring Interaction Networks From Multi-Omics Data
Hawe, J. S., Theis, F. J., & Heinig, M. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00535 · Cited by 139 · see citing works
Self-citationGenetics of human metabolism: an update
Kastenmüller, G., Raffler, J., Gieger, C., & Suhre, K. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv263 · Cited by 128 · see citing works
“eQTLs) (23,25), by linking individual associations in a systems level approach (16,46), and by adding clinical association data to establish complex gene-to-disease networks (for an example, see Fig.”
BackgroundSelf-citationMetabolomics and Its Application to Acute Lung Diseases
Stringer, K. A., McKay, R. T., Karnovsky, A., Quémerais, B., & Lacy, P. (2016) · Frontiers in Immunology · doi:10.3389/fimmu.2016.00044 · Cited by 133 · see citing works
Fontes, P., López, R., van der Plaats, A., Yoram Vodovotz, B., Minervini, M. I., Scott, V., Soltys, K. A., Shiva, S., Paranjpe, S., Sadowsky, D., et al. (2015) · American Journal of Transplantation · doi:10.1111/ajt.12991 · Cited by 129 · see citing works
Systems biology in hepatology: approaches and applications
Mardinoğlu, A., Borén, J., Smith, U., Uhlén, M., & Nielsen, J. (2018) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-018-0007-8 · Cited by 123 · see citing works
González‐Domínguez, R., García, A., García‐Barrera, T., Barbas, C., & Gómez‐Ariza, J. L. (2014) · ELECTROPHORESIS · doi:10.1002/elps.201400196 · Cited by 119 · see citing works
Aichler, M., Borgmann, D. M., Krumsiek, J., Buck, A., MacDonald, P. E., Fox, J. E. M., Lyon, J. G., Light, P. E., Keipert, S., Jastroch, M., et al. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.04.012 · Cited by 115 · see citing works
Self-citationHolo-omics for deciphering plant-microbiome interactions
Xu, L., Pierroz, G., Wipf, H. M. ‐., Gao, C. D., Taylor, J. W., Lemaux, P. G., & Coleman‐Derr, D. (2021) · Microbiome · doi:10.1186/s40168-021-01014-z · Cited by 112 · see citing works
Yousri, N. A., Mook-Kanamori, D. O., Selim, M. M. E.-D., Takiddin, A. H., Al-Homsi, H., Al-Mahmoud, K. A. S., Karoly, E. D., Krumsiek, J., Thinh, K., Neumaier, U., et al. (2015) · Diabetologia · doi:10.1007/s00125-015-3636-2 · Cited by 106 · see citing works
“Metabolite–metabolite interactions in these GGMs are identified by partial correlations between the measured metabolites; they have been shown to correspond to known biochemical interactions that can be used for reconstructing metabolic networks from data alone [16, 17].”
BackgroundMethodSelf-citationThe Human Blood Metabolome-Transcriptome Interface
Bartel, J., Krumsiek, J., Schramm, K., Adamski, J., Gieger, C., Herder, C., Carstensen, M., Peters, A., Rathmann, W., Roden, M., et al. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005274 · Cited by 103 · see citing works
“A total of 224 candidate SNPs reported as lead association signals at genome-wide significance in two recent GWAS studies for 16 metabolites and 186 mRNAs (BMTI contained) were preselected as instrumental variables[23,59].”
BackgroundMethodInfluentialSelf-citationFloegel, A., Wientzek, A., Bachlechner, U., Jacobs, S., Drogan, D., Prehn, C. P., Adamski, J., Krumsiek, J., Schulze, M. B., Pischon, T., et al. (2014) · International Journal of Obesity · doi:10.1038/ijo.2014.39 · Cited by 105 · see citing works
Self-citationNew Advances in Tissue Metabolomics: A Review
Saoi, M., & Britz‐McKibbin, P. (2021) · Metabolites · doi:10.3390/metabo11100672 · Cited by 105 · see citing works
“Additional methods to support metabolite identification include functional group/chemical reactivity, enzymatic transformations, correlation analysis to other known metabolites, as well as integration with genetic data [116].”
MethodGenetic Determinants Influencing Human Serum Metabolome among African Americans
Yu, B., Zheng, Y., Alexander, D. C., Morrison, A. C., Coresh, J., & Boerwinkle, E. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004212 · Cited by 102 · see citing works
“A recent study has used GWAS results and Gaussian graphical modeling to predict unknown metabolite identities [38].”
MethodRaffler, J., Friedrich, N., Arnold, M., Kacprowski, T., Rueedi, R., Altmaier, E., Bergmann, S., Budde, K., Gieger, C., Homuth, G., et al. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005487 · Cited by 101 · see citing works
“In previous mGWAS, we demonstrated the potential of testing pairwise ratios of metabolite concentrations to boost genetic association signals [1, 4, 6, 8, 10, 21, 53].”
BackgroundMethodSelf-citationMoon, J., Zolnik, C. P., Wang, Z., Qiu, Y., Usyk, M., Wang, T., Kizer, J. R., LANDAY, A. L., Kurland, I. J., Anastos, K., et al. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2018.10.037 · Cited by 94 · see citing works
“This targeted method simultaneously measures 188 metabolites with liquid chromatography and flow injection analysis-mass spectrometry, and the detailed assay procedures have been described previously [33,34].”
MethodMetabolic profiling in diabetes
Suhre, K. (2014) · Journal of Endocrinology · doi:10.1530/joe-14-0024 · Cited by 94 · see citing works
Self-citationThe landscape of metabolic brain alterations in Alzheimer's disease
Batra, R., Arnold, M., Wörheide, M. A., Allen, M., Wang, X., Blach, C., Levey, A. I., Seyfried, N. T., Ertekin‐Taner, N., Bennett, D. A., et al. (2022) · Alzheimer s & Dementia · doi:10.1002/alz.12714 · Cited by 88 · see citing works
Self-citationGut microbiome-mediated metabolism effects on immunity in rural and urban African populations
Stražar, M., Temba, G. S., Vlamakis, H., Kullaya, V. I., Lyamuya, F., Mmbaga, B. T., Joosten, L. A. B., van der Ven, A. J. A. M., Netea, M. G., de Mast, Q., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25213-2 · Cited by 91 · see citing works
The evolution of genome-scale models of cancer metabolism
Lewis, N. E., & Abdel-Haleem, A. M. (2013) · Frontiers in Physiology · doi:10.3389/fphys.2013.00237 · Cited by 87 · see citing works
“Some successes have relied on complex statistical methods to find loci that co-vary with metabolites (Kettunen et al., 2012; Krumsiek et al., 2012).”
BackgroundGenome-Wide Association Study of Metabolic Traits Reveals Novel Gene-Metabolite-Disease Links
Rueedi, R., Ledda, M., Nicholls, A. W., Salek, R. M., Marques-Vidal, P. M., Morya, E., Sameshima, K., Montoliu, I., Silva, L. D., Collino, S., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004132 · Cited by 88 · see citing works
“[47], of ‘‘a molecule which can reproducibly be detected and quantified [.”
BackgroundGaussian and Mixed Graphical Models as (multi-)omics data analysis tools
Altenbuchinger, M., Weihs, A., Quackenbush, J. F., Grabe, H. J., & Zacharias, H. U. (2019) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2019.194418 · Cited by 87 · see citing works
“To facilitate the identification of yet unidentified metabolites from untargeted metabolomic measurements in a large-scale population based study, Krumsiek et al. (2012) combined a genome-wide association analysis of 655,658 genotyped single-nucleotide polymorphisms (SNPs) on concentrations of…”
BackgroundHalama, A. (2014) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2014.09.002 · Cited by 82 · see citing works
Integrated Metabolomics and Genomics
Shah, S. H., & Newgard, C. B. (2015) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.114.000223 · Cited by 81 · see citing works
Recent Progress in the Development of Metabolome Databases for Plant Systems Biology
Fukushima, A., & Kusano, M. (2013) · Frontiers in Plant Science · doi:10.3389/fpls.2013.00073 · Cited by 82 · see citing works
“Krumsiek et al. (2012) demonstrated that the integration of metabolite profiling with genome-wide association studies (GWAS) on metabolic quantitative traits is very useful for deriving biochemical pathways for unknown metabolites.”
BackgroundChu, S. H., Huang, M., Kelly, R., Benedetti, E., Siddiqui, J. K., Zeleznik, O., Pereira, A., Herrington, D., Wheelock, C., Krumsiek, J., et al. (2019) · Metabolites · doi:10.3390/metabo9060117 · Cited by 73 · see citing works
Self-citationGenetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms
Yet, İ., Menni, C., Shin, S., Mangino, M., Soranzo, N., Adamski, J., Suhre, K., Spector, T. D., Kastenmüller, G., & Bell, J. T. (2016) · PLoS ONE · doi:10.1371/journal.pone.0153672 · Cited by 77 · see citing works
“5×10)), which were recently shown to be identical molecules [36].”
BackgroundSelf-citationNetwork-Guided GWAS Improves Identification of Genes Affecting Free Amino Acids
Angelovici, R., Batushansky, A., Deason, N., Gonzalez-Jorge, S., Gore, M. A., Fait, A., & DellaPenna, D. (2016) · PLANT PHYSIOLOGY · doi:10.1104/pp.16.01287 · Cited by 72 · see citing works
“…to either prioritize candidate genes retrieved from GWAS performed on absolute metabolite levels (Chan et al., 2011; Lee et al., 2011; Greene et al., 2015; Matsuda et al., 2015) or to identify unknown metabolites from combined metabolites and gene coexpression networks (Krumsiek et al., 2012).”
BackgroundMoreno, P., Jiménez‐Jiménez, C., Garrido‐Rodríguez, M., Calderón-Santiago, M., Molina, S., Lara‐Chica, M., Priego‐Capote, F., Salvatierra, Á., Muñóz, E., & Calzado, M. A. (2018) · Molecular Oncology · doi:10.1002/1878-0261.12369 · Cited by 71 · see citing works
“The three networks (metabolite, subpathway and superpathway) together depict the hierarchical map and have been used in previous studies (Krumsiek et al., 2012; Poisson et al., 2015) (Data S1).”
MethodToward modeling metabolic state from single-cell transcriptomics
Hrovatin, K., Fischer, D. S., & Theis, F. J. (2021) · Molecular Metabolism · doi:10.1016/j.molmet.2021.101396 · Cited by 69 · see citing works
Self-citationMetabolite profiling reveals new insights into the regulation of serum urate in humans
Albrecht, E., Waldenberger, M., Krumsiek, J., Evans, A. M., Jeratsch, U., Breier, M., Adamski, J., Köenig, W., Kunze, S., Fuchs, C., et al. (2013) · Metabolomics · doi:10.1007/s11306-013-0565-2 · Cited by 67 · see citing works
“Aspartame was shown to pose antipyretic, analgesic and anti-inflammatory action and to delay osteoarthritis in animal models (LaBuda and Fuchs 2001; Manion et al. 2011; Pradhan et al. 2011).”
BackgroundSelf-citationMixing omics: combining genetics and metabolomics to study rheumatic diseases
Menni, C., Zierer, J., Valdes, A. M., & Spector, T. D. (2017) · Nature Reviews Rheumatology · doi:10.1038/nrrheum.2017.5 · Cited by 61 · see citing works
Suhre, K. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100506 · Cited by 62 · see citing works
Self-citationComputational approaches for systems metabolomics
Krumsiek, J., Bartel, J., & Theis, F. J. (2016) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2016.04.009 · Cited by 59 · see citing works
Self-citationMetabolomics in the developmental origins of obesity and its cardiometabolic consequences
Hivert, M., Perng, W., Watkins, S. M., Newgard, C. B., Kenny, L. C., Kristal, B. S., Patti, M., ISGANAITIS, E. M., DeMeo, D. L., Oken, E., et al. (2015) · Journal of Developmental Origins of Health and Disease · doi:10.1017/s204017441500001x · Cited by 60 · see citing works
Biochemical insights from population studies with genetics and metabolomics
Suhre, K., Raffler, J., & Kastenmüller, G. (2015) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2015.09.023 · Cited by 55 · see citing works
Self-citationBarkla, B. J., & Vera‐Estrella, R. (2015) · Frontiers in Plant Science · doi:10.3389/fpls.2015.00435 · Cited by 55 · see citing works
“…integrating the data from genetic associations and metabolic networks with biochemical pathway information, has been successful in both humans (Krumsiek et al., 2012) and plants (Luo, 2015) for annotation of unidentified metabolites and will provide a means in the future to accelerate…”
BackgroundTahir, U. A., Katz, D. H., Ávila-Pacheco, J., Bick, A. G., Pampana, A., Robbins, J., Yu, Z., Chen, Z., Benson, M. D., Cruz, D. E., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32275-3 · Cited by 52 · see citing works
A large-scale genome–lipid association map guides lipid identification
Linke, V., Overmyer, K. A., Miller, I. J., Brademan, D. R., Hutchins, P. D., Trujillo, E. A., Reddy, T. R., Russell, J. D., Cushing, E. M., Schueler, K. L., et al. (2020) · Nature Metabolism · doi:10.1038/s42255-020-00278-3 · Cited by 54 · see citing works
Voit, E. O. (2017) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1391 · Cited by 53 · see citing works
Graham, E., Lee, J. K., Price, M., Tarailo‐Graovac, M., Matthews, A., Engelke, U. F. H., Tang, J. S., Kluijtmans, L. A. J., Wevers, R. A., Wasserman, W. W., et al. (2018) · Journal of Inherited Metabolic Disease · doi:10.1007/s10545-018-0139-6 · Cited by 50 · see citing works
“Toward this end, network models that take into account the interactions between genes and metabolites have been used to identify metabolic pathways perturbed in disease (Krumsiek et al 2012; Li et al 2013; Pirhaji et al 2016).”
MethodMetabolomics as a Tool to Understand Pathophysiological Processes
Ivanišević, J., & Thomas, A. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7592-1_1 · Cited by 47 · see citing works
Early Diagnosis of Sepsis: Is an Integrated Omics Approach the Way Forward?
Langley, R. J., & Wong, H. R. (2017) · Molecular Diagnosis & Therapy · doi:10.1007/s40291-017-0282-z · Cited by 50 · see citing works
“However, integration linking metabolomic and transcriptomic changes with genetic and epigenetic changes may improve the identification of disease-causing mutations [122, 123].”
BackgroundAscorbic acid metabolites are involved in intraocular pressure control in the general population
Hysi, P. G., Khawaja, A. P., Menni, C., Tamraz, B., Wareham, N. J., Khaw, K., Foster, P. J., Benet, L. Z., Spector, T. D., & Hammond, C. J. (2018) · Redox Biology · doi:10.1016/j.redox.2018.10.004 · Cited by 47 · see citing works
“The highest-ranking metabolite in order of importance was O-methylascorbate [22].”
BackgroundFind the Needle in the Haystack, Then Find It Again: Replication and Validation in the ‘Omics Era
Perng, W., & Aslibekyan, S. W. (2020) · Metabolites · doi:10.3390/metabo10070286 · Cited by 46 · see citing works
“In the biomedical sciences, genomics-metabolomics analyses using undirected network-based approaches have demonstrated capacity to recreate true biochemical pathways [55] and home in on those involved in disease etiology [56].”
BackgroundAltmaier, E., Emeny, R. T., Krumsiek, J., Lacruz, M. E., Lukaschek, K., Häfner, S., Kastenmüller, G., Römisch‐Margl, W., Prehn, C. P., Mohney, R. P., et al. (2012) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2012.11.014 · Cited by 43 · see citing works
“For a comprehensive view of Type D-effects in this datadriven metabolic network, a Gaussian graphical model (GGM) approach was employed (Krumsiek et al., 2012).”
MethodSelf-citationUrinary proteomics and metabolomics studies to monitor bladder health and urological diseases
Chen, Z., & Kim, J. (2016) · BMC Urology · doi:10.1186/s12894-016-0129-7 · Cited by 44 · see citing works
“For metabolomics data processing, several statistical tools are currently used to analyze NMR and MS-based metabolomics datasets (e.g., MS-DIAL [80], XCMS, MZmine, MetAlign, MathDAMP, and LCMStats) [81, 82].”
Everett, J. R. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2015.01.002 · Cited by 43 · see citing works
“In genome wide association studies on metabonomics data, the pathway information that can be gleaned can also be used to help identify key metabolites, including by metabomatching [28,29].”
MethodAlterations in Lipid and Inositol Metabolisms in Two Dopaminergic Disorders
Schulte, E. C., Altmaier, E., Berger, H. S., Do, K. T., Kastenmüller, G., Wahl, S., Adamski, J., Peters, A., Krumsiek, J., Suhre, K., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0147129 · Cited by 40 · see citing works
“[14, 16, 17] For both RLS and PD, the regression coefficients and the logged-transformed p-values obtained from this regression analysis were mapped onto the filtered (Bonferroni-corrected) GGM, which was then visualized as node pie charts in Cytoscape with node coloring according to the direction (regression coeffici…”
MethodSelf-citationDo, K. T., Kastenmüller, G., Mook-Kanamori, D. O., Yousri, N. A., Theis, F. J., Suhre, K., & Krumsiek, J. (2014) · Journal of Proteome Research · doi:10.1021/pr501130a · Cited by 42 · see citing works
Self-citationKennedy, A. D., Ford, L. A., Wittmann, B. M., Conner, J., Wulff, J. E., Mitchell, M. W., Evans, A. M., & Toal, D. R. (2021) · PLoS ONE · doi:10.1371/journal.pone.0249797 · Cited by 41 · see citing works
Self-citationTeslovich, T. M., Kim, D. S., Yin, X., Stančáková, A., Jackson, A., Wielscher, M., Naj, A. C., Perry, J. R. B., Huyghe, J. R., Stringham, H. M., et al. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy067 · Cited by 36 · see citing works
Genome-Wide Association Studies of Metabolite Concentrations (mGWAS): Relevance for Nephrology
Köttgen, A., Raffler, J., Sekula, P., & Kastenmüller, G. (2018) · Seminars in Nephrology · doi:10.1016/j.semnephrol.2018.01.009 · Cited by 37 · see citing works
Self-citationLuo, S., Feofanova, E. V., Tin, A., Tung, S., Rhee, E. P., Coresh, J., Arking, D. E., Surapaneni, A., Schlosser, P., Li, Y., et al. (2021) · Kidney International · doi:10.1016/j.kint.2021.03.026 · Cited by 39 · see citing works
Deployment-Associated Exposure Surveillance With High-Resolution Metabolomics
Walker, D. I., Mallon, T. M., Hopke, P. K., Uppal, K., Go, Y., Rohrbeck, P., Pennell, K. D., & Jones, D. P. (2016) · Journal of Occupational and Environmental Medicine · doi:10.1097/jom.0000000000000768 · Cited by 36 · see citing works
Li, Y., Li, Q., Cao, Z., & Wu, J. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.962787 · Cited by 37 · see citing works
Genetics of the human metabolome, what is next?
Dharuri, H. K., Demirkan, A., van Klinken, J. B., Mook-Kanamori, D. O., van Duijn, C. M., Hoen, P. A. C. ’., & van Dijk, K. W. (2014) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2014.05.030 · Cited by 34 · see citing works
Hepatic Steatosis Is Associated With Adverse Molecular Signatures in Subjects Without Diabetes
Pietzner, M., Budde, K., Homuth, G., Kastenmüller, G., Henning, A., Artati, A., Krumsiek, J., Völzke, H., Adamski, J., Lerch, M. M., et al. (2018) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2018-00999 · Cited by 32 · see citing works
Self-citationMetabolic health, the metabolome and reproduction in female cattle: a review
D’Occhio, M. J., Baruselli, P. S., & Campanile, G. (2019) · Italian Journal of Animal Science · doi:10.1080/1828051x.2019.1600385 · Cited by 33 · see citing works
“…biology (Woelders et al. 2011) brings together transcriptomic, proteomic and metabolomic information to build integrated gene networks that underpin biological processes linked to phenotypes (Homuth et al. 2012; Krumsiek et al. 2012; Widmann et al. 2013, 2015; Cho et al. 2014; Wang et al. 2019).”
BackgroundGillenwater, L. A., Pratte, K., Hobbs, B. D., Cho, M. H., Zhuang, Y., Halper‐Stromberg, E., Cruickshank-Quinn, C. I., Reisdorph, N. A., Petrache, I., Labaki, W. W., et al. (2020) · Network and Systems Medicine · doi:10.1089/nsm.2020.0009 · Cited by 33 · see citing works
Do, K. T., Pietzner, M., Rasp, L. D., Friedrich, N., Nauck, M. A., Kocher, T., Suhre, K., Mook-Kanamori, D. O., Kastenmüller, G., & Krumsiek, J. (2017) · npj Systems Biology and Applications · doi:10.1038/s41540-017-0029-9 · Cited by 32 · see citing works
Self-citationHASE: Framework for efficient high-dimensional association analyses
Roshchupkin, G. V., Adams, H. H. H., Vernooij, M. W., Albert Hofman, D., van Duijn, C. M., Ikram, M. A., & Niessen, W. J. (2016) · Scientific Reports · doi:10.1038/srep36076 · Cited by 32 · see citing works
“Systematic and large-scale investigations of DNA sequence variations (genomics) 1 , gene expression (transcriptomics) 2 , proteins (proteomics) 3 , small molecule metabolites (metabolomics) 4 , and medical images (radiomics) 5 , among other data, lie at the basis of many recent biological insights.”
BackgroundPais, A. L., Li, X., & Xiang, Q. (2018) · Ecology and Evolution · doi:10.1002/ece3.4090 · Cited by 31 · see citing works
Lipid metabolic networks, Mediterranean diet and cardiovascular disease in the PREDIMED trial
Wang, D. D., Zheng, Y., Toledo, E., Razquín, C., Ruiz‐Canela, M., Guasch‐Ferré, M., Yu, E., Corella, D., Gómez‐Gracia, E., Fiol, M., et al. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy198 · Cited by 31 · see citing works
Feng, Y., Wang, R., Li, C., Cai, X., Huo, Z., Liu, Z., Ge, F., Huang, C., Lu, Y., Zhong, R., et al. (2022) · Translational Lung Cancer Research · doi:10.21037/tlcr-22-34 · Cited by 31 · see citing works
“The KORA dataset and Twins UK dataset have been described in previous studies (8,16).”
Metabolomics in Sepsis and Its Impact on Public Health
Evangelatos, N. G., Bauer, P., Reumann, M., Satyamoorthy, K., Lehrach, H. R., & Brand, A. M. (2017) · Public Health Genomics · doi:10.1159/000486362 · Cited by 29 · see citing works
Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma
Raffler, J., Römisch‐Margl, W., Petersen, A.-K., Pagel, P., Blöchl, F., Hengstenberg, C., Illig, T., Meisinger, C., Stark, K. J., Wichmann, H. ‐., et al. (2013) · Genome Medicine · doi:10.1186/gm417 · Cited by 29 · see citing works
Self-citationA strategy to incorporate prior knowledge into correlation network cutoff selection
Benedetti, E., Pučić‐Baković, M., Keser, T., Gerstner, N., Büyüközkan, M., Štambuk, T., Selman, M. H. J., Rudan, I., Polašek, O., Hayward, C., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18675-3 · Cited by 28 · see citing works
Self-citationRied, J. S., Shin, S., Krumsiek, J., Illig, T., Theis, F. J., Spector, T. D., Adamski, J., Wichmann, H. ‐., Strauch, K., Soranzo, N., et al. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu301 · Cited by 26 · see citing works
“This completely datadriven approach was shown to be a valuable tool to identify metabolite networks, which is able to distinguish direct from indirect associations (24,25).”
MethodSelf-citationSun, S., Jiao, M., Han, C., Zhang, Q., Shi, W., Shi, J., & Li, X. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.00621 · Cited by 23 · see citing works
“We included these “unknown” metabolites in our study as they still attracted attention from other researchers and might provide further useful information in the future (22).”
BackgroundMethodLactation is associated with altered metabolomic signatures in women with gestational diabetes
Much, D., Beyerlein, A., Kindt, A. S. D., Krumsiek, J., Stückler, F., Roßbauer, M., Hofelich, A., Wiesenäcker, D., Hivner, S., Herbst, M., et al. (2016) · Diabetologia · doi:10.1007/s00125-016-4055-8 · Cited by 23 · see citing works
Self-citationUnintended effects of transgenic rice revealed by transcriptome and metabolism
Fu, W., Wang, C., Xu, W., Zhu, P., Lü, Y., Wei, S., Wu, X., Wu, Y., Zhao, Y., & Zhu, S. (2019) · GM crops & food · doi:10.1080/21645698.2019.1598215 · Cited by 22 · see citing works
Gut microbiota, circulating metabolites, and gallstone disease: a Mendelian randomization study
Hu, X., Qiu, B., Shao, G., Huang, Y., & Qiu, W. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1336673 · Cited by 22 · see citing works
Hsu, Y.-H. H., Churchhouse, C. L., Pers, T. H., Mercader, J. M., Metspalu, A., Fischer, K., Fortney, K., Morgen, E. K., González, C., Gonzalez, M. E., et al. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006734 · Cited by 22 · see citing works
“…they focus on a small subset of the unknowns (e.g. those directly associated with known metabolites, genes, or a trait of interest) and do not explicitly provide biological interpretations (e.g. pathway labels) for enriched pathways or network clusters identified during analysis [10, 14, 15].”
BackgroundMethodConstruction and Analysis of Human Diseases and Metabolites Network
Mi, K., Jiang, Y., Chen, J., Lv, D., Qian, Z., Sun, H., & Shang, D. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00398 · Cited by 21 · see citing works
Das, S. K., Ainsworth, H. C., Dimitrov, L. M., Okut, H., Comeau, M. E., Sharma, N., Ng, M. C. Y., Norris, J. M., Chen, Y. I., Wagenknecht, L. E., et al. (2021) · Molecular Metabolism · doi:10.1016/j.molmet.2021.101342 · Cited by 21 · see citing works
Common and specific genetic basis of metabolite-mediated drought responses in rice
Guo, Z., Wang, S., Zhang, F., Xiang, D., Yang, J., Li, D., Bai, B., Dai, M., Luo, J., & Xiong, L. (2024) · Stress Biology · doi:10.1007/s44154-024-00150-4 · Cited by 21 · see citing works
“Gaussian graphical modeling (GGM) was performed using R package “GeneNet” and “DMwR” as the previous study (Krumsiek et al. 2012).”
Method
- Authors
- Krumsiek, J., Suhre, K., Evans, A. M., Mitchell, M. W., Mohney, R. P., Milburn, M. V., Wägele, B., Römisch-Margl, W., Illig, T., Adamski, J., Gieger, C., Theis, F. J., & Kastenmüller, G.
- Affiliations
- Helmholtz Munich (DE)
- Metabolon (United States) (US)
- Corresponding author
- Kastenmüller, Gabi
- ORCID
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-10-18
- Volume
- 8
- Issue
- 10
- Pages
- e1003005
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC3475673
- OpenAlex
- W2080426060
- Cited by
- 197
- References
- 63
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 19 · 2023: 8 · 2022: 15 · 2021: 16 · 2020: 16 · 2019: 9 · 2018: 16 · 2017: 18 · 2016: 14 · 2015: 16 · 2014: 17 · 2013: 6 · 2012: 3
- FWCI
- 5.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Site cant be reached (submittedVersion, cc-by)
- Funders
- Qatar Foundation
- Bundesministerium für Bildung und Forschung (0315494A, 03IS2061B)
- Studienstiftung des Deutschen Volkes
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- genome-wide association study, metabolic networks, SLC22A2, catechol-O-methyltransferase, CYP3A5, CYP2C18, liver detoxification, hypertension, insulin resistance, blood metabolites, systems biology
- MeSH terms
- Humans, Reproducibility of Results, Models, Statistical, Signal Transduction, Computational Biology, Polymorphism, Single Nucleotide, Genomics, Genome-Wide Association Study, Metabolomics, Metabolome, Data Mining
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