Gabi Kastenmüller

Head of Research Group Systems Metabolomics

+49 89 3187-3578 g.kastenmueller@helmholtz-munich.de

Building / Room: 56, 151

“We are all different – and so is our metabolism. Analysis and integration of big metabolomics (omics) data sets will help us to understand the crucial role of metabolic disruptions and individuality in age-related diseases. They will give us new options for personalized prevention and treatment.”

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The main objective of my research is to identify the molecular mechanisms that translate genetic risk factors and their interplay with lifestyle and environmental factors into the development and progression of specific complex diseases such as diabetes, kidney and, Alzheimer’s disease, and depression. Thereby, I have a major focus on investigating how the individual metabolic make-up, as accessible through metabolomics, its changes over time, and its link to genetic variation affect human health. To make full use of the big omics data sets that are produced to address these research questions, we develop new strategies for analyzing, integrating, and visualizing the data and the derived results in bioinformatics frameworks and resources. In particular, we aim to

Unravel determinants of metabolic individuality in data (genetic, microbial, dietary) from large cohorts
Integrate data/results (metabolomic-centric, network-based) to identify processes disturbed in disease, e.g., Alzheimer’s disease
Map out metabolomic responses to physiological challenges such as exercise and diet
Build web tools for intuitive data/result exploration

Having a background in chemistry and computer science, I moved into bioinformatics for my PhD after having paused my career due to family obligations. I received my PhD from Technische Universität München, Germany, in 2009 and joined Karsten Suhre’s lab at Helmholtz Munich as a postdoc in the same year. During my postdoctoral training and a four-month stay at Metabolon Inc, USA, a commercial provider of metabolomics measurements, I gained wide experience in metabolomics and was involved in the analysis of one of the first mass spectrometry-based metabolomics studies at large scale in two population-based cohorts, before I started the adventure of setting up my own lab in 2011.

MetabolomicsBioinformaticsMetabolismData IntegrationBiostatisticsBig DataWebtool development

2019 - present

at the Computational Health Center / ICB, Helmholtz ZMunich

2017 - 2019

of the Institute of Bioinformatics and Systems Biology (IBIS), Helmholtz Munich

2014 - 2017

in the Department of Twin Research, King's College London, UK

2011 - 2019

at IBIS, Helmholtz Munich

2010

(May-Aug), Metabolon Inc., Durham, NC, USA

2009 - 2011

at Karsten Suhre's lab, IBIS, Helmholtz Munich

2006 - 2009

at Genome-oriented Bioinformatics, Life and Food Science Center Weihenstephan, Technische Universität MMünchen (TUM)
Thesis: "In silico prediction and comparison of metabolic capabilities in sequenced genomes"

2001 - 2009

at IBIS, Helmholtz Munich

1999 - 2001

at Munich Information Center for Protein Sequences (MIPS), Max Planck Institute of Biochemistry, Munich

1993 - 1998

at Ludwig-Maximilians-Universität München (LMU)
Thesis: "Similarity search in 3D protein databases" (in German)
(Dipl.-Inform., German equiv. to Master’s degree)

1989 - 1995

at Ludwig-Maximilians-Universität München (LMU)
Thesis: "Light-induced reorientation of azo dye containing, cholesteric oligosiloxanes" (in German)
(Dipl.-Chem., German equiv. to Master’s degree)

Maik Pietzner, Isobel D. Stewart, Johannes Raffler, Kay-Tee Khaw, Gregory A. Michelotti, Gabi Kastenmüller, Nicholas J. Wareham & Claudia Langenberg

Plasma Metabolites to Profile Pathways in Noncommunicable Disease Multimorbidity. Multimorbidity, the simultaneous presence of multiple chronic conditions, is an increasing global health problem and research into its determinants is of high priority. We used baseline untargeted plasma metabolomics profiling covering >1,000 metabolites as a comprehensive readout of human physiology to characterize pathways associated with and across 27 incident noncommunicable diseases (NCDs) assessed using electronic health record hospitalization and cancer registry data from over 11,000 participants (219,415 person years). We identified 420 metabolites shared between at least 2 NCDs, representing 65.5% of all 640 significant metabolite-disease associations. We integrated baseline data on over 50 diverse clinical risk factors and characteristics to identify actionable shared pathways represented by those metabolites. Our study highlights liver and kidney function, lipid and glucose metabolism, low-grade inflammation, surrogates of gut microbial diversity and specific health-related behaviors as antecedents of common NCD multimorbidity with potential for early prevention. We integrated results into an open-access webserver ( https://omicscience.org/apps/mwasdisease/ ) to facilitate future research and meta-analyses.

Daniela Schranner, Martin Schönfelder, Werner Römisch-Margl, Johannes Scherr, Jürgen Schlegel, Otto Zelger, Annett Riermeier, Stephanie Kaps, Cornelia Prehn, Jerzy Adamski, Quirin Söhnlein, Fabian Stöcker, Florian Kreuzpointner, Martin Halle, Gabi Kastenmüller & Henning Wackerhage

Physiological Extremes of the Human Blood Metabolome: A Metabolomics Analysis of Highly Glycolytic, Oxidative, and Anabolic Athletes Human metabolism is highly variable. At one end of the spectrum, defects of enzymes, transporters, and metabolic regulation result in metabolic diseases such as diabetes mellitus or inborn errors of metabolism. At the other end of the spectrum, favorable genetics and years of training combine to result in physiologically extreme forms of metabolism in athletes. Here, we investigated how the highly glycolytic metabolism of sprinters, highly oxidative metabolism of endurance athletes, and highly anabolic metabolism of natural bodybuilders affect their serum metabolome at rest and after a bout of exercise to exhaustion. We used targeted mass spectrometry-based metabolomics to measure the serum concentrations of 151 metabolites and 43 metabolite ratios or sums in 15 competitive male athletes (6 endurance athletes, 5 sprinters, and 4 natural bodybuilders) and 4 untrained control subjects at fasted rest and 5 minutes after a maximum graded bicycle test to exhaustion. The analysis of all 194 metabolite concentrations, ratios and sums revealed that natural bodybuilders and endurance athletes had overall different metabolite profiles, whereas sprinters and untrained controls were more similar. Specifically, natural bodybuilders had 1.5 to 1.8-fold higher concentrations of specific phosphatidylcholines and lower levels of branched chain amino acids than all other subjects. Endurance athletes had 1.4-fold higher levels of a metabolite ratio showing the activity of carnitine-palmitoyl-transferase I and 1.4-fold lower levels of various alkyl-acyl-phosphatidylcholines. When we compared the effect of exercise between groups, endurance athletes showed 1.3-fold higher increases of hexose and of tetradecenoylcarnitine (C14:1). In summary, physiologically extreme metabolic capacities of endurance athletes and natural bodybuilders are associated with unique blood metabolite concentrations, ratios, and sums at rest and after exercise. Our results suggest that long-term specific training, along with genetics and other athlete-specific factors systematically change metabolite concentrations at rest and after exercise.

Matthias Arnold, Kwangsik Nho, Alexandra Kueider-Paisley, Tyler Massaro, Kevin Huynh, Barbara Brauner, Siamak MahmoudianDehkordi, Gregory Louie, M. Arthur Moseley, J. Will Thompson, Lisa St John-Williams, Jessica D. Tenenbaum, Colette Blach, Rui Chang, Roberta D. Brinton, Rebecca Baillie, Xianlin Han, John Q. Trojanowski, Leslie M. Shaw, Ralph Martins, Michael W. Weiner, Eugenia Trushina, Jon B. Toledo, Peter J. Meikle, David A. Bennett, Jan Krumsiek, P. Murali Doraiswamy, Andrew J. Saykin, Rima Kaddurah-Daouk & Gabi Kastenmüller

Sex and APOE ε4 genotype modify the Alzheimer’s disease serum metabolome. Sex and the APOE epsilon 4 genotype are important risk factors for late-onset Alzheimer's disease. In the current study, the authors investigate how sex and APOE epsilon 4 genotype modify the association between Alzheimer's disease biomarkers and metabolites in serum.Late-onset Alzheimer's disease (AD) can, in part, be considered a metabolic disease. Besides age, female sex and APOE epsilon 4 genotype represent strong risk factors for AD that also give rise to large metabolic differences. We systematically investigated group-specific metabolic alterations by conducting stratified association analyses of 139 serum metabolites in 1,517 individuals from the AD Neuroimaging Initiative with AD biomarkers. We observed substantial sex differences in effects of 15 metabolites with partially overlapping differences for APOE epsilon 4 status groups. Several group-specific metabolic alterations were not observed in unstratified analyses using sex and APOE epsilon 4 as covariates. Combined stratification revealed further subgroup-specific metabolic effects limited to APOE epsilon 4+ females. The observed metabolic alterations suggest that females experience greater impairment of mitochondrial energy production than males. Dissecting metabolic heterogeneity in AD pathogenesis can therefore enable grading the biomedical relevance for specific pathways within specific subgroups, guiding the way to personalized medicine.

Jonas Zierer, Matthew A. Jackson, Gabi Kastenmüller, Massimo Mangino, Tao Long, Amalio Telenti, Robert P. Mohney, Kerrin S. Small, Jordana T. Bell, Claire J. Steves, Ana M. Valdes, Tim D. Spector & Cristina Menni

The Fecal Metabolome as a Functional Readout of the Gut Microbiome The human gut microbiome plays a key role in human health1, but 16S characterization lacks quantitative functional annotation2. The fecal metabolome provides a functional readout of microbial activity and can be used as an intermediate phenotype mediating host–microbiome interactions3. In this comprehensive description of the fecal metabolome, examining 1,116 metabolites from 786 individuals from a population-based twin study (TwinsUK), the fecal metabolome was found to be only modestly influenced by host genetics (heritability (H2) = 17.9%). One replicated locus at the NAT2 gene was associated with fecal metabolic traits. The fecal metabolome largely reflects gut microbial composition, explaining on average 67.7% (±18.8%) of its variance. It is strongly associated with visceral-fat mass, thereby illustrating potential mechanisms underlying the well-established microbial influence on abdominal obesity. Fecal metabolic profiling thus is a novel tool to explore links among microbiome composition, host phenotypes, and heritable complex traits.

Matthias Arnold, Johannes Raffler, Arne Pfeufer, Karsten Suhre & Gabi Kastenmüller

SNiPA: An Interactive, Genetic Variant-Centered Annotation Browser Motivation: Linking genes and functional information to genetic variants identified by association studies remains difficult. Resources containing extensive genomic annotations are available but often not fully utilized due to heterogeneous data formats. To enhance their accessibility, we integrated many annotation datasets into a user-friendly webserver.

Availability and implementation: http://www.snipa.org/

Contact: g.kastenmueller@helmholtz-muenchen.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Gabi Kastenmüller, Johannes Raffler, Christian Gieger, Karsten Suhre

Genetics of human metabolism: an update Genome-wide association studies with metabolomics (mGWAS) identify genetically influenced metabotypes (GIMs), their ensemble defining the heritable part of every human's metabolic individuality. Knowledge of genetic variation in metabolism has many applications of biomedical and pharmaceutical interests, including the functional understanding of genetic associations with clinical end points, design of strategies to correct dysregulations in metabolic disorders and the identification of genetic effect modifiers of metabolic disease biomarkers. Furthermore, it has been shown that GIMs provide testable hypotheses for functional genomics and metabolomics and for the identification of novel gene functions and metabolite identities. mGWAS with growing sample sizes and increasingly complex metabolic trait panels are being conducted, allowing for more comprehensive and systems-based downstream analyses. The generated large datasets of genetic associations can now be mined by the biomedical research community and provide valuable resources for hypothesis-driven studies. In this review, we provide a brief summary of the key aspects of mGWAS, followed by an update of recently published mGWAS. We then discuss new approaches of integrating and exploring mGWAS results and finish by presenting selected applications of GIMs in recent studies.

So-Youn Shin, Eric B Fauman, Ann-Kristin Petersen, Jan Krumsiek, Rita Santos, Jie Huang, Matthias Arnold, Idil Erte, Vincenzo Forgetta, Tsun-Po Yang, Klaudia Walter, Cristina Menni, Lu Chen, Louella Vasquez, Ana M Valdes, Craig L Hyde, Vicky Wang, Daniel Ziemek, Phoebe Roberts, Li Xi, Elin Grundberg, The Multiple Tissue Human Expression Resource (MuTHER) Consortium, Melanie Waldenberger, J Brent Richards, Robert P Mohney, Michael V Milburn, Sally L John, Jeff Trimmer, Fabian J Theis, John P Overington, Karsten Suhre, M Julia Brosnan, Christian Gieger, Gabi Kastenmüller, Tim D Spector & Nicole Soranzo

An atlas of genetic influences on human blood metabolites Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism thus far, comprising 7,824 adult individuals from 2 European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metabolism and pharmacological targets. We further developed a database and web-based resources for data mining and results visualization. Our findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease.

Jan Krumsiek, Karsten Suhre, Anne M. Evans, Matthew W. Mitchell, Robert P. Mohney, Michael V. Milburn, Brigitte Wägele, Werner Römisch-Margl, Thomas Illig, Jerzy Adamski, Christian Gieger, Fabian J. Theis & Gabi Kastenmüller

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

Susanne Krug, Gabi Kastenmüller, Ferdinand Stückler, Manuela J. Rist, Thomas Skurk, Manuela Sailer, Johannes Raffler, Werner Römisch-Margl, Jerzy Adamski, Cornelia Prehn, Thomas Frank, Karl-Heinz Engel, Thomas Hofmann, Burkhard Luy, Ralf Zimmermann, Franco Moritz, Philippe Schmitt-Kopplin, Jan Krumsiek, Werner Kremer, Fritz Huber, Uwe Oeh, Fabian J. Theis, Wilfried Szymczak, Hans Hauner, Karsten Suhre & Hannelore Daniel

The Dynamic Range of the Human Metabolome Revealed by Challenges Metabolic challenge protocols, such as the oral glucose tolerance test, can uncover early alterations in metabolism preceding chronic diseases. Nevertheless, most metabolomics data accessible today reflect the fasting state. To analyze the dynamics of the human metabolome in response to environmental stimuli, we submitted 15 young healthy male volunteers to a highly controlled 4 d challenge protocol, including 36 h fasting, oral glucose and lipid tests, liquid test meals, physical exercise, and cold stress. Blood, urine, exhaled air, and breath condensate samples were analyzed on up to 56 time points by MS-and NMR-based methods, yielding 275 metabolic traits with a focus on lipids and amino acids. Here, we show that physiological challenges increased interindividual variation even in phenotypically similar volunteers, revealing metabotypes not observable in baseline metabolite profiles; volunteer-specific metabolite concentrations were consistently reflected in various biofluids; and readouts from a systematic model of β-oxidation (e.g., acetylcarnitine/palmitylcarnitine ratio) showed significant and stronger associations with physiological parameters (e.g., fat mass) than absolute metabolite concentrations, indicating that systematic models may aid in understanding individual challenge responses. Due to the multitude of analytical methods, challenges and sample types, our freely available metabolomics data set provides a unique reference for future metabolomics studies and for verification of systems biology models.—Krug, S., Kastenmüller, G., Stückler, F., Rist, M. J., Skurk, T., Sailer, M., Raffler, J., Römisch-Margl, W., Adamski, J., Prehn, C., Frank, T., Engel, K-H., Hofmann, T., Luy, B., Zimmermann, R., Moritz, F., Schmitt-Kopplin, P., Krumsiek, J., Kremer, W., Huber, F., Oeh, U., Theis, F. J., Szymczak, W., Hauner, H., Suhre, K., Daniel, H. The dynamic range of the human metabolome revealed by challenges. FASEB J. 26, 2607-2619 (2012). www.fasebj.org

The metabolomist

Biocrates Podcast

Data sharing & sources of bias in metabolomics

Alice and Gabi Kastenmüller discuss data stratification, teamwork, and keeping tools open for the community.

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Dr. Gabi Kastenmüller

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Research Interests and Academic Career

Skills and Expertise

Professional Background

Head of Research Group “Systems Metabolomics”

Director (acting)

Honorary Lecturer

Head of Research Group “Metabolomics”

Visiting Research Fellow (J1)

Postdoctoral Fellow

Ph.D. Student

Research Associate

Research Associate

Studies in Computer Science

Studies in Chemistry

Selected Publications

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The metabolomist

Biocrates Podcast

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