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Systems Metabolomics

Kastenmüller Lab

Our mission is to understand the role of metabolism and metabolic individuality in complex human diseases using systems metabolomics.

 

Our mission is to understand the role of metabolism and metabolic individuality in complex human diseases using systems metabolomics.

 

What we do ...

Understand the role of metabolism and metabolic individuality in the development and treatment of diseases using systems metabolomics.

Using metabolomic data, the main objective of our research is to identify metabolic mechanisms that translate genetic risk factors and their interplay with lifestyle and environmental factors into the development and progression of complex diseases, including Alzheimer's disease and chronic kidney disease. Thereby, we have a major focus on investigating how a person's individual metabolic make-up, its changes over time, and its link to genetic variation affect health and disease. We use metabolomics and other omics data from large epidemiological cohorts in combination with advanced computational approaches as tools to access and understand relevant metabolic individuality and its determinants in healthy populations. This forms the basis for elucidating the role of metabolic disruption in age-related diseases, their co-occurrence (multi-morbidity), and heterogeneity in a systems medicine context. Our ultimate goal is to translate our results into applied precision medicine by shifting metabolomics from a valuable research tool to a practical clinical instrument for monitoring metabolic health.

Unravel the determinants of metabolic individuality in data from large cohorts by mapping out significant associations of metabolites with intrinsic (e.g., age) and extrinsic (e.g., life style) factors.

We aim to understand the different factors influencing a person’s individual metabolome. These factors include intrinsic, non-modifiable features such as age, sex, and genetic variation as well as potentially modifiable features such as disease, medication, physical activity and nutrition. We utilize metabolomics and other omics data from big epidemiological cohorts to perform large-scale genome-wide and metabolome-wide association studies (GWAS/MWAS). Here, the metabolome can serve as an intermediate readout, connecting the genetic disposition of individuals with various diseases and risk phenotypes.

Investigate metabolic disruptions in neurodegenerative diseases.

Our computational neurobiology team is studying failures in multi-omics regulatory networks in neurological, neurodegenerative, and mental disorders. The focal omics level in our analyses is the metabolome that we use as intermediate readout for disease risk, state, stage in progression, and resilience. Using advanced computational approaches, we use this readout and interface it with genomic, transcriptomic, and proteomic markers to build multi-level frameworks that can be interrogated to identify functional hypotheses across all available molecular and regulatory layers.

Our work is embedded in several international collaborations and consortium efforts funded by the National Institutes of Health. Our current research focus is on Alzheimer’s Disease (AD), where we are partners in the Alzheimer’s Disease Metabolomics Consortium (ADMC), the Accelerating Medicines Partnership – Alzheimer’s Disease (AMP-AD) and Molecular Mechanisms of the Vascular Etiology of Alzheimer’s Disease (M2OVE-AD) consortia, and Major Depressive Disorder (MDD) within the Mood Disorders Precision Medicine consortium (MDPMC).

 

Provide decision support for nephrologists to personalize treatment of chronic kidney disease based on metabolic networks.

Chronic kidney disease (CKD) is a common and complex disease. It is one of the leading causes of death worldwide and is characterized by varying disease progression patterns and multiple comorbidities.

Our CKDNapp team is part of the BMBF-funded e:Med junior consortium "CKDNapp - Chronic Kidney Disease Nephrologist's App" (www.ckdn.app).  The consortium is developing a clinical decision support software (CDSS) to assist the practicing nephrologist in personalized treatment of chronic kidney disease patients. Our CKDNapp will predict adverse medical events and disease progression, refine diagnosis of CKD staging, return transparent reasoning for all predictions and recommendations, offer in silicio modification of patient parameters by the physican, and will deliver comprehensive literature support. It will be made available as an easy-to-use software for smartphones, tablets and desktop computers.

CKD atlas: In addition to CKDNapp, we are looking to integrate multi-omics data to build a resource that can help biologically justify the results provided by CKDNapp. Indeed, CKDNapp uses machine learning methods to computationally model the complex CKD system and these models do not provide biological justification for the decisions they make. Further to the biological justification, CKD atlas will offer researchers the opportunity to gain new insights into metabolic profiling and pathophysiological mechanisms related to chronic kidney disease.

Explore dynamic changes of the human metabolome in response to metabolic challenges such as diet and exercise.

The human body must continually adapt and dynamically respond to physiological challenges, such as food intake, exercise or stress. Metabolite profiles taken at multiple time points during or immediately after a specific challenge allows to monitor this systemic metabolic adaptation in a time-resolved manner, i.e., metabolomics enables us to watch metabolism ‘at work’.

On the one hand, we use challenge studies and time series metabolomics data to untangle the complexity of metabolic responses and their individuality as observed in healthy populations. A better understanding of these processes will help in optimizing and personalizing non-pharmacological disease treatments or prevention through diet and exercise.

On the other hand, we are interested in detecting features of impaired responses that could serve as early signs of a disease (i.e., analogous to oral glucose tolerance tests that allow to diagnose diabetes earlier compared to checking fasting glucose only).

In addition to time series data collected over a short period of time following a specific trigger/challenge, we use longitudinal metabolomics data with repeated measurement over several years for long-term health monitoring in large cohort studies and for following metabolic trajectories of disease.

 

Elucidating the complex molecular underpinnings of health and disease through integrative multi-omics networks.

Advances in high-throughput technologies have led to the generation of vast amounts of data profiling different biological layers, including DNA sequencing data (genomics), RNA expression levels (transcriptomics) and metabolite levels (metabolomics). Analysis of these single-omics have provided us with valuable insights, but fail to take the complex interplay between these layers into account. Using networks as a flexible and mathematically well-defined framework, we are working on ways to integrate, visualize and analyze heterogeneous, multi-omics datasets.

For complex diseases, which have been linked to perturbations across molecular layers (e.g., transcriptional changes, altered abundances of proteins and metabolites), this integrative analysis has the potential to guide the identification and prioritization of novel therapeutic targets and drug repositioning candidates, as well as provide further insights into the underpinnings of these heterogeneous diseases (e.g., Alzheimer’s disease and chronic kidney disease). Furthermore, we provide easy access to these integrative resources through freely accessible web services, such as the AD Atlas.

 

Provide easy access to metabolomics results and analysis for the wider scientific community.

With our web-services we enable the exploration and interactive visualisation of metabolomics results in our studies. Data sets with huge numbers of metabolite associations or time courses, that would be otherwise hidden in supplemental tables of publications, become thus easily available in online repositories as source and inspiration for further investigations. (OMICSCIENCE, HuMet Repository, metabolomicsGWAS Atlas, proteomicsGWAS)

Our AD Atlas and the SNiPA web-tools go one step further with the integration and annotation of knowledge from public available resources across multi-omics layers. They provide intuitive methods for data queries and show results in the context of previous findings to explore and understand underlying (patho)mechanisms.

Understand the role of metabolism and metabolic individuality in the development and treatment of diseases using systems metabolomics.

Using metabolomic data, the main objective of our research is to identify metabolic mechanisms that translate genetic risk factors and their interplay with lifestyle and environmental factors into the development and progression of complex diseases, including Alzheimer's disease and chronic kidney disease. Thereby, we have a major focus on investigating how a person's individual metabolic make-up, its changes over time, and its link to genetic variation affect health and disease. We use metabolomics and other omics data from large epidemiological cohorts in combination with advanced computational approaches as tools to access and understand relevant metabolic individuality and its determinants in healthy populations. This forms the basis for elucidating the role of metabolic disruption in age-related diseases, their co-occurrence (multi-morbidity), and heterogeneity in a systems medicine context. Our ultimate goal is to translate our results into applied precision medicine by shifting metabolomics from a valuable research tool to a practical clinical instrument for monitoring metabolic health.

Unravel the determinants of metabolic individuality in data from large cohorts by mapping out significant associations of metabolites with intrinsic (e.g., age) and extrinsic (e.g., life style) factors.

We aim to understand the different factors influencing a person’s individual metabolome. These factors include intrinsic, non-modifiable features such as age, sex, and genetic variation as well as potentially modifiable features such as disease, medication, physical activity and nutrition. We utilize metabolomics and other omics data from big epidemiological cohorts to perform large-scale genome-wide and metabolome-wide association studies (GWAS/MWAS). Here, the metabolome can serve as an intermediate readout, connecting the genetic disposition of individuals with various diseases and risk phenotypes.

Investigate metabolic disruptions in neurodegenerative diseases.

Our computational neurobiology team is studying failures in multi-omics regulatory networks in neurological, neurodegenerative, and mental disorders. The focal omics level in our analyses is the metabolome that we use as intermediate readout for disease risk, state, stage in progression, and resilience. Using advanced computational approaches, we use this readout and interface it with genomic, transcriptomic, and proteomic markers to build multi-level frameworks that can be interrogated to identify functional hypotheses across all available molecular and regulatory layers.

Our work is embedded in several international collaborations and consortium efforts funded by the National Institutes of Health. Our current research focus is on Alzheimer’s Disease (AD), where we are partners in the Alzheimer’s Disease Metabolomics Consortium (ADMC), the Accelerating Medicines Partnership – Alzheimer’s Disease (AMP-AD) and Molecular Mechanisms of the Vascular Etiology of Alzheimer’s Disease (M2OVE-AD) consortia, and Major Depressive Disorder (MDD) within the Mood Disorders Precision Medicine consortium (MDPMC).

 

Provide decision support for nephrologists to personalize treatment of chronic kidney disease based on metabolic networks.

Chronic kidney disease (CKD) is a common and complex disease. It is one of the leading causes of death worldwide and is characterized by varying disease progression patterns and multiple comorbidities.

Our CKDNapp team is part of the BMBF-funded e:Med junior consortium "CKDNapp - Chronic Kidney Disease Nephrologist's App" (www.ckdn.app).  The consortium is developing a clinical decision support software (CDSS) to assist the practicing nephrologist in personalized treatment of chronic kidney disease patients. Our CKDNapp will predict adverse medical events and disease progression, refine diagnosis of CKD staging, return transparent reasoning for all predictions and recommendations, offer in silicio modification of patient parameters by the physican, and will deliver comprehensive literature support. It will be made available as an easy-to-use software for smartphones, tablets and desktop computers.

CKD atlas: In addition to CKDNapp, we are looking to integrate multi-omics data to build a resource that can help biologically justify the results provided by CKDNapp. Indeed, CKDNapp uses machine learning methods to computationally model the complex CKD system and these models do not provide biological justification for the decisions they make. Further to the biological justification, CKD atlas will offer researchers the opportunity to gain new insights into metabolic profiling and pathophysiological mechanisms related to chronic kidney disease.

Explore dynamic changes of the human metabolome in response to metabolic challenges such as diet and exercise.

The human body must continually adapt and dynamically respond to physiological challenges, such as food intake, exercise or stress. Metabolite profiles taken at multiple time points during or immediately after a specific challenge allows to monitor this systemic metabolic adaptation in a time-resolved manner, i.e., metabolomics enables us to watch metabolism ‘at work’.

On the one hand, we use challenge studies and time series metabolomics data to untangle the complexity of metabolic responses and their individuality as observed in healthy populations. A better understanding of these processes will help in optimizing and personalizing non-pharmacological disease treatments or prevention through diet and exercise.

On the other hand, we are interested in detecting features of impaired responses that could serve as early signs of a disease (i.e., analogous to oral glucose tolerance tests that allow to diagnose diabetes earlier compared to checking fasting glucose only).

In addition to time series data collected over a short period of time following a specific trigger/challenge, we use longitudinal metabolomics data with repeated measurement over several years for long-term health monitoring in large cohort studies and for following metabolic trajectories of disease.

 

Elucidating the complex molecular underpinnings of health and disease through integrative multi-omics networks.

Advances in high-throughput technologies have led to the generation of vast amounts of data profiling different biological layers, including DNA sequencing data (genomics), RNA expression levels (transcriptomics) and metabolite levels (metabolomics). Analysis of these single-omics have provided us with valuable insights, but fail to take the complex interplay between these layers into account. Using networks as a flexible and mathematically well-defined framework, we are working on ways to integrate, visualize and analyze heterogeneous, multi-omics datasets.

For complex diseases, which have been linked to perturbations across molecular layers (e.g., transcriptional changes, altered abundances of proteins and metabolites), this integrative analysis has the potential to guide the identification and prioritization of novel therapeutic targets and drug repositioning candidates, as well as provide further insights into the underpinnings of these heterogeneous diseases (e.g., Alzheimer’s disease and chronic kidney disease). Furthermore, we provide easy access to these integrative resources through freely accessible web services, such as the AD Atlas.

 

Provide easy access to metabolomics results and analysis for the wider scientific community.

With our web-services we enable the exploration and interactive visualisation of metabolomics results in our studies. Data sets with huge numbers of metabolite associations or time courses, that would be otherwise hidden in supplemental tables of publications, become thus easily available in online repositories as source and inspiration for further investigations. (OMICSCIENCE, HuMet Repository, metabolomicsGWAS Atlas, proteomicsGWAS)

Our AD Atlas and the SNiPA web-tools go one step further with the integration and annotation of knowledge from public available resources across multi-omics layers. They provide intuitive methods for data queries and show results in the context of previous findings to explore and understand underlying (patho)mechanisms.

Scientists at our Lab

Gabi Kastenmüller

Head of Research Group Systems Metabolomics View profile

Matthias Arnold

Head of Team CompNeuro

Jürgen Dönitz

Head of Team CKDNapp

Nick Lehner

Scientist

Sai Krishna Bharadwaj Marella

PhD candidate

Leonard Nevaril

Scientist

Yacoub Abelard Njipouombe Nsangou

PhD candidate

Werner Römisch-Margl

Senior Scientist

Daniela Schranner

Postdoc

Patrick Weinisch

PhD candidate

Lina-Liv Willruth

Research assistant

Maria Wörheide

PhD candidate

Tong Wu

PhD candidate

Current Projects

Multi-omics Data Integration

AD Atlas

The AD Atlas is a network-based data integration resource for investigating Alzheimer's Disease, its biomarkers, and associated endophenotypes in a multi-omics context.

Challenge Studies

Metabolomics & Exercise

Profiling the metabolic response to acute and long-term exercise.

Online Association Databases

OMICSCIENCE

With omicscience.org we provide easy access to the results of several metabolome-wide association studies which otherwise would be hidden in the publication supplements.

Challenge Studies

Metabolomics & Nutrition

How does our food shape the composition of our metabolome?

Data Visualization & Exploration

The HuMet-Repository

Visualize and explore the time-resolved responses of the human metabolism to various challenges.

Genomic Annotation Resource

SNIPA Webservice

A continuously updated interactive, genetic variant-centered annotation browser.

Recent Publications

See all

2022 Nature Communications DOI: 10.1038/s41467-022-30875-7 (2022)

Gemma Cadby, Corey Giles, Phillip E. Melton, Kevin Huynh, Natalie A. Mellett, Thy Duong, Anh Nguyen, Michelle Cinel, Alex Smith, Gavriel Olshansky, Tingting Wang, Marta Brozynska, Mike Inouye, Nina S. McCarthy, Amir Ariff, Joseph Hung, Jennie Hui, John Beilby, Marie-Pierre Dubé, Gerald F. Watts, Sonia Shah, Naomi R. Wray, Wei Ling Florence Lim, Pratishtha Chatterjee, Ian Martins, Simon M. Laws, Tenielle Porter, Michael Vacher, Ashley I. Bush, Christopher C. Rowe, Victor L. Villemagne, David Ames, Colin L. Masters, Kevin Taddei, Matthias Arnold, Gabi Kastenmüller, Kwangsik Nho, Andrew J. Saykin, Xianlin Han, Rima Kaddurah-Daouk, Ralph N. Martins, John Blangero, Peter J. Meikle, and Eric K. Moses

Comprehensive genetic analysis of the human lipidome identifies loci associated with lipid homeostasis with links to coronary artery disease.

We integrated lipidomics and genomics to unravel the genetic architecture of lipid metabolism and identify genetic variants associated with lipid species putatively in the mechanistic pathway for coronary artery disease (CAD). We quantified 596 lipid species in serum from 4,492 individuals from the Busselton Health Study. The discovery GWAS identified 3,361 independent lipid-loci associations, involving 667 genomic regions (479 previously unreported), with validation in two independent cohorts. A meta-analysis revealed an additional 70 independent genomic regions associated with lipid species. We identified 134 lipid endophenotypes for CAD associated with 186 genomic loci. Associations between independent lipid-loci with coronary atherosclerosis were assessed in ∼456,000 individuals from the UK Biobank. Of the 53 lipid-loci that showed evidence of association (P < 1 × 10-3), 43 loci were associated with at least one lipid endophenotype. These findings illustrate the value of integrative biology to investigate the aetiology of atherosclerosis and CAD, with implications for other complex diseases.

2022 Metabolites DOI: 10.3390/metabo12050445 (2022)

Sebastian Gehlert, Patrick Weinisch, Werner Römisch-Margl, Richard T. Jaspers, Anna Artati, Jerzy Adamski, Kenneth A. Dyar, Thorben Aussieker, Daniel Jacko, Wilhelm Bloch, Henning Wackerhage, and Gabi Kastenmüller

Effects of Acute and Chronic Resistance Exercise on the Skeletal Muscle Metabolome.

Resistance training promotes metabolic health and stimulates muscle hypertrophy, but the precise routes by which resistance exercise (RE) conveys these health benefits are largely unknown. Aim: To investigate how acute RE affects human skeletal muscle metabolism. Methods: We collected vastus lateralis biopsies from six healthy male untrained volunteers at rest, before the first of 13 RE training sessions, and 45 min after the first and last bouts of RE. Biopsies were analysed using untargeted mass spectrometry-based metabolomics. Results: We measured 617 metabolites covering a broad range of metabolic pathways. In the untrained state RE altered 33 metabolites, including increased 3-methylhistidine and N-lactoylvaline, suggesting increased protein breakdown, as well as metabolites linked to ATP (xanthosine) and NAD (N1-methyl-2-pyridone-5-carboxamide) metabolism; the bile acid chenodeoxycholate also increased in response to RE in muscle opposing previous findings in blood. Resistance training led to muscle hypertrophy, with slow type I and fast/intermediate type II muscle fibre diameter increasing by 10.7% and 10.4%, respectively. Comparison of post-exercise metabolite levels between trained and untrained state revealed alterations of 46 metabolites, including decreased N-acetylated ketogenic amino acids and increased beta-citrylglutamate which might support growth. Only five of the metabolites that changed after acute exercise in the untrained state were altered after chronic training, indicating that training induces multiple metabolic changes not directly related to the acute exercise response. Conclusion: The human skeletal muscle metabolome is sensitive towards acute RE in the trained and untrained states and reflects a broad range of adaptive processes in response to repeated stimulation.

2022 Alzheimer’s & Dementia DOI: 10.1002/alz.12714 (2022)

Richa Batra, Matthias Arnold, Maria A. Wörheide, Mariet Allen, Xue Wang, Colette Blach, Allan I. Levey, Nicholas T. Seyfried, Nilüfer Ertekin-Taner, David A. Bennett, Gabi Kastenmüller, Rima F. Kaddurah-Daouk, Jan Krumsiek, and Alzheimer’s Disease Metabolomics Consortium (ADMC)

The Landscape of Metabolic Brain Alterations in Alzheimer’s Disease.

Introduction Alzheimer's disease (AD) is accompanied by metabolic alterations both in the periphery and the central nervous system. However, so far, a global view of AD-associated metabolic changes in the brain has been missing. Methods We metabolically profiled 500 samples from the dorsolateral prefrontal cortex. Metabolite levels were correlated with eight clinical parameters, covering both late-life cognitive performance and AD neuropathology measures. Results We observed widespread metabolic dysregulation associated with AD, spanning 298 metabolites from various AD-relevant pathways. These included alterations to bioenergetics, cholesterol metabolism, neuroinflammation, and metabolic consequences of neurotransmitter ratio imbalances. Our findings further suggest impaired osmoregulation as a potential pathomechanism in AD. Finally, inspecting the interplay of proteinopathies provided evidence that metabolic associations were largely driven by tau pathology rather than amyloid beta pathology. Discussion This work provides a comprehensive reference map of metabolic brain changes in AD that lays the foundation for future mechanistic follow-up studies.

2022 Frontiers in Nutrition DOI: 10.3389/fnut.2022.933526 (2022)

Patrick Weinisch, Jarlei Fiamoncini, Daniela Schranner, Johannes Raffler, Thomas Skurk, Manuela J. Rist, Werner Römisch-Margl, Cornelia Prehn, Jerzy Adamski, Hans Hauner, Hannelore Daniel, Karsten Suhre, and Gabi Kastenmüller

Dynamic Patterns of Postprandial Metabolic Responses to Three Dietary Challenges.

Food intake triggers extensive changes in the blood metabolome. The kinetics of these changes depend on meal composition and on intrinsic, health-related characteristics of each individual, making the assessment of changes in the postprandial metabolome an opportunity to assess someone's metabolic status. To enable the usage of dietary challenges as diagnostic tools, profound knowledge about changes that occur in the postprandial period in healthy individuals is needed. In this study, we characterize the time-resolved changes in plasma levels of 634 metabolites in response to an oral glucose tolerance test (OGTT), an oral lipid tolerance test (OLTT), and a mixed meal (SLD) in healthy young males (n = 15). Metabolite levels for samples taken at different time points (20 per individual) during the challenges were available from targeted (132 metabolites) and non-targeted (502 metabolites) metabolomics. Almost half of the profiled metabolites (n = 308) showed a significant change in at least one challenge, thereof 111 metabolites responded exclusively to one particular challenge. Examples include azelate, which is linked to ω-oxidation and increased only in OLTT, and a fibrinogen cleavage peptide that has been linked to a higher risk of cardiovascular events in diabetes patients and increased only in OGTT, making its postprandial dynamics a potential target for risk management. A pool of 89 metabolites changed their plasma levels during all three challenges and represents the core postprandial response to food intake regardless of macronutrient composition. We used fuzzy c-means clustering to group these metabolites into eight clusters based on commonalities of their dynamic response patterns, with each cluster following one of four primary response patterns: (i) “decrease-increase” (valley-like) with fatty acids and acylcarnitines indicating the suppression of lipolysis, (ii) “increase-decrease” (mountain-like) including a cluster of conjugated bile acids and the glucose/insulin cluster, (iii) “steady decrease” with metabolites reflecting a carryover from meals prior to the study, and (iv) “mixed” decreasing after the glucose challenge and increasing otherwise. Despite the small number of subjects, the diversity of the challenges and the wealth of metabolomic data make this study an important step toward the characterization of postprandial responses and the identification of markers of metabolic processes regulated by food intake.

2021 medrxiv DOI: 10.1101/2021.09.14.21263565 (2021)

Maria A. Wörheide, Jan Krumsiek, Serge Nataf, Kwangsik Nho, Anna K. Greenwood, Tong Wu, Kevin Huynh, Patrick Weinisch, Werner Römisch-Margl, Nick Lehner, The AMP-AD Consortium, The Alzheimer’s Disease Neuroimaging Initiative, The Alzheimer’s Disease Metabolomics Consortium, Jan Baumbach, Peter J. Meikle, Andrew J. Saykin, P. Murali Doraiswamy, Cornelia van Duijn, Karsten Suhre, Rima Kaddurah-Daouk, Gabi Kastenmüller, and Matthias Arnold

An Integrated Molecular Atlas of Alzheimer’s Disease.

INTRODUCTION Embedding single-omics disease associations into the wider context of multi-level molecular changes in Alzheimer’s disease (AD) remains one central challenge in AD research. METHODS Results from numerous AD-specific omics studies from AMP-AD, NIAGADS, and other initiatives were integrated into a comprehensive network resource and complemented with molecular associations from large-scale population-based studies to provide a global view on AD. RESULTS We present the AD Atlas, an online resource (www.adatlas.org) integrating over 20 large studies providing disease-relevant information on 20,353 protein-coding genes, 8,615 proteins, 997 metabolites and 31 AD-related phenotypes. Multiple showcases demonstrate the utility of this resource for contextualization of AD research results and subsequent downstream analyses, such as drug repositioning approaches. DISCUSSION By providing a global view on multi-omics results through a user-friendly interface, the AD Atlas enables the formulation of molecular hypotheses and retrieval of clinically relevant insights that can be validated in follow-up analyses or experiments.

Networks and Funding

AMP-AD (NIA/NIH)

The Alzheimer’s Gut Microbiome Project (NIA/NIH)

BiomarKid (JPI/BMBF)

Contacts

Systems Metabolomics

Gabi Kastenmüller

Head of Research Group Systems Metabolomics

56/151
Team CompNeuro

Matthias Arnold

Head of Team CompNeuro

56/150
Team CKDNapp

Jürgen Dönitz

Head of Team CKDNapp