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Super computers and quantum processing;
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Division - Computational Discovery Research

Lutter Lab

Lutter Lab

About our Research

Our group is dedicated to advancing our comprehension of systems metabolism. We focus on unraveling the complex interplay between metabolism, genetic regulation, and environmental influences across various tissues and organs. By integrating diverse biological data types, including multi-omics, fluxomics, and metabolic phenotyping, our goal is to gain a comprehensive understanding of metabolism, its regulation, and its implications for metabolic diseases, with a focus on Type 2 Diabetes.

In collaboration with experimental partners, we employ a systems biology approach to build detailed models of molecular interactions and dependencies with data collected from living organisms and metabolically active tissues and organs. We utilize state-of-the-art machine learning techniques and develop custom tools as needed to extract meaningful insights from this data.

In essence, our work aims to push the boundaries of systems metabolism research, providing key insights into the underlying mechanisms of metabolic diseases.


Our Team

Dr. Mariana Ponce-de-Leon


Dr. Konstantinos Makris


Sabrina Alexandra Liedtke

Master Student

Santhosh Kumar

Master Student

Dr. Ekta Pathak


Recent Key Publications

February 2023 Diabetologia

Dominik Lutter, Stephan Sachs, Marc Walter, Anna Kerege, Leigh Perreault, Darcy E. Kahn, Amare D. Wolide, Maximilian Kleinert, Bryan C. Bergman & Susanna M. Hofmann

Skeletal muscle and intermuscular adipose tissue gene expression profiling identifies new biomarkers with prognostic significance for insulin resistance progression and intervention response

Aims/hypothesis: Although insulin resistance often leads to type 2 diabetes mellitus, its early stages are often unrecognised, thus reducing the probability of successful prevention and intervention. Moreover, treatment efficacy is affected by the genetics of the individual. We used gene expression profiles from a cross-sectional study to identify potential candidate genes for the prediction of diabetes risk and intervention response. Methods: Using a multivariate regression model, we linked gene expression profiles of human skeletal muscle and intermuscular adipose tissue (IMAT) to fasting glucose levels and glucose infusion rate. Based on the expression patterns of the top predictive genes, we characterised and compared individual gene expression with clinical classifications using k-nearest neighbour clustering. The predictive potential of the candidate genes identified was validated using muscle gene expression data from a longitudinal intervention study. Results: We found that genes with a strong association with clinical measures clustered into three distinct expression patterns. Their predictive values for insulin resistance varied substantially between skeletal muscle and IMAT. Moreover, we discovered that individual gene expression-based classifications may differ from classifications based predominantly on clinical variables, indicating that participant stratification may be imprecise if only clinical variables are used for classification. Of the 15 top candidate genes, ST3GAL2, AASS, ARF1 and the transcription factor SIN3A are novel candidates for predicting a refined diabetes risk and intervention response. Conclusion/interpretation: Our results confirm that disease progression and successful intervention depend on individual gene expression states. We anticipate that our findings may lead to a better understanding and prediction of individual diabetes risk and may help to develop individualised intervention strategies.

February 2022 Cell Metabolism

Shogo Sato, Kenneth A. Dyar, Jonas T. Treebak, Sara L. Jepsen, Amy M. Ehrlich , Stephen P. Ashcroft, Kajetan Trost, Thomas Kunzke, Verena M. Prade, Lewin Small, Astrid Linde Basse, Milena Schönke, Siwei Chen, Muntaha Samad, Pierre Baldi, Romain Barrès, Axel Walch, Thomas Moritz, Jens J. Holst, Dominik Lutter, Juleen R. Zierath, Paolo Sassone-Corsi

Atlas of exercise metabolism reveals time-dependent signatures of metabolic homeostasis

Tissue sensitivity and response to exercise vary according to the time of day and alignment of circadian clocks, but the optimal exercise time to elicit a desired metabolic outcome is not fully defined. To understand how tissues independently and collectively respond to timed exercise, we applied a systems biology approach. We mapped and compared global metabolite responses of seven different mouse tissues and serum after an acute exercise bout performed at different times of the day. Comparative analyses of intra- and inter-tissue metabolite dynamics, including temporal profiling and blood sampling across liver and hindlimb muscles, uncovered an unbiased view of local and systemic metabolic responses to exercise unique to time of day. This comprehensive atlas of exercise metabolism provides clarity and physiological context regarding the production and distribution of canonical and novel time-dependent exerkine metabolites, such as 2-hydroxybutyrate (2-HB), and reveals insight into the health-promoting benefits of exercise on metabolism.

November 2021 Molecular Metabolism

Valentina S. Klaus, Sonja C. Schriever, José Manuel Monroy Kuhn, Andreas Peter, Martin Irmler, Janina Tokarz, Cornelia Prehn , Gabi Kastenmüller, Johannes Beckers, Jerzy Adamski, Alfred Königsrainer, Timo D. Müller, Martin Heni, Matthias H. Tschöp, Paul T. Pfluger, Dominik Lutter

Correlation guided Network Integration (CoNI) reveals novel genes affecting hepatic metabolism

Objective Technological advances have brought a steady increase in the availability of various types of omics data, from genomics to metabolomics. Integrating these multi-omics data is a chance and challenge for systems biology; yet, tools to fully tap their potential remain scarce. Methods We present here a fully unsupervised and versatile correlation-based method – termed Correlation guided Network Integration (CoNI) – to integrate multi-omics data into a hypergraph structure that allows for the identification of effective modulators of metabolism. Our approach yields single transcripts of potential relevance that map to specific, densely connected, metabolic subgraphs or pathways. Results By applying our method on transcriptomics and metabolomics data from murine livers under standard Chow or high-fat diet, we identified eleven genes with potential regulatory effects on hepatic metabolism. Five candidates, including the hepatokine INHBE, were validated in human liver biopsies to correlate with diabetes-related traits such as overweight, hepatic fat content, and insulin resistance (HOMA-IR). Conclusion Our method's successful application to an independent omics dataset confirmed that the novel CoNI framework is a transferable, entirely data-driven, flexible, and versatile tool for multiple omics data integration and interpretation.

September 2018 Cell

Kenneth A. Dyar, Dominik Lutter, Anna Artati, Nicholas J. Ceglia, Yu Liu, Danny Armenta, Martin Jastroch, Sandra Schneider, Sara de Mateo, Marlene Cervantes, Serena Abbondante, Paola Tognini, Ricardo Orozco-Solis,, Kenichiro Kinouchi, Christina Wang, Ronald Swerdloff, Seba Nadeef, Selma Masri, Pierre Magistretti, Valerio Orlando, Emiliana Borrelli, N. Henriette Uhlenhaut, Pierre Baldi, Jerzy Adamski , Matthias H. Tschöp , Kristin Eckel-Mahan , Paolo Sassone-Corsi

Atlas of Circadian Metabolism Reveals System-wide Coordination and Communication between Clocks

Metabolic diseases are often characterized by circadian misalignment in different tissues, yet how altered coordination and communication among tissue clocks relate to specific pathogenic mechanisms remains largely unknown. Applying an integrated systems biology approach, we performed 24-hr metabolomics profiling of eight mouse tissues simultaneously. We present a temporal and spatial atlas of circadian metabolism in the context of systemic energy balance and under chronic nutrient stress (high-fat diet [HFD]). Comparative analysis reveals how the repertoires of tissue metabolism are linked and gated to specific temporal windows and how this highly specialized communication and coherence among tissue clocks is rewired by nutrient challenge. Overall, we illustrate how dynamic metabolic relationships can be reconstructed across time and space and how integration of circadian metabolomics data from multiple tissues can improve our understanding of health and disease.

Contact us

Dominik Lutter 1

Dr. Dominik Lutter

Group Leader

3620 / 236c