Dominik Lutter

Head of the Computational Discovery Research Group

+49 89 3187 2046 dominik.lutter@helmholtz-munich.de

"My research is driven by the vision to enable a systemic understanding of health and metabolism that leads to personalized prevention, predictions, and treatments of metabolic diseases."

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Dominik Lutter’s research focuses on the intersection between applied data science and systemic metabolism. The primary and long-term objective of his research is to advance a systemic view on the development of insulin resistance and Type 2 Diabetes Mellitus (T2DM). In close collaboration with his experimental partners, he follows a systems biology approach to unravel regulatory interactions from genes to metabolism to environment, leading to a better understanding of metabolic diseases and enabling novel diagnostic approaches and personalized intervention strategies.

Dominik studied Biology at the University of Regensburg. In 2007, he joined Helmholtz Munich to finish his PhD following a Postdoc position in the group of Fabian Theis at the Institute for Bioinformatics and Systems Biology (IBIS) and in 2013 the Institute for Diabtes and Obesity (IDO). From 2015 on, he is heading the group Computational Discovery Research (CDR) with the IDO and the Helmholtz Ciabtes Center (HDC).

Computational Biology Multi Omics Systems biology Obesity Diabetes Metabolism

2015

Institute for Diabetes and Obesity, Helmholtz Munich, Germany

2013 - 2015

Institute for Diabetes and Obesity, Helmholtz Munich, Germany

2009 - 2013

Computational Modeling in Biology Group, Institute for Bioinformatics and Systems Biology, Helmholtz Munich, Germany

Stefan Loipfinger, Matthias Grosholz, Santhosh Kumar, Helin Erbilir, Kenneth Allen Dyar, Timo Dirk Müller, Stephan Grein, Jan Rozman, Martin Klingenspor, Carola Meyer & Dominik Lutter

Calopy — an advanced framework for the integration and analysis of indirect calorimetry data Here we introduce Calopy, an innovative software suite for the intuitive and comprehensive analysis of indirect calorimetry data. Calopy is an open-source, web-based Shiny for Python application that is accessible online or locally; it is platform-independent and available via any web browser at https://www.calopy.app.

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.

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.

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.

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.

Dr. Dominik Lutter, PhD

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Professional Career

Head of Computational Discovery Research

Postdoctoral Fellow

Postdoctoral Fellow

Selected Publications

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Helmholtz Munich

HDC

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