Dr. Dominik Lutter, PhD
Head of the Computational Discovery Research Group"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."
"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."
Academic Pathway
Dominik Lutter’s research focusses 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).
Expertise
Computational Biology Multi Omics Systems biology Obesity Diabetes Metabolism
Professional Career
Head of Computational Discovery Research
Institute for Diabetes and Obesity, Helmholtz Center Munich, Germany
Postdoctoral Fellow
Institute for Diabetes and Obesity, Helmholtz Center Munich, Germany
Postdoctoral Fellow
Computational Modeling in Biology Group, Institute for Bioinformatics and Systems Biology, Helmholtz Center Munich, Germany
Selected Publications
Two-stage evolution of mammalian adipose tissue thermogenesis
Thermogenesis, the ability to generate heat and maintain body temperature in cold conditions, was an important adaptation that allowed mammals to thrive on Earth. Keipert et al. explored the evolutionary origin of uncoupling protein 1 (UCP1), which acts at the mitochondria to uncouple respiratory metabolism from ATP generation, thus allowing the release of energy as heat (see the Perspective by Grabek and Sprenger). The authors analyzed transcriptome profiles from opossums and mice, representatives of marsupial and eutherian lineages that diverged about 150 million years ago. They also characterized a protein made from the predicted eutherian ancestral UCP1 sequence. Expression of UCP1 appears to have first evolved in adipose tissue and functioned in juvenile cold stress in the common therian ancestor. However, marsupials lack thermogenic UCP1, indicating that thermogenesis emerged in a second stage in the eutherians. —L. Bryan Ray
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.
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.
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.
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.
Diet triggers specific responses of hypothalamic astrocytes in time and region dependent manner
Hypothalamic astrocytes are particularly affected by energy-dense food consumption. How the anatomical location of these glial cells and their spatial molecular distribution in the arcuate nucleus of the hypothalamus (ARC) determine the cellular response to a high caloric diet remains unclear. In this study, we investigated their distinctive molecular responses following exposure to a high-fat high-sugar (HFHS) diet, specifically in the ARC. Using RNA sequencing and proteomics, we showed that astrocytes have a distinct transcriptomic and proteomic profile dependent on their anatomical location, with a major proteomic reprogramming in hypothalamic astrocytes. By ARC single-cell sequencing, we observed that a HFHS diet dictates time- and cell- specific transcriptomic responses, revealing that astrocytes have the most distinct regulatory pattern compared to other cell types. Lastly, we topographically and molecularly characterized astrocytes expressing glial fibrillary acidic protein and/or aldehyde dehydrogenase 1 family member L1 in the ARC, of which the abundance was significantly increased, as well as the alteration in their spatial and molecular profiles, with a HFHS diet. Together, our results provide a detailed multi-omics view on the spatial and temporal changes of astrocytes particularly in the ARC during different time points of adaptation to a high calorie diet.
Networks and Affiliations
Helmholtz Munich
HDC
DZD
