Products of the body’s own metabolism not only have a regulatory effect on the immune system, but can also influence the growth or persistence of bacteria. The contribution of host metabolites in antimicrobial defence is still largely unexplored. We postulate that a targeted modulation of the host metabolism can inhibit pathogen growth and develop an antimicrobial efficacy against persisters. We will investigate this paradigmatically with a Salmonella infection model. We will use methods of bioinformatics and machine learning to identify new antimicrobially effective target structures anchored in the metabolism from highly complex metabolome and transcriptome data. In the Jantsch-Lab macrophages will be infected with Salmonella and different signalling pathways of the macrophages will be disturbed. In the Dettmer-Lab, genome-wide gene expression analyses (in collaboration with the Genomics Core Unit of the University of Regensburg), comprehensive metabolome analyses as well as targeted quantitative metabolite and metabolic tracer analyses are performed on samples obtained from infected macrophages. The Spang group develops predictive models of pathogen control, network modelling of host-pathogen interaction and causal models for the identification of putative antimicrobial target structures. The new candidates are then validated in vitro and in vivo in the Jantsch-Lab and Dettmer-Lab. This will provide the basis for new approaches to host-based therapy of multi-resistant pathogens.
Multi-resistant pathogens do not infect everyone they affect. Some people have high-end macrophages that control and fend off the infection, while other people’s macrophages cannot. We suspect the difference between these scavenger cells of the immune system in their metabolism. As a rule, nobody knows how fit his macrophages will be in the event of an infection. Therefore, it is important to better understand the characteristic properties of potent macrophages and then make these properties diagnosable. In this way, high-risk patients for infections could be identified early on. Furthermore, the metabolism of macrophages can be influenced in different ways not only by a variety of drugs, but also by an inflammatory reaction (metaflammation) caused by excessive food intake and triggered by metabolic processes. We do not yet know how these factors affect the fitness of macrophages to defend themselves against pathogens.
In this project, we rely on a strategy that combines modern metabolic analysis and artificial intelligence methods with experimental infection immunology. All three areas have made great progress in recent years and we see the opportunity to make rapid progress in their networking. Therefore, research teams from all three areas are working together in our project. The Jantsch-Lab has set itself the goal of investigating the interplay between infection defence and immune metabolism and is responsible for the experimental work on infected macrophages. The Dettmer-Lab is well established in the field of metabolomics, i.e. metabolic analysis, and generates in our project high-dimensional measurement data on macrophage metabolism using modern mass spectrometers. In these data sets, the Spang group uses artificial intelligence methods to search for data patterns that can be used for diagnostics or the detection of therapeutic target structures. The group has been developing such algorithms for many years and applies them in clinical contexts.
Our goal is to establish the scientific basis for an approach focused on macrophage metabolism for the diagnosis, prevention and therapy of infections caused by multi-resistant pathogens. We therefore study the metabolism of infected macrophages and investigate their ability to control the ingested infectious agents. Using artificial intelligence methods, we aim to identify patterns in the metabolism that characterize high-end macrophages in particular. In our infection experiments we also intervene experimentally in the metabolism in order to mimic the potential effect of drugs. In addition, we use intelligent algorithms of causal inference to understand how specific therapeutic interventions in macrophage metabolism alter its antimicrobial properties. In this way, we want to learn how to reprogram ordinary macrophages into high-end macrophages.
Macrophages (red) phagocytose bacteria (green)
From the knowledge thus gained, new diagnostic and also immunotherapeutic approaches to combat multi-resistant germs can be derived. The classical antibiotic is to be supplemented by a new therapeutic principle. Instead of killing the germ directly, we strengthen the immune system in controlling the infection. The AI and medical research location Bavaria offers us ideal starting conditions for implementing this project. A strategy for fighting infections that arises from this approach could also be pursued within the framework of a spin-off company. However, the results could also strengthen our healthcare systems far beyond Bavaria and open up new therapeutic options for patients.
The Jantsch-Lab is active in the field of infection immunology. One of the main focuses of research is the role of the immune metabolism in the infection defence mediated by cells of the innate immune system.
The Dettmer-Lab focuses on the field of comprehensive qualitative and quantitative metabolic analysis using coupled mass spectrometric methods (metabolomics).
The Spang group focuses on bioinformatics and machine learning. This includes the predictive modeling of high-dimensional molecular data, the development of new statistical/algorithmic methods for the analysis of highly complex data sets and the modeling of biological networks and processes of causal discovery.
Cooperations
Genomics Core Unit, Universität Regensburg
Prof. Dr. Michael Hensel, Universität Osnabrück
Prof. Dr. Dirk Bumann, Biozentrum Basel
Prof. Dr. Jonathan Jantsch
Project Management
Universität Regensburg
Institut für Medizinische Mikrobiologie & Hygiene
PD Dr. Katja Dettmer-Wilde
Project Management
Universität Regensburg
Institut für Funktionelle Genomik
Prof. Dr. Rainer Spang
Project Management
Universität Regensburg
Institut für Funktionelle Genomik
Universität Regensburg
Institut für Medizinische Mikrobiologie & Hygiene
Universität Regensburg
Institut für Funktionelle Genomik