Press release of the Bavarian State Ministry of Sciences and Arts (20.01.2020)

Fight against multi-resistant germs: Free State invests over 10 million euros in basic research for medical progress:

MUNICH. Developing fundamentally new approaches against multi-resistant germs is the goal of the new Bavarian research network “New strategies against multi-resistant pathogens using digital networking – bayresq.net”. The Free State of Bavaria is providing over 10 million euros for this purpose. This was announced today by Science Minister Bernd Sibler in Munich. Six interdisciplinary research groups will each receive up to 275,000 euros annually for five years from 2020 for their groundbreaking futurological research in the field of multi-resistant pathogens, which is highly relevant to health policy.

The six futurology projects will be conducted – in some cases jointly – at the Friedrich-Alexander University (FAU) Erlangen-Nuremberg, the Ludwig-Maximilian University Munich (LMU), the Technical University Munich (TUM), the University of Regensburg and the Julius-Maximilian University Würzburg (JMU). They use the potential of digital methods, for example, to selectively direct new forms of antibiotics against certain pathogens, thus sparing other types of bacteria, especially the protective intestinal bacteria. High-throughput methods and machine learning will be used to automate this adaptation. The use of Big Data in turn will also enable new approaches, such as predictions of bacterial resistance and virulence based on genome analysis. In this way, a targeted therapy can be made possible.

“New ways to protect our health”

“We need interdisciplinary basic research in order to effectively counter the global threat of multi-resistant pathogens. The new research network ‘bayresq.net’ will help to close a major gap in the research and long-term control of these pathogens. Our universities in the Free State have essential expertise in this area. We are relying on their expertise to gain new insights in this field, to further promote the important interdisciplinary exchange and thus find new ways to protect our health,” said the Minister. Scientists from various disciplines are involved, above all from biology, bioinformatics, chemistry, biophysics, medicine and mathematics.

In addition to the research groups, the Free State’s funding will also enable the establishment of a central data platform and joint data management. “This research network shows how we can use the advantages of digitisation for progress in medicine. We can thus further enhance our excellent reputation as a research location,” emphasised Sibler. The project is part of the Free State’s BAVARIA DIGITAL strategy.

Within the framework of the Bavarian research network “New strategies against multi-resistant pathogens using digital networking – bayresq.net”, these interdisciplinary projects are being funded (Participating university(ies): Title of the research project):

  • Ludwig-Maximilians-Universität München (LMU) and Technische Universität München (TUM): Artificial-intelligent-assisted translation of a new bioassay to decipher the dynamic mode of action of tuberculosis-active antibiotics for the development of new combination therapies for multi-resistant and dormant tuberculosis
  • LMU and TUM: Helicopredict – Genotype to Phenotype: Development of a platform for genome-based resistance and virulence prediction in Helicobacter pylori
  • University of Regensburg: The host metabolism as an antimicrobial effector (metabodefense)
  • University of Regensburg and Friedrich-Alexander-University Erlangen-Nuremberg (FAU): Identification of commensal bacteria-associated immune checkpoints as novel targets for immunotherapy against multidrug-resistant Staphylococcus epidermidis strains
  • Julius-Maximilians-University (JMU) Würzburg: A Digital Approach to Novel RNA Antibiotics for Health and Disease
  • JMU and LMU: Identifying stressor-regulator pairs involved in bacterial stress response, virulence, and antibiotic sensitivity using high-throughput approaches and machine learning

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