
When bacteria remember
New insights into the infection strategy of Pseudomonas aeruginosa
TWINCORE was founded in 2008 by the Helmholtz Centre for Infection Research and the Hannover Medical School. We combine the expertise of medical professionals and scientists from a wide range of disciplines to find answers to the pressing questions in infection research. Our focus: translational research – the bridge between basic science and clinical application.
New insights into the infection strategy of Pseudomonas aeruginosa
Research team from Bochum and Hannover shows that the hepatitis E virus also attacks organs other than the liver
Matthias Bruhn wins the eBioMedicine cover competition.
We conduct translational infection research to improve the prevention, diagnosis and treatment of infectious diseases in humans. We focus on three areas that characterize our research work. Find out here how we proceed and what results we achieve.
Under the leadership of our best scientists, various labs are working on different projects within our research topics.
Bartsch Y, Webb N, Burgess E, Kang J, Lauffenburger D, Julg B
Haller R, Cai Y, DeBuhr N, Rieder J, Schlüter D, Baier C, Rohde H, von Köckritz-Blickwede M, Vital M, Winstel V
Chou Y, Cornberg M
The project investigates the immune response of the central nervous system in viral infections, in particular the role of type I IFN, microglia and monocytes in the development of encephalitis and their influence on seizures and hippocampal damage.
Older people are at high risk of a poor immune response to the flu vaccine. Together with partners, we are looking for biomarkers and risk factors for this inadequate response and are investigating ways to improve the vaccination response.
By applying statistical genetics methods to pathogen genome sequences, we aim to identify and validate genetic determinants of phenotypes such as pathogenicity, virulence and antibiotic resistance, e.g. in E. coli and P. aeruginosa.
Thanks to high-throughput sequencing, genome sequences of hundreds of bacterial strains can be analyzed efficiently, revealing differences of up to 60 % in gene content, as in E. coli. With the help of machine learning, we want to better predict the functions of accessory genes and decipher their contribution to survival in specialized niches.
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