
Exhausted immune cells
Researchers at TWINCORE and MHH investigate the cause of lung damage in autoimmune diseases
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.
Researchers at TWINCORE and MHH investigate the cause of lung damage in autoimmune diseases
TWINCORE researchers show how viruses escape the immune defence system
923,000 € funding for the V³ECTORY project from IBT Lower Saxony
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
We are investigating why HCV infections sometimes heal spontaneously, but often become chronic, and why RSV infections are severe in some children. We use modern sequencing technologies to analyze the genetic characteristics of hosts and pathogens in order to understand susceptibility.
Human, potentially neutralizing antibodies against HEV have advanced the development of new detection methods for the virus in patient samples. Serological and functional analyses are used to determine markers for the course and treatment of chronic infections.
Population genetic studies show that genetic variability between bacterial strains can influence the evolution of antimicrobial resistance. Using automated laboratory evolution (ALE), we are investigating how genetic backgrounds control AMR evolution.
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.