
Warm nights and ill premature babies?
A study from Hanover investigates the spread of Klebsiella in the neonatal intensive care unit.

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.
This year's TWINCORE Symposium will take place on 3 - 4 September.

A study from Hanover investigates the spread of Klebsiella in the neonatal intensive care unit.

TWINCORE researchers investigate gene activity in host cells

Diverse Origins – One Goal
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.
Janshoff S, Plümers R, Kohl A, Nocke M, Behrendt P, Knabbe C, Costa R, Vollmer T, Todt D, Steinmann E, Gömer A
Vandenabeele L, Ayanwale A, Pietschmann T, Nilsson-Payant B
Dinkelborg K, Niehaus C, Bremer B, Wundes C, Tiede A, Petruch N, Deterding K, Kraft A, Hartleben B, Cornberg M, Wedemeyer H, Behrendt P, Maasoumy B
The project is developing methods to specifically transport antibiotics into cells such as alveolar macrophages, which are important in Mycobacterium tuberculosis infections. The aim is to overcome resistance and reduce side effects.
We are investigating how antibodies protect against HCV infection, in particular what properties they have during a healing infection. The aim is to identify antibodies that are important for an effective vaccine against HCV.
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.

