Gastrointestinal Infections

Epidemiology of Gastrointestinal Infections

Through epidemiological observations, DZIF scientists improve the prediction of virulences and antibiotic resistances of gastrointestinal pathogens.

The research focus "Epidemiology of Gastrointestinal Infections" currently includes two important studies. Helicobacter pylori is a widespread bacterial pathogen of the human stomach that causes chronic infections and leads to thousands of cases of gastric ulcers and gastric cancer every year. In the HelicoPTER study, 20,000 adults are recruited in collaboration with the "National Reference Centre for Helicobacter pylori". These will not only be tested for infection with H. pylori (H. pylori prevalence), but the researchers will also investigate which H. pylori strains occur in Germany and which antibiotic resistances these strains carry. With the help of these data, algorithms will be developed to predict antibiotic resistances and virulences of H. pylori as well as to identify transmission pathways. The biosamples collected in the course of the study will be transferred to a biobank and form an important basis for further studies.

A second project deals with the epidemiology of Campylobacter jejuni and Campylobacter coli, the most common acute foodborne diarrhoeal pathogens in humans worldwide. In addition to acute diarrhoeal diseases, these bacteria may also be involved in chronic inflammatory bowel diseases in humans. Antibiotic resistance is currently increasing rapidly in these and other gut pathogenic bacteria and has led to their inclusion in the WHO “Global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics” (external link).

By analysing the genome sequences of a large number of representative clinical isolates of the two most important Campylobacter species, the scientists will draw conclusions about the expression and new acquisition of antibiotic resistance as well as novel genetic alterations that contribute to specific antibiotic resistance phenotypes. The data obtained will be used to develop new algorithms to predict resistance and other genetic changes relevant to the pathogenicity of specific bacterial strains.