Helicobacter pylori (H. pylori) infection is one of the most prevalent bacterial infections worldwide. Chronic infection leads to chronic active gastritis and can result in the development of other complications such as ulcers or gastric cancer. Indeed, approximately 90% of all gastric cancers are associated with H. pylori. Failure of standard eradication therapies is rising dramatically due to the increased development of resistant bacterial strains. Since two antibiotics are needed for successful eradication, the usage of only one antibiotic in other indications such as respiratory diseases will render the (mostly yet undetected) H. pylori strain in these patients resistant. Nowadays, it is estimated that already 10-20% of H. pylori strains are multiresistant. However, culture-based resistance testing, which is currently only recommended after unsuccessful second-line therapy, is a lengthy process; in vitro growth of H. pylori takes 5-7 days after isolation from gastric tissue and further resistance tests last between 3 to 5 days. Taking this into account, a rapid method to determine whether an isolated strain will be prone to antibiotic resistance would be a tremendous help to choose the appropriate therapeutic regimen.To address this challenge, we plan to develop an algorithm for prediction of antibiotic resistance, which will primarily be based on H. pylori genome sequencing data that can be obtained quickly. The algorithm will be made publicly available to physicians and serve as a way to select the optimal therapy. This approach will help to optimize therapeutic efficacy and counteract further resistance development.
Although some mutations in genes related to antibiotic resistance clearly correlate with phenotypic resistance, the significance of many mutations is unclear as phenotypic resistance can often not be associated with a single specific mutation. A drawback of most studies in this context is the small sample size, which will be addressed in our project by including data from up to 2000 patients and their respective H. pylori strains. Such a comprehensive approach will enable us to include in our analysis, in addition to genomic and phenotypic data, other factors that can contribute to resistance in vivo. For example, previous antibiotic treatment for a different condition might interfere with a later eradication therapy for H. pylori infection. In addition, the inflammatory response and degree of gastric pathology can eventually influence therapy success. Another important source for the development of resistances are co-inhabiting bacteria in the gastrointestinal tract, since antibiotic resistances can be transferred between different bacterial species by direct plasmid transfer or through common bacteriophages. Hence, the gastric and gut microbiota may also play an important role in the acquisition of antibiotic resistances. Therefore, we will build a database including genotypic and phenotypic bacterial data, host inflammation-associated parameters as well as microbiota signatures. Based on these data we will develop an algorithm for resistance prediction, which will be incorporated into an online platform. Once completed, the online prediction tool will be tested using selected strains for validation, and then opened for public access.
The aim of the research consortium is the development of a genotypic resistance testing database for the prediction of antibiotic susceptibility of H. pylori. Based on data from more than 2000 patients, we will build a database including genotypic and phenotypic bacterial data, host inflammation-associated parameters as well as microbiota signatures. We will make genetic resistance testing available based on this database in the sense of a “genotype to phenotype concept”. To this end, we will develop an algorithm for prediction of antibiotic resistance that will be made publicly available to physicians and serve as a way to select the optimal therapy. The algorithm will be based on whole genome sequencing of H. pylori but also include other parameters putatively influencing resistance in the stomach such as local inflammation and the gut microbiome, and use machine learning to continuously improve the accuracy.
According to the WHO, antimicrobial resistances such as the ones we find in tuberculosis currently pose the greatest long-term threat to human health and wellbeing. This project builds on novel, data science approaches within basic research to address and counteract the development and spread of resistance within this infectious disease.
Prof. Dr. Sebastian Suerbaum:
At the Ludwig-Maximilian-University, metagenomic sequencing is performed, and genomes of all individual H. pylori strains isolated from the study population are reconstructed and analyzed for mutations in resistance-related genes. Mutations are mapped and compared to published databases and literature, and correlated with phenotypic resistance profiles.
Prof. Dr. Markus Gerhard:
Prof. Gerhard’s group will perform microbiome sequencing of stomach and stool samples. After biostatistic analysis and description of the individual micobiome compositions, data are also included into the database for correlation with other parameters.
PD Dr. Christian Schulz:
At the Klinikum der Universität München, endoscopies will be performed. Gastric biopsies are taken for H. pylori culture, histology and stomach microbiome analysis; stool samples are collected for fecal microbiome analysis. All patient-related data are collected and entered into the study database.
Dr. Atefeh Kazeroonian:
The Technical University Munich will develop algorithms for resistance prediction based on mutations/single-nucleotide polymorphisms in the sequenced H. pylori genome and other parameters possibly influencing resistance (e.g., smoking, co-medication, virulence factors, and microbiome signatures). Models will be optimized for prediction, most importantly by carefully selecting only the most informative variables.
DZIF funded study “Helicobacter pyloriPrevalence and Antibiotic Resistance” (HPPreRes): In this study, 20,000 healthy volunteers are screened for H.pylori infection serologically. Positive volunteers will be offered the possibility to receive an upper endoscopy with biopsy sampling. 2.000 H. pylori positive volunteers are enrolled to undergo endoscopy at the Klinikum der Universität München. The samples from these 2.000 volunteers will form the basis for the Helicopredict project. The partners involved in this DZIF trial have already successfully been working together in several other DZIF trials.
Dr. Atefeh Kazeroonian
Technische Universität München
Institut für Medizinische Mikrobiologie
Immunologie und Hygiene