The importance of digitalization in the life sciences is rapidly increasing. This opens up completely new applications for medical research that may fundamentally change the understanding of health and illness. Data-intensive research can, therefore, help to improve the prevention, diagnosis and therapy of various clinical pictures.

The current call for proposals is part of the digitalization initiative for science, research and transfer of the MWK, in which, among other things, the funding programs of the Lower Saxony "Vorab" of the Volkswagen Foundation are consistently geared towards the topic of digitalization.

The total funding is around 18 million euros - the funds come from the Lower Saxony "”Vorab” of the Volkswagen Foundation. The selection was made by a seven-member expert commission of the Volkswagen Foundation. Of the total of 54 project applications submitted, 16 are now being funded, including two projects by BREATH scientists.

Dr. David DeLuca, head of the bioinformatics platform at BREATH, and Prof. Dr. Antje Prasse, head of the pulmonary fibrosis outpatient clinic, were successful with their project proposal "From Genome Research to Clinically Relevant Findings for Patients with Pulmonary Fibrosis". By integrating data from the novel technology of RNA sequencing at the single cell level with clinical and molecular biology data, their project will lead to a better understanding of the underlying mechanisms of pulmonary fibrosis. The interdisciplinary team around the bioinformatics specialist is completed by doctors and scientists from the MHH Pneumology Clinic, the Fraunhofer ITEM, the Hannover Veterinary College and is being cordinated within the Translation Alliance in Lower Saxony (TRAIN).

Prof. Dr. Dr. Burkhard Tümmler, head of the Clinical Research Unit Molecular Pathology of Cystic Fibrosis at the Department of Pediatric Pulmonology, Allergology and Neonatology, will investigate the topic "Microbial metagenomics of chronic lung diseases". The aim of the research project is to investigate the microbial respiratory metagenome of healthy volunteers, patients with acute respiratory infections and patients with chronic pulmonary disease (asthma, COPD, cystic fibrosis, bronchiectasis). The data sets are to be classified by so-called machine learning with the aim of identifying the samples of acute and chronic respiratory tract infections on the basis of the microbiome phenotype. The metagenome pipeline is to be optimized for patient care so that the requesting physician receives a microbiological result that is more comprehensible, faster and cost-effective than culture-based diagnostics.

Text: BREATH / CD