Computational Virology

RNA viruses are ubiquitously detectable and are among the most widespread biological organisms in the world. They are the cause of many existing and emerging infectious diseases, such as hepatitis and COVID-19. We are fascinated by the diversity and flexibility of RNA viruses and are using state-of-the-art sequencing technologies and molecular biology methods to study the evolution and virus-host interactions of RNA viruses. Although we currently focus on hepatotropic viruses, we have studied a variety of pathogens in the past, such as SARS-CoV-2. The long-term goal of our research is to contribute to the development of novel antiviral strategies through translational collaborations.

1. Evolution of RNA Viruses
RNA viruses are constantly changing due to random mutations. During infection, genetically distinct populations, called quasispecies, can thus arise in a patient. We are studying how RNA viruses adapt and change during infection to identify variants associated with viral persistence or clearance (Gömer et al. 2022). The high mutational potential of RNA viruses also complicates the successful development of vaccines and antiviral drugs, as resistant mutations often occur within weeks or months of treatment initiation. For example, using deep sequencing in patients, we recently demonstrated for the first time that ribavirin exerts a mutagenic effect on the viral genome of hepatitis E virus, leading to the formation of resistant variants (Todt et al., 2016). In the future, we plan to use this technology to identify patients at risk of treatment failure early and enable personalized treatment strategies.

2. Virus-Host Interaction
The clinical course of a viral infection is influenced by both the viral pathogen and the host immune response. We are using state-of-the-art RNA sequencing technology to study the host response to a viral pathogen at high resolution and improve our understanding of viral pathogenesis. In RNA sequencing studies of HEV-infected primary human hepatocytes, we were thus able to observe a temporally structured transcriptional defense response against the virus (Todt et al. 2020). Our goal is to use “machine learning” approaches to analyze transcriptome data to identify patterns that significantly determine the course of infection. In particular, we aim to understand the different immune responses in patients with chronic or acute HEV infections.

3. Viral Fingerprints
NGS sequencing technologies usually generate huge amounts of data, but due to their complexity often only a fraction of these are systematically analyzed and evaluated. For this reason, important new insights can often be gained from existing data sets. In translational collaborations, we develop new analysis pipelines, algorithms and bioinformatics workflows to exploit the full potential of clinically derived sequencing data. In this context, we are particularly interested in datasets from different patient cohorts to detect (co-)infections with multiple viruses, viral mutations and viral integration sites in the host genome and correlate them with clinical features.