Time-Resolved Systems Medicine Reveals Viral Infection-Modulating Host Targets
Christian Wiwie,1 Irina Kuznetsova,2 Ahmed Mostafa,2,3 Alexander Rauch,4 Anders Haakonsson,4 Inigo Barrio-Hernandez,4 Blagoy Blagoev,4 Susanne Mandrup,4 Harald H.H.W. Schmidt,5 Stephan Pleschka,2 Richard Ro¨ ttger,1 and Jan Baumbach1,6,*
Abstract
Introduction: Drug-resistant infections are becoming increasingly frequent worldwide, causing hundreds of thousands of deaths annually. This is partly due to the very limited set of protein drug targets known for human-infecting viral genomes. The eleven influenza virus proteins, for instance, exploit host cell factors for replication and suppression of the antiviral immune responses. A systems medicine approach to identify relevant and druggable host factors would dramatically expand therapeutic options. Therapeutic target identification, however, has hitherto relied on static molecular networks, whereas in reality the interactome, in particular during an infection, is subject to constant change.
Methods: We developed time-course network enrichment (TiCoNE), an expert-centered approach for discovering temporal response pathways. In the first stage of TiCoNE, time-series expression data is clustered in a humanaugmented manner to identify groups of biological entities with coherent temporal responses. Throughout this process, the expert can add, remove, merge, or split temporal patterns. The resulting groups can then be mapped to an interaction network to identify enriched pathways and to analyze cross-talk enrichments and depletions between groups. Finally, temporal response groups of two experiments can be intersected, to identify condition-variant response patterns that represent promising drug-target candidates.
Results: We applied TiCoNE to human gene expression data for influenza A virus infection and rhino virus infection, respectively. We then identified coherent temporal response patterns and employed our cross-talk analysis to establish two potential timelines of systems-level host responses for either infection. Next, we compared the two phenotypes and unraveled condition-variant temporal groups interacting on a networks level. The highestranking ones we then validated via literature search and wet-lab experiments. This not only confirmed many of our candidates as previously known, but we also identified phospholipid scramblase 1 (encoded by PLSCR1) as a previously not recognized host factor that is essential for influenza A virus infection.
Conclusion: With TiCoNE we developed a novel approach for conjointly analyzing molecular networks with timeseries expression data and demonstrated its power by identifying temporal drug-targets. We provide proof-ofconcept that not only novel targets can be identified using our approach, but also that anti-infective drug target discovery can be enhanced by investigating temporal molecular networks of the host in response to viral infection.
Keywords: drug discovery; influenza A; time series clustering; network enrichment; network pharmacology; host factors