Viruses depend on their host cells for replication and spread. Although much research has been done on the cellular mechanisms hijacked by viruses, quantitative functional insights in the sets of host cellular genes required for virus infection, the interactions between these genes, and the interactions between viral and host genes remain poorly mapped.

To obtain such information, we were the first to systematically analyse the involvement of human kinases in virus infection, and to compare this, at the systems-level, between two different viruses (Pelkmans, Fava, Grabner, Hannus, Habermann, Krausz, Zerial, 2005). In recent years, we have further developed this approach. As a result, we can now study virus infection with large-scale quantitative unbiased approaches, using computational analysis and data-driven modelling, different means of cellular perturbation (e.g. RNAi and small compounds), and by harnessing a natural source of ‘perturbation’, namely the cell-to-cell variability in molecular and phenotypic readouts of virus-infected cell populations . Results obtained from these approaches and their systems-level comparisons are a great resource for advancing our understanding of mammalian cells and how viruses infect them, and form a basis for the development of broad-spectrum antiviral agents.

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Towards a systems-level analysis of host factors regulating infection across mammalian viruses. Bootstrapped hierarchical clustering of 147 direct siRNA effects on 17 different mammalian viruses in 2 or more cell lines. Per branch-point, three bootstrapped empirical P-values were calculated: Red, approximately unbiased P-value; Green: sub-tree P-value; Blue: leaf-set (i.e. clade or cluster) P-value. Direct effect of DYRK3 (orange) and FRAP1 (blue) silencing for each assay. Per screen, the median value of all three siRNAs, and median of triplicates is shown (i.e. similar to a two out of three criterion). Asterisks indicate gene-silencing phenotypes, which were independently validated. Distribution below the x-axis depicts typical data density of the normal distribution. Note that for visual purposes the x-axis is inverted, with negative values (reduced infection phenotypes) on the right side. The correlation of DYRK3/FRAP1 phenotypes over all assays is 0.48 (P<0.0038). Gene scores for selected genes are shown for all small-scale RNAi screens of virus infection. Bar colours correspond to different genes (see legend) .

 

Current questions:

  • Inferring functional genetic relationships between host genes from the infectome dataset generated in the lab, which contains directly comparable, population context-corrected readouts of perturbations of infection by 7 mammalian viruses for 7,000 human genes, targeted with 3 independent siRNAs in 3 replicate measurements.
  • Mapping the cellular genes involved in different stages of Rotavirus infection, and inferring functional genetic relationships underlying Rotavirus infection.

 

Current lab members involved:

Yauhen Yakimovich

Victoria Green

Rene Holtackers

 

Some relevant publications from the lab:

1.
Pelkmans, L. et al. Genome-wide analysis of human kinases in clathrin- and caveolae/raft-mediated endocytosis. Nature 436, 78–86 (2005).