|Host institution:||Robert Koch Institute (RKI), Germany|
|Local supervisor:||Dr. Max von Kleist (RKI, Bioinformatics Unit)|
|Local co-supervisor:||Prof. Dr. Dino McMahon (FU Berlin, Host Parasite Evolution and Ecology)|
|Project partner:||ESR 4, ESR 9, ESR 2|
Viruses hijack the host cellular machinery for replication. This hijacking is driven by the interaction of viral proteins and non-coding RNAs with host-cellular components. Viral genomic sites instrumental to these interactions are thus conserved through evolution, yet bioinformatics analysis of evolutionary conservation cannot tell apart and quantify the functional relevance of genomic sites under selection. We have previously shown using the Mutational Interference Mapping Experiment (MIME) that in vitro and in vivo evolution experiments with subsequent NGS generate data sets that allow quantifying the phenotype of every nucleotide in a single experiment.
The goal of this project is to improve and tailor mathematical and computational methods to identify and functionally characterise domains in the viral genome under evolutionary selection and to apply these tools to data from ESR 4, ESR 9, ESR 2 and our secondment.
- On the technical side, we will learn maximum entropy models (direct coupling analysis, DCA) from nucleotide abundances in functionally selected and de-selected virus quasispecies to identify single- and interacting sites.
- As an alternative approach and a means to validate the above, we investigate the application of deep learning approaches to identify higher-order epistasis in collaboration with secondment Bernhard Renard (HPI).
- We then derive algebraic expressions from kinetic modelling of the investigated selection process to interpret the derived coupling terms mechanistically (phenotypically). This allows to characterise the complex (and possibly highly constraining) fitness landscape on which adaptation takes place.
In the development phase our computational methods will be benchmarked by simulating the selection experiments and the corresponding NGS data sets, as well as with existing NGS datasets and phenotypic endpoints from previous work (e.g. HIV genome packaging). Subsequently, we will apply the tools to NGS data generated by in vitro evolution experiments of ESR 2 / secondment R. Smyth (HIRI) to investigate domains in the influenza genomic RNA, which are responsible for viral packaging/re-assortment, in collaboration with AllGenetics. Moreover, we will apply the methods to in vivo evolution experiments conducted by ESR 4 (deformed wing virus), as well as ESR 9 (Drosophila virus) to predict phenotypic contributions to virus growth, mortality and host transcriptional responses.