Computational based Modeling and Engineering of the Fitness of Synthetic Variants of Porcine Circovirus based on the Analysis of a Large Scale Genomic Data

Viral genomes not only determine protein products, but also include silent, overlapping codes which are important to the viral life cycle and affect its evolution. Due to the high density of these codes, their non-modular nature, and the complex intracellular processes they encode, the ability of the current approaches to decipher them is very limited.

My research is a part of the first computational-experimental pipeline for studying the effects of viral silent information on its fitness. The pipeline was implemented to study the Porcine Circovirus (PCV2), which is a major pig pathogen, and includes the following steps: 1) Based on the analyses of over 2,000 viral genomes suspected silent codes were inferred. 2) 500 variants of the PCV were designed to include various smart silent mutations. 3) Using state of the art synthetic biology approaches the genomes of these 500 variants were generated. 4) Competition experiments between the variants were performed in PK15 cell-lines. 5) The variants titers were analyzed based on novel NGS experiments and in comparison to endogenous PCV genomes.

The analysis enables the detection of various novel silent functional sequences and structural motives in the PCV genome, which can be integrated for predicting the fitness of a PCV variant based on its genome. This predictor can be used both for understating the genome of the PCV and for engineering novel synthetic PCV vaccines.