Viral genomes not only determine protein products, but also include silent, overlapping codes which are important to the viral life cycle and affects 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.
We describe 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), the shortest known eukaryotic virus, and includes the following steps: 1) Based on the analyses of over 2,500 viral genomes and 2,100 variants of PCV, 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, and in collaboration with Twist Bioscience, 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.
The analyses enable detection of various novel silent functional sequence and structural motives. Furthermore, we demonstrate that 30 of the silent variants exhibit higher fitness than the wild type in the analyzed conditions.