Computational Based Design, Generation, and Tracking of 500 Synthetic Silent Variants of Porcine Circovirus Reveals the Relations between Silent Genomic Information and Viral Fitness

Viral genomes not only code the protein content, but also include silent, overlapping codes which are important to the regulation of 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.

We describe the first computational-experimental pipeline for studying the effects of viral silent and non-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 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, the genomes of these 500 variants were generated. 4) Competition experiments between the variants were performed in Porcine kidney-15 (PK15) cell-lines. 5) The variants titers were analyzed based on novel next-generation sequencing (NGS) experiments. 6) The features related to the titer of the variants were inferred and their analyses enable detection of various novel silent functional sequence and structural motives. Furthermore, we demonstrate that 50 of the silent variants exhibit higher fitness than the wild type in the analyzed conditions.