Computational Deciphering and Modeling of the Regulatory Information Encoded in the Porcine Circovirus Genome

Porcine circovirus (PCV) is the smallest mammalian virus known (a genome with about 1770 nucleotides) and yet it displays a complex transcription pattern; in addition the rate of nucleotide substitution for PCV2 is the highest recorded for ss-DNA virus, allowing PCV2 to maintain evolutionary dynamics closer to ss-RNA viruses than to ds-DNA viruses. Two major PCV types have been recognized: PCV1 and PCV2. PCV1 is non-pathogenic, whereas PCV2 is associated with many diseases in growing pigs. Since the late 1990s, PCV2 has emerged as a major pig pathogen around the globe. PCV1 and PCV2 are noticeably distinct from one another with less than 76% overall nucleotide sequence homology. PCV2 strains are subdivided into five phylogenetic genotypes: PCV2a – PCV2e.

Thus, developing tools for accurate modeling and engineering PCV strains should contribute both to applied sciences such as developing vaccine methodologies and to basic sciences such as molecular evolution.

In this study, we are analyzing for the first time over 2,000 different PCV1 and PCV2 strains. We report for the first time various silent regulatory signals that are encoded in the genomes of the different PCV strains. These regulatory signals can be used for engineering biotechnological products based on PCV.