Population structure of Cydia pomonella granulovirus isolates revealed by quantitative analysis of genetic variation

Genetic diversity of viruses is driven by genomic mutations and selection through its host, resulting in differences in virulence as well as host responses. For baculoviruses, which are naturally occurring pathogens of insects and which are frequently sprayed on hundred thousands to millions of hectares as biocontrol agents of insect pests, the phenomenon of virus-host co-evolution is of particular scientific interest and economic importance because high virulence of baculovirus products is essential and emergence of host resistance needs to be avoided as much as possible. In the present study, the population structure of twenty isolates of the Cydia pomonella granulovirus (CpGV), including twelve isolates from different geographic origins and eight commercial formulations, were studied on the basis of next generation sequencing data and by analyzing the distribution of single nucleotide polymorphisms (SNPs). An entirely consensus sequence free quantitative SNP analysis was applied for the identification of 753 variant SNP sites being specific for single as well as groups of CpGV isolates. Based on the quantitative SNP analysis, homogenous, heterogenous as well as mixed isolates were identified and their proportions of genotypes were deciphered, revealing a high genetic diversity of CpGV isolates from around the world. Based on hierarchical clustering on principal components (HCPC) six distinct isolate/group clusters were identified, representing the proposed main phylogenetic lineages of CpGV but comprising full genome information from virus mixtures. The relative location of different isolates in HCPC reflected the proportion of variable compositions of different genotypes. The established methods provide novel analysis tools to decipher the molecular complexity of genotype mixtures in baculovirus isolates, thus depicting the population structure of baculovirus isolates in a more adequate form than consensus based analyses.