Exploring the Fitness Landscape of an RNA Virus by Using a Universal Barcode Microarray

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Abstract
Studies of viral pathogenesis have relied heavily on analyses of specific clones and their genetic determinants of virulence. It is sometimes difficult to apply this reductionist approach to the study of RNA viruses, which by virtue of their very high mutation rates, exist as a complex mixture of mutants. While quasispecies theory has provided an intellectual framework for exploring the relationship between the viral population structure and phenotype, experimental studies have been limited by the relatively poor resolution of traditional sequencing-based approaches. We have addressed this problem by developing a molecular barcoding strategy in which viral subpopulations are tagged with unique 20-nucleotide sequences. The behavior of these subpopulations can be monitored using a universal barcode microarray. We demonstrate the performance of our barcode microarray platform using poliovirus, a model RNA virus. Using this platform, we explored the fitness landscape occupied by an artificial quasispecies consisting of 48 randomly mutagenized clones. We were able to rapidly derive precise fitness measurements for a majority of these clones and identified a neutral space surrounding the wild type. The experimental paradigm presented here is readily adaptable to other viral systems and can potentially be used to track thousands of variants in a cost-effective manner.