Correlation Between Mutation Rate and Genome Size in Riboviruses: Mutation Rate of Bacteriophage Qb

Genome sizes and mutation rates covary across all domains of life. In unicellular organisms and DNA viruses, they show an inverse relationship known as Drake’s rule. However, it is still unclear whether a similar relationship exists between genome sizes and mutation rates in RNA genomes. Coronaviruses, the RNA viruses with the largest genomes ( 30 kb), encode a proofreading 39 exonuclease that allows them to increase replication fidelity. However, it is unknown whether, conversely, the RNA viruses with the smallest genomes tend to show particularly high mutation rates. To test this, we measured the mutation rate of bacteriophage Qb, a 4.2-kb levivirus. Amber reversion-based Luria–Delbrück fluctuation tests combined with mutant sequencing gave an estimate of 1.4 3 1024 substitutions per nucleotide per round of copying, the highest mutation rate reported for any virus using this method. This estimate was confirmed using a direct plaque sequencing approach and after reanalysis of previously published estimates for this phage. Comparison with other riboviruses (all RNA viruses except retroviruses) provided statistical support for a negative correlation between mutation rates and genome sizes. We suggest that the mutation rates of RNA viruses might be optimized for maximal adaptability and that the value of this optimum may in turn depend inversely on genome size. RNA viruses are among the fastest mutating and evolving entities in nature (Domingo 2006; Holmes 2009). However, their mutation rates vary substantially, from 1026 to 1024 substitutions per nucleotide per cell infection (s/n/c) (Sanjuán et al. 2010), and little is known about the mechanistic and evolutionary causes of this variability. Among DNA viruses and unicellular organisms, mutation rates vary inversely with genome size, as first noted by Drake (Drake 1991; Drake et al. 1998; Lynch 2010; Sanjuán et al. 2010; Sung et al. 2012). However, evidence supporting a similar relationship for RNA genomes has remained elusive. This may be due to error and bias in mutation rate estimates, use of different estimation methods, or the relatively narrow range of variation in RNA virus genome sizes. Despite this, some indirect observations support this correlation. First, coronaviruses have the largest RNA genomes and are the only RNA virus family for which a 39-exonuclease proofreading activity has been demonstrated (Minskaia et al. 2006; Eckerle et al. 2007, 2010; Denison et al. 2011; Ulferts and Ziebuhr 2011). Second, there is a weak but significant negative association between genome size and the rate of molecular evolution among RNA viruses (Sanjuán 2012) and, since evolution rates are partly determined by mutation rates, this suggests that the latter may correlate negatively with genome sizes. Analysis of published mutation rates for 11 different riboviruses (i.e., all RNA viruses except reverse-transcribing viruses) suggested a negative correlation with genome size (Sanjuán et al. 2010). However, this finding has to be taken carefully because its significance depended critically on inclusion of bacteriophage Qb, the virus with the smallest genome in this data set (4217 bases). The estimate for this phage was based on pioneer work from Domingo et al., who scored a single G/ A substitution at position 40 from the 39 end of the genome, using T1 RNase digestion (Batschelet et al. 1976; Domingo et al. 1976). The calculated mutation rate, 1.1 3 1023 s/n/c, is the highest reported for an RNA virus (Sanjuán et al. 2010). Extrapolating from a singlenucleotide site, though, can lead to large errors, and the fact Copyright © 2013 by the Genetics Society of America doi: 10.1534/genetics.113.154963 Manuscript received May 8, 2013; accepted for publication July 5, 2013 Corresponding author: Instituto Cavanilles de Biodiversidad y Biología Evolutiva, Valencia, Spain. E-mail: [email protected] Genetics, Vol. 195, 243–251 September 2013 243 that transitions are generally more frequent than transversions makes this value a likely overestimation. Recently, the mutational properties of bacteriophage Qb were further characterized (García-Villada and Drake 2012). In that study, the read-through gene was redundantly expressed from a plasmid to suppress selection against loss-of-function mutations in this gene. Mutations were first scored phenotypically by plating the virus in bacteria lacking the plasmid and then confirmed by sequencing, giving a mutation rate of 1.8 3 1025 s/n/c. However, the estimated rate increased by nearly 10-fold when mutations were scored directly by sequencing instead of using a phenotypic screen first. Therefore, current mutation rate estimates for bacteriophage Qb range across two orders of magnitude. Use of different estimation methods probably contributes to explaining this high level of uncertainty. For instance, in trans-complementation studies, selection may not be fully removed if the expression level or timing of the plasmid gene copy does not match the wild-type expression profile or if this region of the viral genome contains functional RNA structures or other cis-acting elements. Here, we measured the mutation rate of bacteriophage Qb as substitutions per nucleotide per round of copying (s/n/r), using the Luria– Delbrück fluctuation test, a standard estimation method that has been used previously for at least six other RNA viruses (Sedivy et al. 1987; Suárez et al. 1992; Schrag et al. 1999; Chao et al. 2002; Furió et al. 2005; de La Iglesia et al. 2012). To do so, we engineered amber mutant viruses that could grow only on an Escherichia coli amber suppressor strain and then scored revertants to a functional codon by assaying for viral growth in nonsuppressor cells. This yielded an estimate of 1.4 3 1024 s/n/r, the mutation rate per cell (s/n/c) being probably very similar because the phage undergoes approximately one replication cycle per cell. This value was confirmed by a direct plaque sequencing approach in which a larger genome region was analyzed. Careful analysis of potential sources of error or bias in our data and in previously published estimates strongly suggests that the average mutation rate of bacteriophage Qb is probably close to 1024 s/n/c, and comparison with other riboviruses supports the existence of a negative correlation between mutation rates