Empirical estimates of the mutation rate for an alphabaculovirus

Abstract

Mutation rates are of key importance for understanding evolutionary processes and predicting their outcomes. Empirical mutation rate estimates are available for a number of RNA viruses, but few are available for DNA viruses, which tend to have larger genomes. Whilst some viruses have very high mutation rates, lower mutation rates are expected for viruses with large genomes to ensure genome integrity. Alphabaculoviruses are insect viruses with large genomes and often have high levels of polymorphism, suggesting high mutation rates despite evidence of proofreading activity by the replication machinery. Here, we report an empirical estimate of the mutation rate per base per strand copying (s/n/r) of Autographa californica multiple nucleopolyhedrovirus (AcMNPV). To avoid biases due to selection, we analyzed mutations that occurred in a stable, non-functional genomic insert after five serial passages in Spodoptera exigua larvae. Our results highlight that viral demography and the stringency of mutation calling affect mutation rate estimates, and that using a population genetic simulation model to make inferences can mitigate the impact of these processes on estimates of mutation rate. We estimated a mutation rate of μ = 1×10-7 s/n/r when applying the most stringent criteria for mutation calling, and estimates of up to μ = 5×10-7 s/n/r when relaxing these criteria. The rates at which different classes of mutations accumulate provide good evidence for neutrality of mutations occurring within the inserted region. We therefore present a robust approach for mutation rate estimation for viruses with stable genomes, and strong evidence of a much lower alphabaculovirus mutation rate than supposed based on the high levels of polymorphism observed.

Fig 1. Illustration of the neutral region (green diagonal bars) used for estimating mutation rate.

ORF603 and ORF1629 (light blue) are native AcMNPV genes, between which lies the polyhedrin gene which was disrupted (box with magenta bars) by the insertion of the bacmid sequences in its 5’ end. To restore polyhedrin expression the gene has been reinserted under control of its native promotor within the bacmid insert. We consider the sequences of bacterial origin in the insert and the remnants of the pseudogenized polyhedrin gene copy as neutral sequences.

Fig 2. Mutation rate estimates (s/n/r, mutations per site per strand copying) derived with the model are given based on the neutral bacmid region and the whole genome.

We estimated mutation rates for different values of the threshold frequency for detecting mutations (τ), noted as percentages here, and for different values of the maximum number of lineages in which a mutation could occur before being excluded from the analysis (ψ). Error bars represent the 95% confidence interval, as determined by bootstrapping. Note that for the neutral region and τ = 2.0%, the lower fiducial limit extends to zero due to the low number of mutations detected.

Fig 3. Trends for the rates of intergenic (dI) or non-synonymous (dN) mutations are reported, all normalized by the rate of synonymous mutations in native viral genes (dS), for the different values used for the mutation threshold value (τ) and the maximum number of lineages in which a mutation can occur before being excluded from the analysis (ψ).

Error bars indicate the 95% confidence interval of the estimate as determined by bootstrapping, and results of a one-sample t-test on log₁₀-transformed dI/dS or dN/dS values are indicated by ns (non-significant, p > 0.05), * (p < 0.05), ** (p < 0.01) and *** (P < 0.001). Note that for many conditions the confidence interval could not be determined, or the test could not be performed, as one or more values for a mutation class were zero, in which case no test results are indicated. The bars on the left labelled “dI/dS (Bacmid insert)” indicate the rate of intergenic mutations in the artificial neutral regions. These mutations occur with a normalized rate close to 1, indicating neutral evolution. The central bars labelled “dN/dS (Virus genome)” indicate the rate of non-synonymous mutations. These mutations were under-represented compared to synonymous mutations, indicating purifying selection. Finally, the columns on the right labelled “dI/dS (Virus genome)” indicate the rate of intergenic mutations in the viral genome, with bars extending to a value of 0.01 indicative of a value of zero. These results were inconclusive, as dI/dS was strongly dependent on the threshold for mutation detection chosen.

Fig 4. An overview of known mutation rate estimates (s/n/r, ordinate) for viruses with different genome sizes (abscissa) is given.

The color and shapes of symbols indicate the Baltimore classification group to which each virus belongs, as indicated by the legend in the top right of the figure. The solid black line marks the regression line, with dotted lines marking the 95% confidence interval. The slope of the fitted relationship is significantly lower than zero (t₈ = -3.243, p = 0.012), and the coefficient of determination (r²) is 0.568. Our mutation rate estimate for AcMNPV is in good agreement with the fitted relationship between genome size and mutation rate. Other virus names in the figure are Enterobacteria phage T2 (T2), Escherichia virus λ (λ), Escherichia virus ΦX174 (phiX174), Influenza A virus (IAH), Measles virus (MV), Poliovirus (PV), Pseudomonas virus Φ6 (phi6), Turnip mosaic virus (TuMV), and Vesicular stomatitis virus (VSV).