Distribution of Fitness Effects Caused by Single-Nucleotide Substitutions in Bacteriophage f1

Abstract

Empirical knowledge of the fitness effects of mutations is important for understanding many evolutionary processes, yet this knowledge is often hampered by several sources of measurement error and bias. Most of these problems can be solved using site-directed mutagenesis to engineer single mutations, an approach particularly suited for viruses due to their small genomes. Here, we used this technique to measure the fitness effect of 100 single-nucleotide substitutions in the bacteriophage f1, a filamentous single-strand DNA virus. We found that approximately one-fifth of all mutations are lethal. Viable ones reduced fitness by 11% on average and were accurately described by a log-normal distribution. More than 90% of synonymous substitutions were selectively neutral, while those affecting intergenic regions reduced fitness by 14% on average. Mutations leading to amino acid substitutions had an overall mean deleterious effect of 37%, which increased to 45% for those changing the amino acid polarity. Interestingly, mutations affecting early steps of the infection cycle tended to be more deleterious than those affecting late steps. Finally, we observed at least two beneficial mutations. Our results confirm that high mutational sensitivity is a general property of viruses with small genomes, including RNA and single-strand DNA viruses infecting animals, plants, and bacteria.

Viral Mutation Rates

“…We can estimate ␣ from empirical information about the statistical distribution of the fitness effects of random single-nucleotide substitutions, which has been obtained in previous work using site-directed mutagenesis (6,23,73,80). We did so numerically by simulating the effects of mutation and selection.…”

“…Even if the effect of each individual mutation on viral fitness is unknown, the effect of selection can be statistically accounted for as long as the number of mutations sampled for estimating mutation rates is large. We do this here using empirical information about the distribution of mutational fitness effects previously obtained for several viruses (6,23,73,80). Importantly, the basic properties of this distribution appear to be well conserved (78), and hence the proposed method should be applicable to a wide variety of viruses.…”

Can Purifying Natural Selection Preserve Biological Information?

“…This function is similar to a gamma distribution but allows a wider range of fitness effects. The use of this distribution is based on the evidence that even synonymous mutations and mutations in non-coding regions often have at least a very slightly deleterious effect [35,36]. Indeed, two recent papers [23,36] contend that the two-parameter Weibull distribution fits biological reality very well.…”

“…The use of this distribution is based on the evidence that even synonymous mutations and mutations in non-coding regions often have at least a very slightly deleterious effect [35,36]. Indeed, two recent papers [23,36] contend that the two-parameter Weibull distribution fits biological reality very well. Because of the basic similarity of exponential distributions, there is little reason that alternative exponentialtype distributions should give substantially different results.…”

Computational Evolution Experiments Reveal a Net Loss of Genetic Information Despite Selection

“…Viruses are somewhat exceptional for their high mutational sensitivity. Approximately 20 to 41% of viral mutations are lethal, while viable mutations have an average deleterious fitness effect of 0.10 to 0.13, and many mutations appear neutral [50,51]. However, viable mutations of small effect in viruses are still more abundant than those of large effect, and, as Lind et al [49] have noted, it is possible that such experiments report large numbers of neutral mutations because of assays that lack sufficient sensitivity to detect low-impact mutations.…”