Molecular Clocks and the Puzzle of RNA Virus Origins

Abstract

Although the ultimate origins of RNA viruses are uncertain, it seems reasonable to assume that these infectious agents have a long evolutionary history, appearing with, or perhaps before, the first cellular life-forms (38). While the RNA viruses we see today may not date back quite this far, the evidence that some DNA viruses have evolved with their vertebrate hosts over many millions of years (24) makes an equally ancient history for RNA viruses a natural expectation. Yet a very different picture of RNA virus origins is painted if their gene sequences are compared; by using the best estimates for rates of evolutionary change (nucleotide substitution) and assuming an approximate molecular clock (21, 33), it can be inferred that the families of RNA viruses circulating today could only have appeared very recently, probably not more than about 50,000 years ago. Hence, if evolutionary rates are accurate and relatively constant, present-day RNA viruses may have originated more recently than our own species.Before discussing the solutions to this apparent paradox, it is important to determine exactly why the molecular clock estimates of RNA virus origins are so recent. The key to establishing a timescale of viral evolution lies in accurately determining the rate of nucleotide substitution. Most analyses undertaken to date suggest that the average rate of nucleotide substitution in RNA viruses is ϳ10 Ϫ3 substitutions per site per year, with an approximately fivefold range around this (21). The fact that broadly similar rates are found in RNA viruses with very different genome organizations and lifestyles implies that both the error rate associated with RNA polymerase, estimated to be about one mutation per genome replication (10), and the rate of viral replication are roughly constant. If the average substitution rate of ϳ10 Ϫ3 substitutions/site/year is accurate, then, on average, every nucleotide position will have fixed 1 substitution after ϳ1,000 years of evolution (corresponding to an average divergence time between two lineages of only 500 years). This also corresponds to an evolutionary (corrected) distance (d) between two sequences of 1.0, the maximum that can be reliably estimated through sequence comparisons; larger distances will be inherently inaccurate because of uncounted multiple substitutions at single sites. Of course, reality is a little more complex because viral genomes are a patchwork of synonymous sites, where mutations do not change the encoded amino acid, and nonsynonymous sites, where mutations alter amino acids and which usually evolve more slowly. If we conservatively assume that the substitution rate at nonsynonymous sites is roughly 100-fold less than that at synonymous sites, at ϳ10 Ϫ5 substitutions/site/year, then a distance d of 1 corresponds to a divergence time of ϳ50,000 years ago, although the greater influence of natural selection at nonsynonymous sites is likely to increase the variance around this. Hence, if two RNA viruses have an evolutionary distance of Ͻ1.0 at nonsynonymous si...

Endogenous hepadnaviruses, bornaviruses and circoviruses in snakes

“…It is known that many factors may influence viral substitution rates and their variation over time [72]. Various methodological biases probably influence the calculation of both short-and long-term rates and therefore could explain the large difference between these rates [3,5,73,74], which has also been observed in cellular organisms [75]. Specifically, it has been recently shown that pervasive purifying selection could be one of the major obstacles in accurately estimating the ancient age of recent pathogens [76], and the development of substitution models accounting for this phenomenon are beginning to yield more realistic results [76,77].…”

Evolutionary timescale of rabies virus adaptation to North American bats inferred from the substitution rate of the nucleoprotein gene

“…Currently, the validity of applying a molecular clock to RNA virus evolution is unclear (Holmes, 2003). Saturation of synonymous mutations (leading to potential underestimations of substitution rates; Holmes, 2003), recombination (reviewed by Awadalla, 2003), RNA secondary structure (Simmonds & Smith, 1999) and selection pressures can undoubtedly all contribute to misleading estimates of evolutionary rate.…”

“…Computational methods to estimate substitution rate are problematic (Drummond et al, 2003) and the validity of applying clocklike models to viral sequence data is contentious (Holmes, 2003). Recently, Bayesian techniques using Markov Chain Monte Carlo (MCMC) methods have been developed for estimating substitution rates from dated sequences (Drummond et al, 2002) and have been successfully applied to RV where estimations of evolutionary rate and subsequent divergence times were shown to be both comparable to maximum-likelihood methods and supported by field prevalence data .…”

Analyses of evolutionary dynamics in viruses are hindered by a time-dependent bias in rate estimates

“…RNA viruses display the largest rate disparities among timescales [5,20]. An important example is the primate lentiviruses, which include human and simian immunodeficiency viruses (HIV and SIV, respectively).…”