Unequal distribution of RT-PCR artifacts along the E1-E2 region of Hepatitis C virus

Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data

BACKGROUND

RNA viruses have high mutation rates and exist within their hosts as large, complex and heterogeneous populations, comprising a spectrum of related but non-identical genome sequences. Next generation sequencing is revolutionising the study of viral populations by enabling the ultra deep sequencing of their genomes, and the subsequent identification of the full spectrum of variants within the population. Identification of low frequency variants is important for our understanding of mutational dynamics, disease progression, immune pressure, and for the detection of drug resistant or pathogenic mutations. However, the current challenge is to accurately model the errors in the sequence data and distinguish real viral variants, particularly those that exist at low frequency, from errors introduced during sequencing and sample processing, which can both be substantial.

RESULTS

We have created a novel set of laboratory control samples that are derived from a plasmid containing a full-length viral genome with extremely limited diversity in the starting population. One sample was sequenced without PCR amplification whilst the other samples were subjected to increasing amounts of RT and PCR amplification prior to ultra-deep sequencing. This enabled the level of error introduced by the RT and PCR processes to be assessed and minimum frequency thresholds to be set for true viral variant identification. We developed a genome-scale computational model of the sample processing and NGS calling process to gain a detailed understanding of the errors at each step, which predicted that RT and PCR errors are more likely to occur at some genomic sites than others. The model can also be used to investigate whether the number of observed mutations at a given site of interest is greater than would be expected from processing errors alone in any NGS data set. After providing basic sample processing information and the site's coverage and quality scores, the model utilises the fitted RT-PCR error distributions to simulate the number of mutations that would be observed from processing errors alone.

CONCLUSIONS

These data sets and models provide an effective means of separating true viral mutations from those erroneously introduced during sample processing and sequencing.

Genetic diversity and evolution of satellite RNAs associated with the bamboo mosaic virus

Satellite RNAs (satRNAs) are subviral agents that depend on cognate helper viruses for genome replication and encapsidation. Their negative impacts on helper viruses have been exploited to control plant viral diseases. SatBaMV is a commonly found satRNA associated with Bamboo mosaic virus (BaMV) that infects diverse bamboo species in the field. To investigate the genetic diversity and evolution of satRNAs, we examined seven satBaMV populations derived from five bamboo species and cultivars from Taiwan, China, and India and one from the greenhouse. We found 3 distinct clades among the seven populations. Clade I is consisted of all satBaMV isolates, except for those from Dendrocalamus latiflorus in Taiwan and Bambusa vulgaris in India, which belong to Clades II and III, respectively. Interestingly, nucleotide diversity was lower for Clade I than II and III. However, the nucleotide diversity did not seem to depend on bamboo species or geographic location. Our population genetic analyses revealed the presence of excessive low-frequency polymorphic sites, which suggests that the satBaMV population was under purifying selection and/or population expansion. Further analysis of P20, the only satBaMV gene that encodes a non-structural protein involved in the long-distance movement of satBaMV, showed evidence of purifying selection. Taken together, our results suggest that purifying selection against defective P20 protein is responsible at least in part for the evolution of the satBaMV genome.

Biomedical implications of viral mutation and evolution

Mutation rates vary hugely across viruses and strongly determine their evolution. In addition, viral mutation and evolution are biomedically relevant because they can determine pathogenesis, vaccine efficacy and antiviral resistance. We review experimental methods for estimating viral mutation rates and how these estimates vary across viral groups, paying special attention to the more general trends. Recent advances positing a direct association between viral mutation rates and virulence, or the use of high-fidelity variants as attenuated vaccines, are also discussed. Finally, we review the implications of viral mutation and evolution for the design of rational antiviral therapies and for efficient epidemiological surveillance.