Identification of HCV protease inhibitor resistance mutations by selection pressure-based method.

A major challenge to successful antiviral therapy is the emergence of drug-resistant viruses. Recent studies have developed several automated analyses of HIV sequence polymorphism based on calculations of selection pressure (K(a)/K(s)) to predict drug resistance mutations. Similar resistance analysis programs for HCV inhibitors are not currently available. Taking advantage of the recently available sequence data of patient HCV samples from a Phase II clinical study of protease inhibitor boceprevir, we calculated the selection pressure for all codons in the HCV protease region (amino acid 1-181) to identify potential resistance mutations. The correlation between mutations was also calculated to evaluate linkage between any two mutations. Using this approach, we identified previously known major resistant mutations, including a recently reported mutation V55A. In addition, a novel mutation V158I was identified, and we further confirmed its resistance to boceprevir in protease enzyme and replicon assay. We also extended the approach to analyze potential interactions between individual mutations and identified three pairs of correlated changes. Our data suggests that selection pressure-based analysis and correlation mapping could provide useful tools to analyze large amount of sequencing data from clinical samples and to identify new drug resistance mutations as well as their linkage and correlations.

Quantification of single nucleotide polymorphisms by automated DNA sequencing.

Single nucleotide polymorphisms (SNPs) are linked to phenotypes associated with diseases and drug responses. Many techniques are now available to identify and quantify such SNPs in DNA or RNA pools, although the information on the latter is limited. The majority of these methodologies require prior knowledge of target sequences, normally obtained through DNA sequencing. Direct quantitation of SNPs from DNA sequencing raw data will save time and money for large amount sample analysis. A high throughput DNA sequencing assay, in combination with a SNP quantitative algorithm, was developed for the quantitation of a SNP present in HCV RNA sequences. For a side-by-side comparison, a Pyrosequencing assay was also developed. Quantitation performance was evaluated for both methods. The direct DNA sequencing quantitation method was shown to be more linear, accurate, sensitive, and reproducible than the Pyrosequencing method for the quantitation of the SNP present in HCV RNA molecules.