Pipeline for specific subtype amplification and drug resistance detection in hepatitis c virus

Abstract

[Background] Despite the high sustained virological response rates achieved with current directly-acting antiviral agents (DAAs) against hepatitis C virus (HCV), around 5–10% of treated patients do not respond to current antiviral therapies, and basal resistance to DAAs is increasingly detected among treatment-naïve infected individuals. Identification of amino acid substitutions (including those in minority variants) associated with treatment failure requires analytical designs that take into account the high diversification of HCV in more than 86 subtypes according to the ICTV website (June 2017).

[Methods] The methodology has involved five sequential steps: (i) to design 280 oligonucleotide primers (some including a maximum of three degenerate positions), and of which 120 were tested to amplify NS3, NS5A-, and NS5B-coding regions in a subtype-specific manner, (ii) to define a reference sequence for each subtype, (iii) to perform experimental controls to define a cut-off value for detection of minority amino acids, (iv) to establish bioinformatics’ tools to quantify amino acid replacements, and (v) to validate the procedure with patient samples.

[Results] A robust ultra-deep sequencing procedure to analyze HCV circulating in serum samples from patients infected with virus that belongs to the ten most prevalent subtypes worldwide: 1a, 1b, 2a, 2b, 2c, 2j, 3a, 4d, 4e, 4f has been developed. Oligonucleotide primers are subtype-specific. A cut-off value of 1% mutant frequency has been established for individual mutations and haplotypes.

[Conclusion] The methodological pipeline described here is adequate to characterize in-depth mutant spectra of HCV populations, and it provides a tool to understand HCV diversification and treatment failures. The pipeline can be periodically extended in the event of HCV diversification into new genotypes or subtypes, and provides a framework applicable to other RNA viral pathogens, with potential to couple detection of drug-resistant mutations with treatment planning.