4.7 Article

Analysis of hepatitis C NS5A resistance associated polymorphisms using ultra deep single molecule real time (SMRT) sequencing

期刊

ANTIVIRAL RESEARCH
卷 126, 期 -, 页码 81-89

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.antiviral.2015.12.005

关键词

Hepatitis C virus; NS5A; Resistance; RAV; Deep sequencing; Pacific biosciences

资金

  1. Uppsala-Orebro Regional Research Council
  2. Selander Foundation
  3. RFI/VR
  4. Science for Life Laboratory, Sweden

向作者/读者索取更多资源

Development of Hepatitis C virus (HCV) resistance against direct-acting antivirals (DAAs), including NS5A inhibitors, is an obstacle to successful treatment of HCV when DAAs are used in sub-optimal combinations. Furthermore, it has been shown that baseline (pre-existing) resistance against DAAs is present in treatment naive-patients and this will potentially complicate future treatment strategies in different HCV genotypes (GTs). Thus the aim was to detect low levels of NS5A resistant associated variants (RAVs) in a limited sample set of treatment-naive patients of HCV GT1a and 3a, since such polymorphisms can display in vitro resistance as high as 60000 fold. Ultra-deep single molecule real time (SMRT) sequencing with the Pacific Biosciences (PacBio) RSII instrument was used to detect these RAVs. The SMRT sequencing was conducted on ten samples; three of them positive with Sanger sequencing (GT1a Q30H and Y93N, and GT3a Y93H), five GT1a samples, and two GT3a non-positive samples. The same methods were applied to the HCV GT1a H77-plasmid in a dilution series, in order to determine the error rates of replication, which in turn was used to determine the limit of detection (LOD), as defined by mean + 3SD, of minority variants down to 0.24%. We found important baseline NS5A RAVs at levels between 0.24 and 0.5%, which could potentially have clinical relevance. This new method with low level detection of baseline RAVs could be useful in predicting the most cost-efficient combination of DAA treatment, and reduce the treatment duration for an HCV infected individual. (C) 2015 Elsevier B.V. All rights reserved.

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