Validation of a new software for detection of resistance associated substitutions in Hepatitis C-virus

• Main Author: Vigetun Haughey, Caitlin
• Format: Others
• Language: English
• Published: Uppsala universitet, Institutionen för biologisk grundutbildning 2019
• Subjects: Hepatitis C virus; HCV; Resistance; NS5A; Resistance associated substitutions; RAS; PacBio sequencing; Bioinformatics; Hepatit C virus; Resistens; Sekvensering; PacBio; Bioinformatik; Bioinformatics (Computational Biology); Bioinformatik (beräkningsbiologi)
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-387407

Hepatitis C infection is a global disease that causes an estimated 399,000 deaths per year. Treatment has improved dramatically in recent years through the development of direct acting antivirals that target specific regions of the Hepatitis C virus (HCV). Unfortunately the virus can have a preexisting resistance or become resistant to these drugs by mutations in the genes that code for the target proteins. These mutations are called resistance-associated substitutions (RASs). Since RASs can cause treatment failure for patients, resistance detection is performed in clinical practice to select the ideal regimen. Currently RASs are detected by using Sanger sequencing and a partly manual workflow that can discriminate the presence of a RAS if it is present in 15-20% of viruses in a patients blood. A new method with the capacity to detect lower ratios of RASs in HCV sequences was developed, which utilizes Pacific Biosciences’ (PacBio’s) sequencing and a bioinformatics analysis software called CLAMP. To validate this new approach, 123 HCV patient samples were sequenced with both methods and then analyzed. The RASs detected with the new method were congruent to what was found with the Sanger-based workflow. The new approach was also shown to correctly genotype the virus samples, identify any co-existing mutations on the same sequences, and detect if there were any mixed genotype infections in the samples. The new procedure was found to be a valid replacement for the Sanger based workflow, with the possibility to perform additional analyses and perform automated and time efficient RAS detection.