Related Experiment Video
Updated: Jan 30, 2026

Modeling Hepatitis B Virus Infection in Non-Hepatic 293T-NE-3NRs Cells
Published on: June 5, 2020
A systematic, deep sequencing-based methodology for identification of mixed-genotype hepatitis C virus infections
Andrea D Olmstead1, Vincent Montoya1, Celia K Chui1
1BC Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada.
Abstract:
Hepatitis C virus (HCV) mixed genotype infections can affect treatment outcomes and may have implications for vaccine design and disease progression. Previous studies demonstrate 0-39% of high-risk, HCV-infected individuals harbor mixed genotypes however standardized, sensitive methods of detection are lacking. This study compared PCR amplicon, random primer (RP), and probe enrichment (PE)-based deep sequencing methods coupled with a custom sequence analysis pipeline to detect multiple HCV genotypes. Mixed infection cutoff values, based on HCV read depth and coverage, were identified using receiver operating characteristic curve analysis. The methodology was validated using artificially mixed genotype samples and then applied to two clinical trials of HCV treatment in high-risk individuals (ACTIVATE, 114 samples from 90 individuals; DARE-C II, 26 samples from 18 individuals) and a cohort of HIV/HCV co-infected individuals (Canadian Coinfection Cohort (CCC), 3 samples from 2 individuals with suspected mixed genotype infections). Amplification bias of genotype (G)1b, G2, G3 and G5 was observed in artificially mixed samples using the PCR method while no genotype bias was observed using RP and PE. RP and PE sequencing of 140 ACTIVATE and DARE-C II samples identified the following primary genotypes: 15% (n = 21) G1a, 76% (n = 106) G3, and 9% (n = 13) G2. Sequencing of ACTIVATE and DARE-C II demonstrated, on average, 2% and 1% of HCV reads mapping to a second genotype using RP and PE, respectively, however none passed the mixed infection cutoff criteria and phylogenetics confirmed no mixed infections. From CCC, one mixed infection was confirmed while the other was determined to be a recombinant genotype. This study underlines the risk for false identification of mixed HCV infections and stresses the need for standardized methods to improve prevalence estimates and to understand the impact of mixed infections for management and elimination of HCV.
Related Concept Videos
Systematic Error: Methodological and Sampling Errors
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
What are Viruses?
Random and Systematic Errors
Systematic Sampling Method
Systematic sampling is one of the simplest methods...
Propagation of Uncertainty from Systematic Error
Cis-regulatory Sequences

