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A systematic, deep sequencing-based methodology for identification of mixed-genotype hepatitis C virus infections
- Source :
- Infection, Genetics and Evolution. 69:76-84
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
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.
- Subjects :
- 0301 basic medicine
Microbiology (medical)
Genes, Viral
Genotype
Sequence analysis
Hepatitis C virus
030106 microbiology
Genome, Viral
Hepacivirus
Biology
medicine.disease_cause
Microbiology
Deep sequencing
03 medical and health sciences
Genetics
medicine
Humans
Molecular Biology
Phylogeny
Ecology, Evolution, Behavior and Systematics
Receiver operating characteristic
Coinfection
Computational Biology
High-Throughput Nucleotide Sequencing
Genomics
Amplicon
medicine.disease
Hepatitis C
Virology
030104 developmental biology
Infectious Diseases
ROC Curve
Cohort
RNA, Viral
Subjects
Details
- ISSN :
- 15671348
- Volume :
- 69
- Database :
- OpenAIRE
- Journal :
- Infection, Genetics and Evolution
- Accession number :
- edsair.doi.dedup.....3a319cbf5c5d624a4113d05c7736c258
- Full Text :
- https://doi.org/10.1016/j.meegid.2019.01.016