Abstract

Human immunodeficiency virus type-1 (HIV-1) exhibits high between-host genetic diversity and within-host heterogeneity, recognized as quasispecies. Because HIV-1 quasispecies fluctuate in terms of multiple factors, such as antiretroviral exposure and host immunity, analyzing the HIV-1 genome is critical for selecting effective antiretroviral therapy and understanding within-host viral coevolution mechanisms. Here, to obtain HIV-1 genome sequence information that includes minority variants, we sought to develop a method for evaluating quasispecies throughout the HIV-1 near-full-length genome using the Illumina MiSeq benchtop deep sequencer. To ensure the reliability of minority mutation detection, we applied an analysis method of sequence read mapping onto a consensus sequence derived from de novo assembly followed by iterative mapping and subsequent unique error correction. Deep sequencing analyses of aHIV-1 clone showed that the analysis method reduced erroneous base prevalence below 1% in each sequence position and discarded only < 1% of all collected nucleotides, maximizing the usage of the collected genome sequences. Further, we designed primer sets to amplify the HIV-1 near-full-length genome from clinical plasma samples. Deep sequencing of 92 samples in combination with the primer sets and our analysis method provided sufficient coverage to identify >1%-frequency sequences throughout the genome. When we evaluated sequences of pol genes from 18 treatment-naïve patients' samples, the deep sequencing results were in agreement with Sanger sequencing and identified numerous additional minority mutations. The results suggest that our deep sequencing method would be suitable for identifying within-host viral population dynamics throughout the genome.

Highlights

  • Knowledge of the genome sequence of human immunodeficiency virus type-1 (HIV-1) is fundamental for improving the clinical outcome of patients infected with Human immunodeficiency virus type-1 (HIV-1) and for understanding viral co-evolution within hosts

  • These results suggest that iterative mapping and de novo assembly followed by iterative mapping can estimate the true consensus sequence and are most appropriate for sequence read mapping

  • We mapped sequence reads from deep sequencing using consensus sequence estimation by de novo assembly followed by iterative mapping (Yang et al, 2012; Malboeuf et al, 2013; Gibson et al, 2014; McElroy et al, 2014; Verbist et al, 2015)

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Summary

Introduction

Knowledge of the genome sequence of human immunodeficiency virus type-1 (HIV-1) is fundamental for improving the clinical outcome of patients infected with HIV-1 and for understanding viral co-evolution within hosts. Genetic diversity between the subtypes ranges from 25 to 35% (Korber et al, 2001), which is extremely high compared to the human population, in which

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