Long-Range HIV Genotyping Using Viral RNA and Proviral DNA for Analysis of HIV Drug Resistance and HIV Clustering

Abstract

The goal of the study was to improve the methodology of HIV genotyping for analysis of HIV drug resistance and HIV clustering. Using the protocol of Gall et al. (A. Gall, B. Ferns, C. Morris, S. Watson, M. Cotten, M. Robinson, N. Berry, D. Pillay, and P. Kellam, J Clin Microbiol 50:3838-3844, 2012, doi:10.1128/JCM.01516-12), we developed a robust methodology for amplification of two large fragments of viral genome covering about 80% of the unique HIV-1 genome sequence. Importantly, this method can be applied to both viral RNA and proviral DNA amplification templates, allowing genotyping in HIV-infected subjects with suppressed viral loads (e.g., subjects on antiretroviral therapy [ART]). The two amplicons cover critical regions across the HIV-1 genome (including pol and env), allowing analysis of mutations associated with resistance to protease inhibitors, reverse transcriptase inhibitors (nucleoside reverse transcriptase inhibitors [NRTIs] and nonnucleoside reverse transcriptase inhibitors [NNRTIs]), integrase strand transfer inhibitors, and virus entry inhibitors. The two amplicons generated span 7,124 bp, providing substantial sequence length and numbers of informative sites for comprehensive phylogenic analysis and greater refinement of viral linkage analyses in HIV prevention studies. The long-range HIV genotyping from proviral DNA was successful in about 90% of 212 targeted blood specimens collected in a cohort where the majority of patients had suppressed viral loads, including 65% of patients with undetectable levels of HIV-1 RNA loads. The generated amplicons could be sequenced by different methods, such as population Sanger sequencing, single-genome sequencing, or next-generation ultradeep sequencing. The developed method is cost-effective-the cost of the long-range HIV genotyping is under $140 per subject (by Sanger sequencing)-and has the potential to enable the scale up of public health HIV prevention interventions.

FIG 1

Overview of long-range HIV genotyping. The 1st- and 2nd-round products are mapped against the HIV-1 genome structure. The 1st-round (RT) PCR product is shown at the bottom as a hatched bar. The 2nd-round PCR products, amplicon 1 and amplicon 2, are shown as gray bars.

FIG 2

Distribution of the HIV-1 RNA load in BCPP samples (n = 202) that were subjects of long-range HIV genotyping using proviral DNA as a template for amplification. The histogram depicts the distribution of HIV-1 RNA in all samples (n = 202). The x axis shows HIV-1 RNA on a log₁₀ scale. The two pie charts illustrate the distributions of HIV-1 RNA among successfully amplified (n = 181) and failed (n = 21) samples. The legend on the right outlines the breakdown intervals of HIV-1 RNA presented in the pie charts.

FIG 3

Distributions of APOBEC-induced hypermutations in sequences amplified from proviral DNA (histograms). The horizontal box plots outline the distributions of APOBEC-induced hypermutations in subsets of sequences with identified drug resistance mutations. The box plots are drawn to the x-axis scale. The left and right box boundaries indicate lower and upper quartiles, the line within the box is the median, and the left and right whiskers indicate minimum and maximum values without outliers. (A and B) Amplicon 1 (n = 649), distribution of hypermutations adjusted by sequence length (A) and distribution of hypermutation ratio data (B) (see Materials and Methods). (C and D) Amplicon 2 (n = 90), distribution of hypermutations adjusted by sequence length (C) and distribution of hypermutation ratio data (D) (see Materials and Methods).

FIG 4

G-to-A hypermutations in HIV-1C sequences with and without the M184I mutation. Bean plots (a combination of a box plot, a density plot, and a rug with ticks for each value in the middle) are shown (96). Comparison between groups was performed by a Wilcoxon signed-rank test. (A) Hypermutations adjusted by sequence length. (B) Hypermutation ratios. Summary statistics are presented at the bottom.

FIG 5

Clustering of HIV-1C sequences by locus (n = 547). The proportions of HIV-1C sequences in clusters were estimated by bootstrapped ML inference. The extent of HIV clustering was analyzed at bootstrap thresholds for cluster definition of ≥0.70, ≥0.80, ≥0.90, and 1.0. The numbers of viral sequences found in clusters for a specified locus and at a specified bootstrap support were compared between loci. Four loci were used: amplicon 1 concatenated with the V1C5 region of gp120 (Amp 1 + V1c5), amplicon 1 alone (Amp 1), the ViroSeq sequence (ViroSeq), and the V1C5 region of gp120 (V1C5). The pie charts show concordant (++ and −−) and discordant (+− and −+) clustering between specified sequence loci (the first sign corresponds to the first sequence locus listed). Cases of significantly different clustering between loci with P values of less than 1.0E−04 in McNemar's test are highlighted with gray backgrounds.