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Comparative Study
. 2020 Jun 27;12(7):694.
doi: 10.3390/v12070694.

Multi-Laboratory Comparison of Next-Generation to Sanger-Based Sequencing for HIV-1 Drug Resistance Genotyping

Neil T Parkin  1 , Santiago Avila-Rios  2 , David F Bibby  3 , Chanson J Brumme  4   5 , Susan H Eshleman  6 , P Richard Harrigan  7 , Mark Howison  8 , Gillian Hunt  9 , Hezhao Ji  10 , Rami Kantor  11 , Johanna Ledwaba  9 , Emma R Lee  10 , Margarita Matías-Florentino  2 , Jean L Mbisa  3 , Marc Noguera-Julian  12 , Roger Paredes  12 , Vanessa Rivera-Amill  13 , Ronald Swanstrom  14 , Daniel J Zaccaro  15 , Yinfeng Zhang  6 , Shuntai Zhou  14 , Cheryl Jennings  16
Affiliations
Comparative Study

Multi-Laboratory Comparison of Next-Generation to Sanger-Based Sequencing for HIV-1 Drug Resistance Genotyping

Neil T Parkin et al. Viruses. .

Abstract

Next-generation sequencing (NGS) is increasingly used for HIV-1 drug resistance genotyping. NGS methods have the potential for a more sensitive detection of low-abundance variants (LAV) compared to standard Sanger sequencing (SS) methods. A standardized threshold for reporting LAV that generates data comparable to those derived from SS is needed to allow for the comparability of data from laboratories using NGS and SS. Ten HIV-1 specimens were tested in ten laboratories using Illumina MiSeq-based methods. The consensus sequences for each specimen using LAV thresholds of 5%, 10%, 15%, and 20% were compared to each other and to the consensus of the SS sequences (protease 4-99; reverse transcriptase 38-247). The concordance among laboratories' sequences at different thresholds was evaluated by pairwise sequence comparisons. NGS sequences generated using the 20% threshold were the most similar to the SS consensus (average 99.6% identity, range 96.1-100%), compared to 15% (99.4%, 88.5-100%), 10% (99.2%, 87.4-100%), or 5% (98.5%, 86.4-100%). The average sequence identity between laboratories using thresholds of 20%, 15%, 10%, and 5% was 99.1%, 98.7%, 98.3%, and 97.3%, respectively. Using the 20% threshold, we observed an excellent agreement between NGS and SS, but significant differences at lower thresholds. Understanding how variation in NGS methods influences sequence quality is essential for NGS-based HIV-1 drug resistance genotyping.

Keywords: HIV-1; NGS; drug resistance; genotyping.

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Conflict of interest statement

N.P. is a consultant to the WHO HIVDR surveillance team and has performed contract work for Abbott Molecular, Aldatu Biosciences, Gilead Sciences, Roche Molecular Systems, Stanford University, and ThermoFisher Scientific. C.J.B. has received honoraria from Gilead Canada paid to his institution. The University of North Carolina is pursuing IP protection for Primer ID, and R.S. is listed as a co-inventor and has received nominal royalties.

Figures

Figure 1
Figure 1
Plots of next-generation sequencing (NGS)-derived PR-RT nucleotide sequence identity vs. VQA Sanger consensus at various thresholds. Each line represents one specimen from panel 24 (24.1 through 24.5) or 26 (26.1 through 26.5).
Figure 2
Figure 2
Nucleotide sequence alignment for six laboratories. The VQA Sanger sequencing (SS) consensus is shown at the top. Mixtures of A and G (R) or C and T (Y) that were reported by some but not all laboratories are highlighted in blue. The sequences from laboratories 5, 7, and 8 did not contain any mixtures in this region, and those from laboratory 4 contained the Y in codon 221 at all thresholds reported (5%, 10%, and 15%).
Figure 3
Figure 3
Protease/reverse transcriptase nucleotide sequence concordance between laboratories. The mean percent identity with standard deviation is shown for each specimen and threshold.
Figure 4
Figure 4
Sequence quality assurance anomalies (total for all laboratories) at different low-abundance variant (LAV) thresholds. Sequence quality evaluation was performed with Stanford HIVdb (https://hivdb.stanford.edu/). HIVdb sequence analysis was performed using NGS sequences generated using the 5%, 10%, 15%, or 20% threshold levels.
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