Abstract
The DC Cohort is an ongoing longitudinal observational study of persons living with HIV. To better understand HIV-1 drug resistance and potential transmission clusters among these participants, we performed targeted, paired-end next-generation sequencing (NGS) of protease, reverse transcriptase and integrase amplicons. We elected to use free, publicly-available software (HyDRA Web, Stanford HIVdb and HIV-TRACE) for data analyses so that laboratory personnel without extensive bioinformatics expertise could use it; making the approach accessible and affordable for labs worldwide. With more laboratories transitioning away from Sanger-based chemistries to NGS platforms, lower frequency drug resistance mutations (DRMs) can be detected, yet their clinical relevance is uncertain. We looked at the impact choice in cutoff percentage had on number of DRMs detected and found an inverse correlation between the two. Longitudinal studies will be needed to determine whether low frequency DRMs are an early indicator of emerging resistance. We successfully validated this pipeline against a commercial pipeline, and another free, publicly-available pipeline. RT DRM results from HyDRA Web were compared to both SmartGene and PASeq Web; using the Mantel test, R2 values were 0.9332 (p<0.0001) and 0.9097 (p<0.0001), respectively. PR and IN DRM results from HyDRA Web were then compared with PASeq Web only; using the Mantel test, R2 values were 0.9993 (p<0.0001) and 0.9765 (p<0.0001), respectively. Drug resistance was highest for the NRTI drug class and lowest for the PI drug class in this cohort. RT DRM interpretation reports from this pipeline were also highly correlative compared to SmartGene pipeline; using the Spearman’s Correlation, rs value was 0.97757 (p<0.0001). HIV-TRACE was used to identify potential transmission clusters to better understand potential linkages among an urban cohort of persons living with HIV; more individuals were male, of black race, with an HIV risk factor of either MSM or High-risk Heterosexual. Common DRMs existed among individuals within a cluster. In summary, we validated a comprehensive, easy-to-use and affordable NGS approach for tracking HIV-1 drug resistance and identifying potential transmission clusters within the community.
Highlights
More laboratories are considering transitioning away from Sanger-based chemistries to Generation Sequencing (NGS) platforms for generating data on HIV-1 drug resistant mutations (DRMs)
With the World Health Organization (WHO) recommendation to use dolutegravir as an alternative first-line HIV regimen [7], it will be important for sequencing laboratories everywhere to build capacity to screen for DRMs impacting all four drug classes including Reverse Transcriptase Inhibitors (NRTIs and Nucleoside Reverse Transcriptase Inhibitors (NNRTIs)), Protease Inhibitors (PIs) and Integrase Strand Transfer Inhibitors (INSTIs) [8]
Describing the impact choice in cutoff percentage had on the number of DRMs detected for HIV-1 PR, reverse transcriptase (RT) and IN targets
Summary
More laboratories are considering transitioning away from Sanger-based chemistries to Generation Sequencing (NGS) platforms for generating data on HIV-1 drug resistant mutations (DRMs) This switch would result in detecting minor populations of drug resistant variants well below the 15–20% frequency threshold achievable with Sanger sequencing [1]. We chose HyDRA Web to detect DRMs [9], the Stanford HIVdb program to generate DRM interpretation reports [10], and HIV-TRACE to assess transmission clusters [11,12,13] We successfully validated this approach by comparing data generated using this pipeline to both a commercial pipeline (SmartGene IDNS 5 for HIV-1 Deep-Sequencing) [14] and another free, publicly-available Web-based pipeline (PASeq) [15]
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