Sanger and Next Generation Sequencing Approaches to Evaluate HIV-1 Virus in Blood Compartments

Abstract

The implementation of antiretroviral treatment combined with the monitoring of drug resistance mutations improves the quality of life of HIV-1 positive patients. The drug resistance mutation patterns and viral genotypes are currently analyzed by DNA sequencing of the virus in the plasma of patients. However, the virus compartmentalizes, and different T cell subsets may harbor distinct viral subsets. In this study, we compared the patterns of HIV distribution in cell-free (blood plasma) and cell-associated viruses (peripheral blood mononuclear cells, PBMCs) derived from ART-treated patients by using Sanger sequencing- and Next-Generation sequencing-based HIV assay. CD4+CD45RA−RO+ memory T-cells were isolated from PBMCs using a BD FACSAria instrument. HIV pol (protease and reverse transcriptase) was RT-PCR or PCR amplified from the plasma and the T-cell subset, respectively. Sequences were obtained using Sanger sequencing and Next-Generation Sequencing (NGS). Sanger sequences were aligned and edited using RECall software (beta v3.03). The Stanford HIV database was used to evaluate drug resistance mutations. Illumina MiSeq platform and HyDRA Web were used to generate and analyze NGS data, respectively. Our results show a high correlation between Sanger sequencing and NGS results. However, some major and minor drug resistance mutations were only observed by NGS, albeit at different frequencies. Analysis of low-frequency drugs resistance mutations and virus distribution in the blood compartments may provide information to allow a more sustainable response to therapy and better disease management.

Prevalence and Factors Related to Natural Resistance-Associated Substitutions to Direct-Acting Antivirals in Patients with Genotype 1 Hepatitis C Virus Infection

“…Although Sanger automated sequencing is generally limited to the detection of variants with greater than 20% of prevalence [16], the two RASs mentioned above could not be detected by automated sequencing. This lack of correlation between both sequencing methods was probably due to the fact that both samples exhibited low viral loads (3.2 log 10 copies/mL), as it was previously reported [61]. In fact, it has been estimated that the efficiency of RNA extraction and cDNA synthesis is 1% to 10% [62]; thus, in low viral load samples, the small number of molecules present in the cDNA could be better examined by the higher analytical sensitivity of NGS [63].…”

Validation of publicly-available software used in analyzing NGS data for HIV-1 drug resistance mutations and transmission networks in a Washington, DC, Cohort

“…Arias et al also found strong agreement with MiSeq-generated HIV-1 NGS data analyzed by HyDRA Web software using a 20% frequency cutoff to that using Sanger-based sequencing; additional DRMs were seen from the NGS data when using a 5% frequency cutoff [19]. In addition, Tzou et al reported a strong correlation between data generated using Sanger sequencing and that from an in vitro diagnostic NGS assay [20].…”

Ancient DNA From Museum Specimens and Next Generation Sequencing Help Resolve the Controversial Evolutionary History of the Critically Endangered Puebla Deer Mouse

“…Both Sanger and NGS are accurate, though some studies have disagreed on which is more accurate for the detection of low frequency mutations (e.g., Ihle et al, 2014;Arsenic et al, 2015;Beck et al, 2016). Most of the studies comparing sequences derived from the two methods show a high correlation between the two methodologies (Gunnarsdottir et al, 2011;Arsenic et al, 2015;Arias et al, 2018). Critically, all of these studies have been focused on clinical research using fresh samples (e.g., blood, organ tissues, etc.)…”