Human immunodeficiency virus type 1 (HIV-1) encodes a transactivator of transcription (Tat) protein, which has several functions that promote viral replication, pathogenesis, and disease. Amino acid variation within Tat has been observed to alter the functional properties of Tat and, depending on the HIV-1 subtype, may produce Tat phenotypes differing from viruses representative of each subtype and commonly used in in vivo and in vitro experimentation. The molecular properties of Tat allow for distinctive functional activities to be determined such as the subcellular localization and other intra-and extracellular functional aspects of this important viral protein influenced by variation within the Tat sequence. Once Tat has been transported into the nucleus and becomes engaged in transactivation of the long terminal repeat (LTR), various Tat variants may differ in their capacity to activate viral transcription. Post-translational modification patterns based on these amino acid variations may alter interactions between Tat and host factors, which may positively or negatively affect this process. Additionally, the ability of HIV-1 to utilize or not utilize the transactivation response (TAR) element within the LTR, based on genetic variation and cellular phenotype, adds a layer of complexity to the processes that govern Tatmediated proviral DNA-driven transcription and replication. In contrast, cytoplasmic or extracellular localization of Tat may cause pathogenic effects in the form of altered cell activation, apoptosis, or neurotoxicity. Tat variants have been shown to differentially induce these processes, which may have implications for long-term HIV-1-infected patient care in the antiretroviral therapy era. Future studies concerning genetic variation of Tat with respect to function should focus on variants derived from HIV-1-infected individuals to efficiently guide Tat-targeted therapies and elucidate mechanisms of pathogenesis within the global patient population.
Accounting for genetic variation is an essential consideration during human immunodeficiency virus type 1 (HIV-1) investigation. Nanopore sequencing preserves proviral integrity by passing long genomic fragments through ionic channels, allowing reads that span the entire genome of different viral quasispecies (vQS). However, this sequencing method has suffered from high error rates, limiting its utility. This was the inspiration behind HIV-Quasipore: an HIV-1-specific Nanopore basecaller suite designed to overcome these error rates through training with gold-standard data. It comprises three deep learning-based R9.4.1 basecallers: fast, high accuracy (HAC), super accuracy (SUP), and two R10.3 deep learning-based basecallers: HAC and SUP. This was accomplished by sequencing the HIV-1 J-Lat 10.6 cell line using Nanopore and high-quality Sanger techniques. Training significantly reduced basecaller error rates across all models (Student’s one-sided t-test; p = 0.0) where median error rates were 0.0189, 0.0018, 0.0008, for R9.4.1 HIV-Quasipore-fast, HAC, SUP, and 0.0007, 0.0011 for R10.3 HIV-Quasipore-HAC, and SUP, respectively. This improved quality reduces the resolution needed to accurately detect a vQS from 22.4 to 2.6% of total positional coverage for R9.4.1 HIV-Quasipore-fast, 6.9 to 0.5% for R9.4.1 HIV-Quasipore-HAC, 4.5 to 0.3% for R9.4.1 HIV-Quasipore-SUP, 8.0 to 0.3% for R10.3 HIV-Quasipore-HAC, and 5.4 to 0.3% for R10.3 HIV-Quasipore-SUP. This was consistently observed across the entire J-Lat 10.6 genome and maintained across longer reads. Reads with greater than 8,000 nucleotides display a median nucleotide identity of 0.9819, 0.9982, and 0.9991, for R9.4.1 HIV-Quasipore-fast, HAC, SUP, and 0.9993, 0.9988 for R10.3 HIV-Quasipore-HAC, and SUP, respectively. To evaluate the robustness of this tool against unseen data, HIV-Quasipore and their corresponding pretrained basecallers were used to sequence the J-Lat 9.2 cell line and a clinical isolate acquired from the Drexel Medicine CARES cohort. When sample reads were compared against their corresponding consensus sequence, all HIV-Quasipore basecallers displayed higher median alignment accuracies than their pretrained counterparts for both the J-Lat 9.2 cell line and clinical isolate. Using Nanopore sequencing can allow investigators to explore topics, such as vQS profile detection, HIV-1 integration site analysis, whole genome amplification, gene coevolution, and CRISPR-induced indel detection, among others. HIV-Quasipore basecallers can be acquired here: https://github.com/DamLabResources/HIV-Quasipore-basecallers.
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