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Open Access

Peer-reviewed

Research Article

Novel assays to investigate the mechanisms of latent infection with HIV-2

  • Michael D. Lu ,

    Contributed equally to this work with: Michael D. Lu, Sushama Telwatte

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft

    Affiliation Department of Medicine, University of California, San Francisco (UCSF), San Francisco, CA, United States of America

    ⨯
  • Sushama Telwatte ,

    Contributed equally to this work with: Michael D. Lu, Sushama Telwatte

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft

    Affiliations Department of Medicine, University of California, San Francisco (UCSF), San Francisco, CA, United States of America, Department of Medicine, San Francisco VA Health Care System, San Francisco, CA, United States of America

    ⨯
  • Nitasha Kumar,

    Roles Investigation, Methodology, Supervision, Validation, Writing – review & editing

    Affiliations Department of Medicine, University of California, San Francisco (UCSF), San Francisco, CA, United States of America, Department of Medicine, San Francisco VA Health Care System, San Francisco, CA, United States of America

    ⨯
  • Fernanda Ferreira,

    Roles Investigation, Resources, Writing – review & editing

    Affiliation Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America

    ⨯
  • Holly Anne Martin,

    Roles Investigation, Writing – original draft

    Affiliations Department of Medicine, University of California, San Francisco (UCSF), San Francisco, CA, United States of America, Department of Medicine, San Francisco VA Health Care System, San Francisco, CA, United States of America

    ⨯
  • Gayatri Nikhila Kadiyala,

    Roles Investigation, Writing – review & editing

    Affiliations Department of Medicine, University of California, San Francisco (UCSF), San Francisco, CA, United States of America, Department of Medicine, San Francisco VA Health Care System, San Francisco, CA, United States of America

    ⨯
  • Adam Wedrychowski,

    Roles Investigation, Writing – review & editing

    Affiliations Department of Medicine, University of California, San Francisco (UCSF), San Francisco, CA, United States of America, Department of Medicine, San Francisco VA Health Care System, San Francisco, CA, United States of America

    ⨯
  • Sara Moron-Lopez,

    Roles Investigation, Writing – review & editing

    Affiliations Department of Medicine, University of California, San Francisco (UCSF), San Francisco, CA, United States of America, Department of Medicine, San Francisco VA Health Care System, San Francisco, CA, United States of America

    ⨯
  • Tsui-Hua Chen,

    Roles Investigation, Writing – review & editing

    Affiliation Department of Medicine, San Francisco VA Health Care System, San Francisco, CA, United States of America

    ⨯
  • Erin A. Goecker,

    Roles Investigation, Writing – review & editing

    Affiliation Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, United States of America

    ⨯
  • Robert W. Coombs,

    Roles Resources, Writing – review & editing

    Affiliation Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, United States of America

    ⨯
  • Chuanyi M. Lu,

    Roles Resources, Writing – review & editing

    Affiliations Department of Medicine, University of California, San Francisco (UCSF), San Francisco, CA, United States of America, Department of Medicine, San Francisco VA Health Care System, San Francisco, CA, United States of America

    ⨯
  • Joseph K. Wong,

    Roles Resources, Writing – review & editing

    Affiliations Department of Medicine, University of California, San Francisco (UCSF), San Francisco, CA, United States of America, Department of Medicine, San Francisco VA Health Care System, San Francisco, CA, United States of America

    ⨯
  • Athe Tsibris,

    Roles Methodology, Resources, Supervision, Writing – review & editing

    Affiliation Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America

    ⨯
  • Steven A. Yukl

    Roles Conceptualization, Funding acquisition, Methodology, Resources, Supervision, Writing – review & editing

    Steven.Yukl@ucsf.edu

    Affiliations Department of Medicine, University of California, San Francisco (UCSF), San Francisco, CA, United States of America, Department of Medicine, San Francisco VA Health Care System, San Francisco, CA, United States of America

    ⨯

Abstract

Although there have been great advancements in the field of HIV treatment and prevention, there is no cure. There are two types of HIV: HIV-1 and HIV-2. In addition to genetic differences between the two types of HIV, HIV-2 infection causes a slower disease progression, and the rate of new HIV-2 infections has dramatically decreased since 2003. Like HIV-1, HIV-2 is capable of establishing latent infection in CD4+ T cells, thereby allowing the virus to evade viral cytopathic effects and detection by the immune system. The mechanisms underlying HIV latency are not fully understood, rendering this a significant barrier to development of a cure. Using RT-ddPCR, we previously demonstrated that latent infection with HIV-1 may be due to blocks to HIV transcriptional elongation, distal transcription/polyadenylation, and multiple splicing. In this study, we describe the development of seven highly-specific RT-ddPCR assays for HIV-2 that can be applied to the study of HIV-2 infections and latency. We designed and validated seven assays targeting different HIV-2 RNA regions along the genome that can be used to measure the degree of progression through different blocks to HIV-2 transcription and splicing. Given that HIV-2 is vastly understudied relative to HIV-1 and that it can be considered a model of a less virulent infection, application of these assays to studies of HIV-2 latency may inform new therapies for HIV-2, HIV-1, and other retroviruses.

Citation: Lu MD, Telwatte S, Kumar N, Ferreira F, Martin HA, Kadiyala GN, et al. (2022) Novel assays to investigate the mechanisms of latent infection with HIV-2. PLoS ONE 17(4): e0267402. https://doi.org/10.1371/journal.pone.0267402

Editor: Fatah Kashanchi, George Mason University, UNITED STATES

Received: November 9, 2021; Accepted: March 14, 2022; Published: April 27, 2022

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Data Availability: All relevant data are contained within the paper and its Supporting Information files.

Funding: This work was supported by the National Institute of Allergy and Infectious Diseases at the National Institutes of Health (1R01AI132128 [SY, JW], UW CFAR P30-AI-027757 [RWC]); the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) at the National Institutes of Health (1R01DK108349 [SY], 1R01DK120387 [SY]); and the U.S. Department of Veterans Affairs (IK2 CX000520 [SY], I01 BX000192 [JW]). The funders had no role in study design, data collection and analyses, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

HIV-2 is endemic to West Africa [1, 2]. While the incidence of HIV-2 infection is dramatically lower than that of HIV-1, there are up to 2 million people worldwide living with HIV-2 [3, 4]. It is important to distinguish between HIV-1 and HIV-2 infections because the disease progression and management differ. HIV-2 is characterized by a longer asymptomatic stage [5], a slower decline of CD4+ T cells, lower mortality related to AIDS (Acquired Immunodeficiency Syndrome), and lower person-to-person transmission rates than HIV-1 [6–8]. Individuals living with HIV-2 generally have a slower progression to AIDS, which is mediated by faster recognition and activation of the immune system in response to HIV-2 infection [9]. Globally, HIV-1 has had a steady number of new infections since 1988, while the number of new HIV-2 infections has declined in the same period, leading to growth and spread of HIV-1 but depletion of HIV-2 [9]. This decrease has been linked to lower HIV-2 transmissibility, lower viral fitness, lower capacity of HIV-2 Vif to counteract antiviral APOBEC3G/F editing [10] and more HIV awareness [11]. Effective treatment options are also available for HIV-2, but HIV-2 exhibits intrinsic resistance to non-nucleoside reverse transcriptase inhibitors (NNRTIs) and the fusion inhibitor T-20 (enfuvirtide) [12]. In addition, reduced susceptibility to NRTIs, protease inhibitors, and entry inhibitors has been reported in some HIV-2 isolates [13–17]. Despite these differing features, end-stage disease (AIDS) from both HIV-1 and HIV-2 follows a similar clinical course [18, 19].

Although the reasons why HIV-2 causes slower disease progression are not fully understood, a few contributing factors have been identified in the limited previous research. First, HIV-2 uses a wider variety of receptors, including C-C chemokine receptor type 1 (CCR1), CCR2, CCR3, C-X-C motif Chemokine Receptor 6 (CXCR6), and G-protein coupled receptor 15 (GPR15, or Brother of Bonzo [BOB]). Broader receptor usage allows infection of a wider range of T-cells and antigen-presenting cells [20, 21], which can lead to earlier detection of the virus as well as faster and more robust immune responses. HIV-2 capsids also bind more strongly than HIV-1 capsids to the non-POU domain-containing octamer-binding protein (NONO), leading to DNA sensing through the cGAS pathway and stimulation of a strong interferon-gamma (IFN-y) response [22, 23]. HIV-2 also has an additional accessory protein, VPX, which targets and degrades SAMHD1, a protein responsible for blocking the replication of HIV-1 in dendritic cells. VPX allows HIV-2 to replicate in dendritic cells, providing another pathway for earlier immune system detection [24, 25]. Lastly, contrary to HIV-1 infection, CD4+ and CD8+ T-cells maintain their poly-functionality during HIV-2 infection [26, 27]. Collectively, these mechanisms lead to an earlier detection of HIV-2 infection, allowing for a more robust immune response, better control of viral replication, and less CD4+ T-cell depletion.

HIV-2 infection is characterized by lower viremia than HIV-1, but studies are conflicting as to the burden of proviral DNA. Some studies report that HIV DNA levels are similar for HIV-1 and HIV-2 infection [28–31], while others demonstrate smaller reservoirs associated with HIV-2 infection [32], particularly in individuals infected with HIV-2 group B [33]. Studies in untreated individuals with HIV-2 suggest that despite generally lower plasma viral loads, levels of cell-associated gag mRNA are similar to those observed in HIV-1 infection, indicating significant HIV-2 transcription [34]. In contrast, mutations in the HIV-2 LTR [33] and lower levels of HIV-2 tat mRNA relative to HIV-1 infected individuals suggest transcriptional differences between HIV-1 and HIV-2 that may contribute to the lower pathogenicity of HIV-2 [34]. Given the slower progression to AIDS and lower mortality with HIV-2 infection [5], comparison of HIV-1 and HIV-2 persistence and expression may lead to new hypotheses about treatment and eradication of both viruses, especially since HIV-2 is vastly understudied.

The major barrier to curing HIV-1 and HIV-2 is the ability of these viruses to establish a latent infection in some CD4+ T-cells and possibly other cells. These latently infected cells harbor an integrated provirus and do not constitutively produce virions, but can be induced by activation to produce infectious virions [35–38]. Intensive study of HIV-1 has implicated numerous factors associated with viral persistence, including proviral amplification through clonal expansion [39–44], accumulation of proviral defects [45–47], and the influence of integration into actively-transcribed genes [48] or non-genic chromosomal regions [49]. Blocks at various stages of HIV expression are likely important molecular mechanisms underlying drivers of latency. In HIV-1 infection, these blocks have been attributed to transcriptional interference [48, 50], proviral integration into heterochromatin [51], epigenetic modification [52–57], absence of host transcription initiation factors [48, 58] or elongation factors [59–62], nucleosome positioning [55, 61, 63, 64], insufficient Tat activity [65–71], antisense transcription [72] and post-transcriptional processes, such as insufficient Rev levels [73], nuclear export defects [74, 75], RNA interference [76–79], or blocks to splicing [80, 81]. Although less is known about HIV-2 latency or expression, one study comparing the 5’ untranslated regions (5’UTR) for HIV-1 and HIV-2 found that the intrinsic structural motifs present in the HIV-2 trans-activation of transcription (TAR) region slow translation from HIV-2 genomic RNA, resulting in lower Gag production during viral replication [82].

To investigate the degree to which blocks at different stages of HIV-1 transcription may contribute to latency in ART-treated individuals, we previously developed a panel of seven reverse transcription droplet digital PCR (RT-ddPCR) assays targeting different regions of HIV-1 RNA along the genome [83]. Droplet digital PCR was selected for its ability to provide “absolute” quantification, its lower susceptibility to the effects of sequence mismatches or inhibitors, and the fact that it is more precise than quantitative PCR (qPCR) at low copy numbers [84, 85]. Using these RT-ddPCR assays, we determined that blocks to HIV-1 transcriptional elongation, completion, and splicing are the major reversible mechanisms that inhibit HIV-1 expression in blood cells from ART-treated individuals, while HIV-infected cells in the gut and cervix are also characterized by a stronger block to HIV transcriptional initiation.

Reasoning that the mechanisms of latency may or may not be the same for HIV-2, we employed a similar strategy to design primer/probe sets targeting different HIV-2 RNA regions that indicate transcriptional interference (read through transcripts) or progression through stages of HIV-2 transcriptional initiation, elongation, completion, and splicing. Application of these assays to clinical samples will advance our understanding of the mechanisms of HIV-2 latency and may assist in efforts towards new therapies aimed at disrupting latent infection with HIV-2, HIV-1, and other retroviruses.

Materials and methods

Primer design and selection

To measure the degree to which blocks at different stages of transcription may contribute to HIV-2 latency, we designed a panel of 7 RT-ddPCR assays to measure “read-through” (U3-U5), initiated (TAR), 5’ elongated (R-U5-pre-gag; “Long LTR”), mid-elongated and unspliced (gag), distally transcribed (nef), completed/polyadenylated (U3-polyA; “polyA”), and multiply-spliced (tat-rev) HIV-2 transcripts (Fig 1) [83]. For a given region of interest (e.g.: U3-U5 for read-through transcripts), the Los Alamos National Lab HIV Sequence Database was used to design primers and probes to target the areas that were most conserved and to match the consensus of all available sequences from individuals infected with HIV-2 group A or group B [86] (Table 1). In cases where group A had a consensus that differed sufficiently from group B and too many degenerate bases would be required to match both, primer/probe sequences were chosen to match the consensus of group A. Two primer/probe sets were designed for Long LTR, polyA, and gag RT-ddPCR assays; preliminary experiments were used to select the best performing set for subsequent testing.

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Fig 1. Schematic representation of the HIV-2 genome and targets for HIV-2 transcription profiling assays.

The schematic depicts the genetic organization of the HIV-2 genome, proviral DNA, and the HIV-2 ‘transcription profiling’ primer/probe sets that target specific RNA sequence regions: ‘read-through’, ‘TAR’, ‘Long LTR’, gag’, ‘nef’, ‘polyA’ and ‘tat-rev’. The transcriptional feature targeted is listed below the respective assay. Adapted from Telwatte et al., PLoS Pathogens 2018 and HIV sequence compendium 2010.

https://doi.org/10.1371/journal.pone.0267402.g001

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Table 1. Primer/probes for HIV-2 assays.

https://doi.org/10.1371/journal.pone.0267402.t001

Preparation of supernatant viral RNA (ROD) standard

To measure the performance of the new HIV-2 assays that detect genomic (unspliced) viral RNA, an HIV-2 genomic RNA standard was prepared from the supernatant of cells infected with ROD, an infectious HIV-2 laboratory virus derived from a clinical isolate [1, 87]. The ROD virus was passaged in peripheral blood mononuclear cells (PBMC) and harvested at day 7 post infection. Supernatant virus was clarified using two centrifugation steps (500 xg, 30 minutes). Virion-associated RNA from the supernatant was isolated and prepared as previously described [88]. Briefly, ROD supernatant (400 μL diluted in PBS) was treated with DNAse I and RNAse A to eliminate free nucleic acids and subjected to three freeze/thaw cycles to lyse any residual cells but not virions [89, 90]. RNA extraction was performed using the QIAgen Viral RNA Mini Kit as per manufacturer’s guidelines, with 100 μL elution volume per column. The number of HIV-2 copies in the extracted RNA was quantified in replicate using the Abbott m2000sp/rt platform adapted for quantification of HIV-2 RNA in plasma. This method, which was developed at the University of Washington and validated for clinical use, targets a region of the LTR conserved in group A and B HIV-2 [91–93]. The supernatant viral RNA standard was subsequently diluted in nuclease free water, reverse transcribed, and used to evaluate the performance of the new ddPCR assays for HIV-2. The “expected copies” in each ddPCR well were calculated using the value from the established Abbott qPCR assay, dilution factor, volume added to the reverse transcription (RT) reaction, and proportion of the RT added to each ddPCR well.

Preparation of synthetic, in vitro transcribed (IVT) viral RNA standard

The HIV-2 supernatant viral RNA standard would be expected to contain mostly unspliced genomic HIV-2 RNA, which should not be detected by the assays for read-through or multiply-spliced HIV-2 RNA. Therefore, we designed a 2886bp synthetic HIV-2 viral RNA standard that begins with the 5’ U3 region, splices from D1 to exon 1 of tat and between exons 1 and 2 of tat and rev, and encodes a polyA tail at the end of the 3’ R region. This standard contains the regions targeted by the read-through, TAR, Long LTR, nef, polyA, and multiply-spliced tat-rev assays (but not gag, which is found only in unspliced HIV RNA). The HIV-2 sequence was chosen to match the consensus of HIV-2 group A using the Los Alamos National Lab HIV Sequence Database and was constructed between the Nhe I and Xba I cloning sites of the plasmid pcDNA3.1(+). This plasmid was used as an HIV-2 DNA standard and also used for in vitro transcription of an HIV-2 RNA standard.

To prepare the latter, the plasmid was linearized by digestion with Xba I, and the HIV-2 sequence was in vitro transcribed using the T7 RiboMAX™ Express Large Scale RNA Production System (Promega, Madison, WI). After in vitro transcription, the RNA was purified by the QIAgen RNeasy kit with on-column DNAse. The concentration of the extracted RNA (synthetic IVT standard) was measured by ultraviolet (UV) spectrophotometry (NanoDrop ND-1000 instrument, Thermo Fisher) and the molecular weight was used to calculate the expected number of molecules per μL. The length, integrity, and concentration of the IVT standard was also confirmed using the Agilent Bioanalyzer RNA 6000 Nano assay (Agilent, Santa Clara, CA). In addition, the copies of HIV-2 RNA were independently quantified using the Abbott m2000sp/rt platform adapted for the clinical quantification of HIV-2 [92]. The IVT standard was subsequently diluted in nuclease free water and used for RT and ddPCR. The “expected copies” in each ddPCR well were calculated using the value from the Nanodrop, dilution factor, volume added to the reverse transcription (RT) reaction, and proportion of the RT added to each ddPCR well.

Reverse transcription

To mitigate bias towards reverse transcription of the 3’ end (as expected with poly-dT alone), the 5’ end (as expected with random hexamers), or any one region (as expected with specific reverse primers), reverse transcription (RT) reactions were primed using both random hexamers and poly-dT. RT reactions were performed in a 50 μL mixture containing 5 μL of 10× SuperScript III buffer (Invitrogen), 5 μL of 50 mM MgCl2, 2.5 μL of random hexamers (50 ng/μL; Invitrogen), 2.5 μL of 50 μM poly-dT15, 2.5 μL of 10 mM deoxynucleoside triphosphates (dNTPs), 1.25 μL of RNAseOUT (40 U/μL; Invitrogen), 2.5 μL of SuperScript III RT (200 U/μL; Invitrogen), and water or one of the HIV-2 standards +/- cellular RNA from uninfected donor cells. Reactions were performed in a standard thermocycler with the following cycling conditions: 25°C for 10 min, 50°C for 50 min, and an inactivation step at 85°C for 5 min. Aliquots of cDNA from the RT reaction were then added to ddPCR reactions containing primers and probe specific for a given HIV-2 RNA region.

Droplet digital PCR

Primer/probe sets were tested using the QX100 (Bio-Rad, Hercules, CA) droplet digital PCR (ddPCR) system. Each HIV-2 assay (primer/probe set) was tested in at least duplicate using aliquots from the same common RT reaction. The total reaction volume (20 μL) consisted of 10 μL of ddPCR probe supermix (no deoxyuridine triphosphate; Bio-Rad, CA, USA), 900 nM of primers (Table 1; Life Technologies, CA, USA), 250 nM of probe (Life Technologies; Table 1), and 5 μL of cDNA. After droplet formation, the cDNA was amplified using a Mastercycler® Nexus (Eppendorf, Hamburg, Germany) with the following cycling conditions: 95°C for 10 min, 45 cycles of 30 s at 95°C and 59°C for 1 min, and a final droplet cure step at 98°C for 10 min (S1 Table). Droplets were read and analyzed using the QX100 Droplet Reader and QuantaSoft software (Bio-Rad, CA, USA) in the absolute quantification mode.

Determination of HIV-2 assay sensitivity using plasmid DNA and in vitro transcribed HIV-2 RNA standards

Plasmid DNA encoding the read-through, multiply-spliced synthetic HIV-2 region was quantified by UV spectrophotometry (NanoDrop) and the molecular weight was used to calculate the number of molecules per μL. The plasmid HIV-2 DNA was added to ddPCR wells at expected inputs of 1−104 copies/well in duplicate (104, 103 and 102 copies) or quadruplicate (10 and 1 copy) prior to ddPCR.

Known concentrations of HIV-2 RNA were added to RT reactions and the resultant cDNA was added at expected inputs (per final ddPCR well) of 1−103 copies/well in duplicate (103, 102 and 101 copies) or quadruplicate (1 copy) prior to ddPCR.

Nucleic acid isolation from donor PBMC for “background” RNA

RNA and DNA were extracted from cryopreserved donor PBMC using TRIreagent according to the manufacturer’s protocol (Molecular Research Center), except that DNA was extracted using the back extraction buffer (4M guanidine thiocyanate, 50 mM sodium citrate, and 1 M Tris [free base]). RNA was resuspended in 25 μL of nuclease free water, measured on the NanoDrop instrument, and stored at -80°C until use. DNA was resuspended in 25 μL of QIAgen EB buffer (Qiagen, Hilden, Germany), incubated at room temperature overnight, and stored at -80°C until use.

Assay sensitivity in the presence of background RNA

Additional experiments were performed using the standards and isolated donor PBMC RNA to determine each assay’s performance in the presence of “background” cellular RNA. Standards were diluted to the 10,000; 1,000; 100; 10; and 1 copies/5 μL before being added to 50 μL to 70 μL RT reactions with or without cellular RNA from PBMCs. RT products were then added to 20 μL ddPCR reactions and run as described above for each assay.

Results

Efficiency, sensitivity, and linearity of HIV-2 RT-ddPCR assays determined using HIV-2 plasmid DNA standard

The efficiency, linearity, and sensitivity of the new HIV-2 primer/probe sets for all regions except gag were measured using terminal dilutions of the synthetic HIV-2 plasmid (11,330 copies down to 0.11 expected copies). Each primer/probe set was consistently able to detect fewer than 10 copies, and as few as 1 copy in quadruplicate measurements (Fig 2). Assay efficiencies (slopes of measured vs. expected copies) were greater than ≥83% and similar for all assays (Readthrough = 0.83, TAR = 0.85, Long LTR = 0.88, Nef = 0.87, PolyA = 0.89, Tat-Rev = 0.88; Fig 2). All assays showed linear quantification (R2≥0.999) with a dynamic range of ≥4 log10. The gag coding region is absent from this construct and as such, the gag primer/probe set was not evaluated using plasmid DNA.

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Fig 2. Efficiency and linearity of HIV-2 RT-ddPCR assays determined using HIV-2 plasmid DNA.

Plasmid DNA was quantified by UV spectrophotometry (NanoDrop) and diluted (expected copies) to determine the absolute number of copies detected by each primer/probe set using ddPCR reactions (measured copies). Data represent average of duplicate/quadruplicate wells from a representative experiment. S = slope, indicating assay efficiency. Each primer/probe set was tested in at least two independent experiments.

https://doi.org/10.1371/journal.pone.0267402.g002

Efficiency, sensitivity, and linearity of HIV-2 RT-ddPCR assays using synthetic IVT HIV-2 RNA standard

All primer/probe sets except those for gag were also tested using standards prepared from in vitro transcribed (IVT) RNA generated from the custom-designed HIV-2 plasmid. Expected copy numbers were calculated using the RNA concentration and molecular weight. RNA was added to RT reactions to achieve final input copy numbers of 103, 102, 101, 100 and 10−1 copies/ddPCR well. All primer/probe sets could detect as few as 10 copies, while read-through, polyA and tat-rev were also detected in wells containing 1 copy (Fig 3). Assay efficiencies (slopes of measured vs. expected copies) were high but showed some variation between target regions (read-through = 0.85, TAR = 0.95, Long LTR = 1.14, nef = 0.76, polyA = 0.43, and tat-rev = 0.57; median = 0.77). All assays showed linear quantification (R2≥0.999) with a dynamic range of ≥4 log10. These validation experiments demonstrated that our selected primer/probe sets exhibit high efficiency and linearity using RNA that underwent reverse transcription and ddPCR.

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Fig 3. Efficiency and linearity of HIV-2 RT-ddPCR assays determined using in vitro transcribed (IVT) RNA.

An RNA standard was prepared by in vitro transcription (IVT) from a HIV-2 plasmid and quantified by independent means (UV spectroscopy, the Agilent Bioanalyzer, and a clinical assay using the Abbott m2000sp/rt platform). Various inputs of the IVT RNA standard (which were used to calculate ‘Expected Copies’ per ddPCR well) were reverse transcribed. Replicate aliquots of cDNA were used to measure the absolute number of copies detected by each primer/probe set (‘Measured Copies’). Each primer/probe set was tested using expected inputs of 1−104 copies per ddPCR well. Data represent average of duplicate wells from a representative experiment. S = slope, indicating assay efficiency for n = 2 independent experiments, R2 = coefficient of determination/goodness of fit of linear regression for n = 2 independent experiments.

https://doi.org/10.1371/journal.pone.0267402.g003

Performance of HIV-2 RT-ddPCR assays using viral supernatant RNA (ROD) standard

The viral supernatant RNA (ROD) standard was used to further evaluate the performance of our new RT-ddPCR assays for HIV-2 TAR, Long LTR, gag, nef, and polyA RNA. Known inputs of the viral supernatant RNA standard (based on quantification by clinical assay) were added to a common RT reaction. The subsequent cDNA was divided among different assays, and each was measured in duplicate with an expected input of 15,714 copies/ddPCR well. Our TAR assay detected 15,800 copies, which is similar to the expected value from the clinical assay targeting the LTR region (Fig 4). The measured copies of Long LTR (7,840), gag (6,540), and nef (6,480) were approximately half that of the TAR region, consistent with the fact that every full-length genomic HIV-2 RNA contains 2 TAR regions. PolyA was detected at lower levels (868 copies), but it should be noted that the reverse primer of the polyA assay was designed to match the consensus from HIV-2 infected individuals and has multiple sequence mismatches with the ROD virus. Read-through and tat-rev transcripts were detected at even lower levels (37 and 6 copies, respectively), likely because these transcripts are not efficiently packaged into virions and most free viral RNA should be degraded by the freeze-thaw nuclease treatment used to prepare the standard.

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Fig 4. Levels of HIV-2 targets in viral supernatant RNA.

The viral supernatant HIV-2 standard was quantified by a clinical assay [92] and a known input was added to a common RT reaction. cDNA was synthesized in this common RT reaction and subsequently divided equally between ddPCR wells for all HIV-2 targets to assess the efficiency of each primer/probe set.

https://doi.org/10.1371/journal.pone.0267402.g004

Primer/probe specificity for HIV-2 vs. HIV-1 using DNA standards

Despite sharing genetic features with HIV-1, HIV-2 exhibits considerable sequence differences. Accordingly, it was hypothesized that our primer/probe sets would be highly specific for HIV-2 templates only. We assessed the specificity of each primer/probe set using inputs of either 400 copies of HIV-2 plasmid or 10,000 copies of HIV-1 (pNL4.3) plasmid DNA. At a 400-copy input of HIV-2 plasmid, all our HIV-2 primer/probe sets performed efficiently, detecting a median of 83% of the expected input (78%-88%; Fig 5A). As predicted, even at a high input of 10,000 copies of HIV-1 DNA, our HIV-2 primer/probe sets showed minimal false positives (no copies for read-through, TAR, gag, nef, or polyA, and only 1 copy for Long LTR and tat-rev; Fig 5B). In contrast, our HIV-1 primer/probes sets detected around 10,000 copies, in line with previously measured efficiencies [83].

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Fig 5. Specificity of HIV-2 primer/probes for HIV-2 DNA.

(A) HIV-2 plasmid DNA was added at a constant input of 400 copies into duplicate reactions to measure each target (read-through, TAR, Long LTR, nef, polyA, and tat-rev). (B) HIV-1 plasmid (pNL4.3) was added at a constant input of 10,000 copies into each reaction to measure detection of both HIV-2 and HIV-1 targets.

https://doi.org/10.1371/journal.pone.0267402.g005

Assay efficiency is not adversely affected by background RNA

To determine whether our primer/probe sets are inhibited by the presence of cellular RNA (“background”), we performed an RT-ddPCR experiment using a constant input of 1,000 copies of HIV-2 IVT RNA standard in the presence or absence of 1 μg RNA from peripheral blood mononuclear cells (PBMC). In line with previous observations using other primer/probe sets [94], we found that the presence of background RNA was not inhibitory (S1 Fig). Instead, the detection of all HIV-2 targets actually improved in the presence of background RNA, consistent with our previous observations with SARS-CoV-2 targeting primer/probe sets [95]. No false positives were detected in wells containing donor PBMC RNA without HIV-2 RNA.

Primer/probe specificity and false positive rates

To determine the non-specific reactivity of oligonucleotides (false positive rate) of our assays, each primer/probe set was tested using ‘no template’ controls (NTC). These reactions were performed with molecular-grade H2O, HIV-1 RNA (from pNL4-3), and RNA isolated from HIV-negative donor PBMC (S2 and S3 Tables). Identifying any HIV-1 non-specificity is important for the purposes of our assays because epidemiology and clinical management differ for HIV-1 and HIV-2, and since HIV-2 can be found in dual-infection with HIV-1, albeit at lower incidences [96, 97]. Except for Long LTR and tat-rev, both of which only had 1 false positive droplet amidst tens of thousands of analyzed droplets, no other HIV-1-related false positives were observed. For the H2O and PBMC NTCs, false positive detections were only seen in Long LTR (2 of 14 wells) and TAR (1 of 15 wells), all of which were from donor PBMCs (S2, S3 Tables). Again, these results were amidst tens and even hundreds of thousands of analyzed droplets.

Assay reproducibility

S4 Table shows the coefficient of variation (% CV) between experiments (inter-experiment) and within experiments (intra-experiment) for each assay at inputs of 100 and 1,000 copies of the HIV DNA plasmid standard or the IVT HIV RNA standard.

Discussion

We present a novel panel of primer/probe sets targeting key regions of the HIV-2 transcriptome that may provide insight into the transcriptional blocks which govern HIV-2 latency. We selected droplet digital PCR rather than conventional qRT-PCR because ddPCR allows for absolute quantification (without the need for external calibration), is more forgiving of sequence mismatches in primer/probe sequences, is likely more precise at lower copy numbers, and provides similar sensitivity and reproducibility [83, 85]. We selected 5′-6-carboxyfluorescein dye (FAM) and a 3′-Minor Groove Binder (MGB) group for our probes given MGB forms highly-stable duplexes with single-stranded DNA, permitting the use of shorter probes and yielding higher specificity [98]. Our validation studies demonstrated that all primer/probe sets could detect as few as 1–10 copies using plasmid DNA carrying the cognate sequence and demonstrated linearity over 4 orders of magnitude (R2>0.999 for all; Figs 2 and 3). Using plasmid DNA, we found that PCR efficiencies were high and very similar across all targets (range = 83–89%; Fig 2).

Using synthetic RNA, we found that read-through, TAR, Long LTR and nef primer/probe sets performed with similar efficiencies to those observed using plasmid DNA, indicating that reverse transcription (RT) does not considerably diminish the ability to detect input copies. We did observe lower efficiency in the polyA and tat-rev primer/probe sets using the IVT standard compared to HIV-2 plasmid DNA (polyA: 43% vs. 89% and Tat-Rev: 57% vs. 88%, respectively), suggesting that the RT reaction could contribute some loss in detection capability. However, in the presence of background RNA (up to 1μg PBMC RNA), which is a closer approximation of clinical samples, we found that this loss of sensitivity of polyA and tat-rev was reduced (S1 Fig).

Each of our HIV-2 assays demonstrated high level sensitivity (down to 1–10 copy inputs) over >4 orders of magnitude (see Figs 2 and 3) and inter-assay reproducibility (S4 Table), with minimal false positives using non-template controls (NTCs) (see S2 and S3 Tables). For studies involving people with HIV (PWH), it is imperative to distinguish HIV-2 from HIV-1. We found that our assays are highly-specific for HIV-2 (Fig 5). At 10,000 copies of HIV-1 (NL4-3), the HIV-2 read-through, TAR, gag, nef, and polyA assays exhibited no detection, while Long LTR and tat-rev had only 1 positive droplet across duplicate wells. With inputs of H2O or donor PBMC RNA, only Long LTR and TAR had any false positives (Long LTR: 2/14 wells, and TAR: 1/15 wells; S3 Table). Together, these data suggest that our assays are highly-specific and sensitive for HIV-2, with minimal false positives. These defined characteristics are extremely important for studying clinical samples because HIV-2 infected individuals may have low levels of HIV-2 RNA.

Our primer/probe sets performed better in the presence of additional background PBMC RNA at an input of 1 μg (S1 Fig). This observation is supported by previous studies showing that the presence of background RNA is not always inhibitory and can sometimes increase efficiency during the reverse transcription step [94]. The lack of inhibition from cellular RNA suggests that our HIV-2 assays are well suited for testing cellular samples from HIV-2 infected individuals.

At the same time, it should be remembered that a few of our primers/probes do not match the consensus sequence of HIV-2 group B. Although we tried to design primers and probes that match the consensus of both group A and B, in some cases this was not possible without having too many degenerate bases, which can increase nonspecific binding. In these situations, we chose to match the consensus of group A. However, most of the primers and probes are very well conserved for both group A and B. For example, the probes for the TAR, Long LTR, PolyA, and Read-through regions are all highly conserved for both group A and group B. For individual primers where there are mismatches with group B, these are often limited to one or a few nucleotides in the 5’ portion of the primer, where such changes may not have a significant effect on assay performance. Moreover, ddPCR is less susceptible than qPCR to the effects of sequence mismatches. Therefore, we suspect that most of our assays would actually work quite well for both group A and group B. If there are sequence mismatches that substantially inhibit a given assay, this inhibition may be apparent from the raw ddPCR plots (another advantage of ddPCR), and the individual primer or probe could always be corrected to match group B.

Another limitation of this study is that we were unable to apply these assays to clinical samples from HIV-2 infected individuals. However, based on our data using RNA and DNA standards and the use of consensus sequences to design the assays, these RT-ddPCR assays should be ideally suited for investigating the mechanisms of HIV-2 latency in clinical samples. It is also likely that the designed primer/probe sets will perform well in qRT-PCR assays, although we did not evaluate this question directly.

Assays to quantify and study HIV-2 transcription are needed to bridge the gaps in our understanding of HIV latency, particularly in the context of HIV-2. We previously reported the development of a novel HIV-2 plasma RNA viral load assay, validated for clinical use with the Abbott m2000 platform [91, 92]. Other methods to measure HIV-2 include primer sets directed to gag [99–101] or the LTR-gag junction [102, 103], RT-qPCR methods that target HIV-2 unspliced and multiply-spliced mRNA [104], and ddPCR-based quantification of episomal HIV 2-LTR circles as a potential marker for residual viral replication [105].

Given that even during untreated infection, the majority of HIV-2-infected individuals exhibit very low viral loads [8, 106] and a significant proportion of these behave as ‘elite-controllers’ [107], it has been suggested that HIV-2 could serve as a model for a functional HIV cure [108]. Therefore, quantitative assays that can assess transcriptional features of HIV-2 in infected individuals are critical to advance those efforts.

The HIV-2 “transcription profiling” assays described here could be used to investigate how blocks to HIV-2 transcription vary across different tissue sites [109] and to measure the effects of antiretroviral treatment interruption or therapies designed to disrupt latency (such as latency reversing agents) in HIV-2 infected individuals. This innovative method will help to provide critical new insights into HIV-2 transcription and latency, which could advance efforts towards new therapies aimed at "shock and kill,‴ "block and lock," and other approaches designed to disrupt latent HIV infection or achieve viral eradication or functional cure.

Supporting information

S1 Fig. HIV-2 assay sensitivity in presence of background RNA.

The IVT HIV-2 RNA standard was added to a common RT reaction with or without background RNA to achieve a final concentration of 1,000 copies/5μL (the input into each ddPCR well, or “expected copies”). cDNA synthesized in this common RT reaction was subsequently divided equally between ddPCR wells for all HIV-2 targets except gag (not present in IVT standard) to assess the efficiency of each primer/probe set.

https://doi.org/10.1371/journal.pone.0267402.s001

(TIF)

S1 Table. PCR conditions for droplet digital PCR.

https://doi.org/10.1371/journal.pone.0267402.s002

(DOCX)

S2 Table. False positive ddPCR droplets from no template controls.

https://doi.org/10.1371/journal.pone.0267402.s003

(DOCX)

S3 Table. False positive ddPCR wells from no template controls.

https://doi.org/10.1371/journal.pone.0267402.s004

(DOCX)

S4 Table. Assay reproducibility parameters.

https://doi.org/10.1371/journal.pone.0267402.s005

(DOCX)

References

  1. 1. Clavel F, Guétard D, Brun-Vézinet F, Chamaret S, Rey MA, Santos-Ferreira MO, et al. Isolation of a new human retrovirus from West African patients with AIDS. Science. 1986;233(4761):343–6. Epub 1986/07/18. pmid:2425430.
  2. 2. de Mendoza C, Cabezas T, Caballero E, Requena S, Amengual MJ, Peñaranda M, et al. HIV type 2 epidemic in Spain: challenges and missing opportunities. Aids. 2017;31(10):1353–64. Epub 2017/03/31. pmid:28358736.
  3. 3. Spach DH, Budak JZ. HIV-2 Infection hiv.uw.edu2020 [updated July 24, 2020; cited 2021 august 1]. Available from: https://www.hiv.uw.edu/go/key-populations/hiv-2/core-concept/all.
  4. 4. De Cock KM, Adjorlolo G, Ekpini E, Sibailly T, Kouadio J, Maran M, et al. Epidemiology and transmission of HIV-2. Why there is no HIV-2 pandemic. Jama. 1993;270(17):2083–6. Epub 1993/11/03. pmid:8147962.
  5. 5. Esbjörnsson J, Månsson F, Kvist A, da Silva ZJ, Andersson S, Fenyö EM, et al. Long-term follow-up of HIV-2-related AIDS and mortality in Guinea-Bissau: a prospective open cohort study. Lancet HIV. 2018. Epub 2018/11/06. pmid:30392769.
  6. 6. Drylewicz J, Matheron S, Lazaro E, Damond F, Bonnet F, Simon F, et al. Comparison of viro-immunological marker changes between HIV-1 and HIV-2-infected patients in France. Aids. 2008;22(4):457–68. Epub 2008/02/28. pmid:18301058; PubMed Central PMCID: PMC2727618.
  7. 7. Jaffar S, Wilkins A, Ngom PT, Sabally S, Corrah T, Bangali JE, et al. Rate of decline of percentage CD4+ cells is faster in HIV-1 than in HIV-2 infection. J Acquir Immune Defic Syndr Hum Retrovirol. 1997;16(5):327–32. Epub 1998/01/07. pmid:9420309.
  8. 8. Marlink R, Kanki P, Thior I, Travers K, Eisen G, Siby T, et al. Reduced rate of disease development after HIV-2 infection as compared to HIV-1. Science. 1994;265(5178):1587–90. Epub 1994/09/09. pmid:7915856.
  9. 9. Nyamweya S, Hegedus A, Jaye A, Rowland-Jones S, Flanagan KL, Macallan DC. Comparing HIV-1 and HIV-2 infection: Lessons for viral immunopathogenesis. Rev Med Virol. 2013;23(4):221–40. pmid:23444290.
  10. 10. Bertine M, Charpentier C, Visseaux B, Storto A, Collin G, Larrouy L, et al. High level of APOBEC3F/3G editing in HIV-2 DNA vif and pol sequences from antiretroviral-naive patients. Aids. 2015;29(7):779–84. Epub 2015/05/20. pmid:25985400.
  11. 11. Visseaux B, Damond F, Matheron S, Descamps D, Charpentier C. Hiv-2 molecular epidemiology. Infect Genet Evol. 2016;46:233–40. pmid:27530215.
  12. 12. Witvrouw M, Pannecouque C, Switzer WM, Folks TM, De Clercq E, Heneine W. Susceptibility of HIV-2, SIV and SHIV to various anti-HIV-1 compounds: implications for treatment and postexposure prophylaxis. Antivir Ther. 2004;9(1):57–65. Epub 2004/03/26. pmid:15040537.
  13. 13. Descamps D, Damond F, Matheron S, Collin G, Campa P, Delarue S, et al. High frequency of selection of K65R and Q151M mutations in HIV-2 infected patients receiving nucleoside reverse transcriptase inhibitors containing regimen. J Med Virol. 2004;74(2):197–201. Epub 2004/08/28. pmid:15332266.
  14. 14. Colson P, Henry M, Tivoli N, Gallais H, Gastaut JA, Moreau J, et al. Polymorphism and drug-selected mutations in the reverse transcriptase gene of HIV-2 from patients living in southeastern France. J Med Virol. 2005;75(3):381–90. Epub 2005/01/14. pmid:15648062.
  15. 15. Jallow S, Kaye S, Alabi A, Aveika A, Sarge-Njie R, Sabally S, et al. Virological and immunological response to Combivir and emergence of drug resistance mutations in a cohort of HIV-2 patients in The Gambia. Aids. 2006;20(10):1455–8. Epub 2006/06/23. pmid:16791023.
  16. 16. Menéndez-Arias L, Tözsér J. HIV-1 protease inhibitors: effects on HIV-2 replication and resistance. Trends Pharmacol Sci. 2008;29(1):42–9. Epub 2007/12/07. pmid:18054799.
  17. 17. Smith RA, Anderson DJ, Pyrak CL, Preston BD, Gottlieb GS. Antiretroviral drug resistance in HIV-2: three amino acid changes are sufficient for classwide nucleoside analogue resistance. J Infect Dis. 2009;199(9):1323–6. Epub 2009/04/11. pmid:19358668; PubMed Central PMCID: PMC3726187.
  18. 18. Martinez-Steele E, Awasana AA, Corrah T, Sabally S, van der Sande M, Jaye A, et al. Is HIV-2- induced AIDS different from HIV-1-associated AIDS? Data from a West African clinic. Aids. 2007;21(3):317–24. Epub 2007/01/27. pmid:17255738.
  19. 19. Clavel F, Mansinho K, Chamaret S, Guetard D, Favier V, Nina J, et al. Human immunodeficiency virus type 2 infection associated with AIDS in West Africa. N Engl J Med. 1987;316(19):1180–5. Epub 1987/05/07. pmid:3472076.
  20. 20. Shi Y, Brandin E, Vincic E, Jansson M, Blaxhult A, Gyllensten K, et al. Evolution of human immunodeficiency virus type 2 coreceptor usage, autologous neutralization, envelope sequence and glycosylation. J Gen Virol. 2005;86(Pt 12):3385–96. pmid:16298986.
  21. 21. Visseaux B, Charpentier C, Ozanne A, Nizard A, Drumard S, Fagard C, et al. Tropism distribution among antiretroviral-naive HIV-2-infected patients. AIDS. 2015;29(16):2209–12. pmid:26544584.
  22. 22. Jaye A, Sarge-Njie R, Schim van der Loeff M, Todd J, Alabi A, Sabally S, et al. No differences in cellular immune responses between asymptomatic HIV type 1- and type 2-infected Gambian patients. J Infect Dis. 2004;189(3):498–505. pmid:14745708.
  23. 23. Lahaye X, Satoh T, Gentili M, Cerboni S, Conrad C, Hurbain I, et al. The capsids of HIV-1 and HIV-2 determine immune detection of the viral cDNA by the innate sensor cGAS in dendritic cells. Immunity. 2013;39(6):1132–42. pmid:24269171.
  24. 24. Baldauf HM, Pan X, Erikson E, Schmidt S, Daddacha W, Burggraf M, et al. SAMHD1 restricts HIV-1 infection in resting CD4(+) T cells. Nat Med. 2012;18(11):1682–7. pmid:22972397; PubMed Central PMCID: PMC3828732.
  25. 25. Duvall MG, Lore K, Blaak H, Ambrozak DA, Adams WC, Santos K, et al. Dendritic cells are less susceptible to human immunodeficiency virus type 2 (HIV-2) infection than to HIV-1 infection. J Virol. 2007;81(24):13486–98. pmid:17913821; PubMed Central PMCID: PMC2168847.
  26. 26. Duvall MG, Jaye A, Dong T, Brenchley JM, Alabi AS, Jeffries DJ, et al. Maintenance of HIV-specific CD4+ T cell help distinguishes HIV-2 from HIV-1 infection. J Immunol. 2006;176(11):6973–81. pmid:16709858.
  27. 27. Sousa AE, Chaves AF, Loureiro A, Victorino RM. Comparison of the frequency of interleukin (IL)-2-, interferon-gamma-, and IL-4-producing T cells in 2 diseases, human immunodeficiency virus types 1 and 2, with distinct clinical outcomes. J Infect Dis. 2001;184(5):552–9. pmid:11494161.
  28. 28. Rodriguez SK, Sarr AD, MacNeil A, Thakore-Meloni S, Gueye-Ndiaye A, Traoré I, et al. Comparison of heterologous neutralizing antibody responses of human immunodeficiency virus type 1 (HIV-1)- and HIV-2-infected Senegalese patients: distinct patterns of breadth and magnitude distinguish HIV-1 and HIV-2 infections. J Virol. 2007;81(10):5331–8. Epub 2007/02/16. pmid:17301136; PubMed Central PMCID: PMC1900200.
  29. 29. Popper SJ, Sarr AD, Guèye-Ndiaye A, Mboup S, Essex ME, Kanki PJ. Low plasma human immunodeficiency virus type 2 viral load is independent of proviral load: low virus production in vivo. J Virol. 2000;74(3):1554–7. Epub 2000/01/11. pmid:10627569; PubMed Central PMCID: PMC111493.
  30. 30. Damond F, Descamps D, Farfara I, Telles JN, Puyeo S, Campa P, et al. Quantification of proviral load of human immunodeficiency virus type 2 subtypes A and B using real-time PCR. J Clin Microbiol. 2001;39(12):4264–8. Epub 2001/11/29. pmid:11724830; PubMed Central PMCID: PMC88534.
  31. 31. MacNeil A, Sarr AD, Sankalé JL, Meloni ST, Mboup S, Kanki P. Direct evidence of lower viral replication rates in vivo in human immunodeficiency virus type 2 (HIV-2) infection than in HIV-1 infection. J Virol. 2007;81(10):5325–30. Epub 2007/03/03. pmid:17329334; PubMed Central PMCID: PMC1900238.
  32. 32. Gueudin M, Damond F, Braun J, Taïeb A, Lemée V, Plantier JC, et al. Differences in proviral DNA load between HIV-1- and HIV-2-infected patients. Aids. 2008;22(2):211–5. Epub 2007/12/22. pmid:18097223.
  33. 33. Le Hingrat Q, Visseaux B, Bertine M, Chauveau L, Schwartz O, Collin F, et al. Genetic Variability of Long Terminal Repeat Region between HIV-2 Groups Impacts Transcriptional Activity. J Virol. 2020;94(7). Epub 2020/01/10. pmid:31915276; PubMed Central PMCID: PMC7081896.
  34. 34. Soares RS, Tendeiro R, Foxall RB, Baptista AP, Cavaleiro R, Gomes P, et al. Cell-associated viral burden provides evidence of ongoing viral replication in aviremic HIV-2-infected patients. J Virol. 2011;85(5):2429–38. Epub 2010/12/17. pmid:21159859; PubMed Central PMCID: PMC3067805.
  35. 35. Siliciano JD, Siliciano RF. HIV-1 eradication strategies: design and assessment. Curr Opin HIV AIDS. 2013;8(4):318–25. pmid:23698561; PubMed Central PMCID: PMC3963140.
  36. 36. Chun TW, Finzi D, Margolick J, Chadwick K, Schwartz D, Siliciano RF. In vivo fate of HIV-1-infected T cells: quantitative analysis of the transition to stable latency. Nat Med. 1995;1(12):1284–90. Epub 1995/12/01. pmid:7489410.
  37. 37. Finzi D, Hermankova M, Pierson T, Carruth LM, Buck C, Chaisson RE, et al. Identification of a reservoir for HIV-1 in patients on highly active antiretroviral therapy. Science. 1997;278(5341):1295–300. Epub 1997/11/21. pmid:9360927.
  38. 38. Ventura JD. Human Immunodeficiency Virus 1 (HIV-1): Viral Latency, the Reservoir, and the Cure. Yale J Biol Med. 2020;93(4):549–60. pmid:33005119; PubMed Central PMCID: PMC7513431.
  39. 39. Maldarelli F, Wu X, Su L, Simonetti FR, Shao W, Hill S, et al. HIV latency. Specific HIV integration sites are linked to clonal expansion and persistence of infected cells. Science. 2014;345(6193):179–83. Epub 2014/06/28. pmid:24968937; PubMed Central PMCID: PMC4262401.
  40. 40. Wagner TA, McLaughlin S, Garg K, Cheung CY, Larsen BB, Styrchak S, et al. HIV latency. Proliferation of cells with HIV integrated into cancer genes contributes to persistent infection. Science. 2014;345(6196):570–3. Epub 2014/07/12. pmid:25011556; PubMed Central PMCID: PMC4230336.
  41. 41. Cohn LB, Silva IT, Oliveira TY, Rosales RA, Parrish EH, Learn GH, et al. HIV-1 integration landscape during latent and active infection. Cell. 2015;160(3):420–32. Epub 2015/01/31. pmid:25635456; PubMed Central PMCID: PMC4371550.
  42. 42. Lorenzi JC, Cohen YZ, Cohn LB, Kreider EF, Barton JP, Learn GH, et al. Paired quantitative and qualitative assessment of the replication-competent HIV-1 reservoir and comparison with integrated proviral DNA. Proc Natl Acad Sci U S A. 2016;113(49):E7908–e16. Epub 2016/11/23. pmid:27872306; PubMed Central PMCID: PMC5150408.
  43. 43. Hosmane NN, Kwon KJ, Bruner KM, Capoferri AA, Beg S, Rosenbloom DI, et al. Proliferation of latently infected CD4(+) T cells carrying replication-competent HIV-1: Potential role in latent reservoir dynamics. J Exp Med. 2017;214(4):959–72. Epub 2017/03/28. pmid:28341641; PubMed Central PMCID: PMC5379987.
  44. 44. Bui JK, Sobolewski MD, Keele BF, Spindler J, Musick A, Wiegand A, et al. Proviruses with identical sequences comprise a large fraction of the replication-competent HIV reservoir. PLoS Pathog. 2017;13(3):e1006283. Epub 2017/03/23. pmid:28328934; PubMed Central PMCID: PMC5378418 WS, BTL, and BFK are employees of Leidos Biomedical Research, Inc. No other authors report competing financial interests.
  45. 45. Sanchez G, Xu X, Chermann JC, Hirsch I. Accumulation of defective viral genomes in peripheral blood mononuclear cells of human immunodeficiency virus type 1-infected individuals. J Virol. 1997;71(3):2233–40. Epub 1997/03/01. pmid:9032358; PubMed Central PMCID: PMC191331.
  46. 46. Ho YC, Shan L, Hosmane NN, Wang J, Laskey SB, Rosenbloom DI, et al. Replication-competent noninduced proviruses in the latent reservoir increase barrier to HIV-1 cure. Cell. 2013;155(3):540–51. Epub 2013/11/19. pmid:24243014; PubMed Central PMCID: PMC3896327.
  47. 47. Bruner KM, Murray AJ, Pollack RA, Soliman MG, Laskey SB, Capoferri AA, et al. Defective proviruses rapidly accumulate during acute HIV-1 infection. Nat Med. 2016;22(9):1043–9. Epub 2016/08/09. pmid:27500724; PubMed Central PMCID: PMC5014606.
  48. 48. Han Y, Lassen K, Monie D, Sedaghat AR, Shimoji S, Liu X, et al. Resting CD4+ T cells from human immunodeficiency virus type 1 (HIV-1)-infected individuals carry integrated HIV-1 genomes within actively transcribed host genes. J Virol. 2004;78(12):6122–33. Epub 2004/05/28. pmid:15163705; PubMed Central PMCID: PMC416493.
  49. 49. Einkauf KB, Lee GQ, Gao C, Sharaf R, Sun X, Hua S, et al. Intact HIV-1 proviruses accumulate at distinct chromosomal positions during prolonged antiretroviral therapy. J Clin Invest. 2019;129(3):988–98. Epub 2019/01/29. pmid:30688658; PubMed Central PMCID: PMC6391088.
  50. 50. Lenasi T, Contreras X, Peterlin BM. Transcriptional interference antagonizes proviral gene expression to promote HIV latency. Cell Host Microbe. 2008;4(2):123–33. Epub 2008/08/12. pmid:18692772; PubMed Central PMCID: PMC4217705.
  51. 51. Winslow BJ, Pomerantz RJ, Bagasra O, Trono D. HIV-1 latency due to the site of proviral integration. Virology. 1993;196(2):849–54. Epub 1993/10/01. pmid:8372452.
  52. 52. Bednarik DP, Mosca JD, Raj NB. Methylation as a modulator of expression of human immunodeficiency virus. J Virol. 1987;61(4):1253–7. Epub 1987/04/01. pmid:3469417; PubMed Central PMCID: PMC254089.
  53. 53. Schulze-Forster K, Götz F, Wagner H, Kröger H, Simon D. Transcription of HIV1 is inhibited by DNA methylation. Biochem Biophys Res Commun. 1990;168(1):141–7. Epub 1990/04/16. pmid:2327994.
  54. 54. Gutekunst KA, Kashanchi F, Brady JN, Bednarik DP. Transcription of the HIV-1 LTR is regulated by the density of DNA CpG methylation. J Acquir Immune Defic Syndr (1988). 1993;6(6):541–9. Epub 1993/06/01. pmid:8496786.
  55. 55. Van Lint C, Emiliani S, Ott M, Verdin E. Transcriptional activation and chromatin remodeling of the HIV-1 promoter in response to histone acetylation. Embo j. 1996;15(5):1112–20. Epub 1996/03/01. pmid:8605881; PubMed Central PMCID: PMC450009.
  56. 56. Sheridan PL, Mayall TP, Verdin E, Jones KA. Histone acetyltransferases regulate HIV-1 enhancer activity in vitro. Genes Dev. 1997;11(24):3327–40. Epub 1998/02/07. pmid:9407026; PubMed Central PMCID: PMC316802.
  57. 57. Pearson R, Kim YK, Hokello J, Lassen K, Friedman J, Tyagi M, et al. Epigenetic silencing of human immunodeficiency virus (HIV) transcription by formation of restrictive chromatin structures at the viral long terminal repeat drives the progressive entry of HIV into latency. J Virol. 2008;82(24):12291–303. Epub 2008/10/03. pmid:18829756; PubMed Central PMCID: PMC2593349.
  58. 58. Ghose R, Liou LY, Herrmann CH, Rice AP. Induction of TAK (cyclin T1/P-TEFb) in purified resting CD4(+) T lymphocytes by combination of cytokines. J Virol. 2001;75(23):11336–43. Epub 2001/11/02. pmid:11689614; PubMed Central PMCID: PMC114719.
  59. 59. Lassen K, Han Y, Zhou Y, Siliciano J, Siliciano RF. The multifactorial nature of HIV-1 latency. Trends Mol Med. 2004;10(11):525–31. Epub 2004/11/03. pmid:15519278.
  60. 60. Lassen KG, Bailey JR, Siliciano RF. Analysis of human immunodeficiency virus type 1 transcriptional elongation in resting CD4+ T cells in vivo. J Virol. 2004;78(17):9105–14. Epub 2004/08/17. pmid:15308706; PubMed Central PMCID: PMC506937.
  61. 61. Kim YK, Bourgeois CF, Pearson R, Tyagi M, West MJ, Wong J, et al. Recruitment of TFIIH to the HIV LTR is a rate-limiting step in the emergence of HIV from latency. Embo j. 2006;25(15):3596–604. Epub 2006/07/29. pmid:16874302; PubMed Central PMCID: PMC1538560.
  62. 62. D’Orso I, Frankel AD. RNA-mediated displacement of an inhibitory snRNP complex activates transcription elongation. Nat Struct Mol Biol. 2010;17(7):815–21. Epub 2010/06/22. pmid:20562857; PubMed Central PMCID: PMC2921552.
  63. 63. Verdin E, Paras P Jr., Van Lint C. Chromatin disruption in the promoter of human immunodeficiency virus type 1 during transcriptional activation. Embo j. 1993;12(8):3249–59. Epub 1993/08/01. pmid:8344262; PubMed Central PMCID: PMC413592.
  64. 64. Agbottah E, Deng L, Dannenberg LO, Pumfery A, Kashanchi F. Effect of SWI/SNF chromatin remodeling complex on HIV-1 Tat activated transcription. Retrovirology. 2006;3:48. Epub 2006/08/09. pmid:16893449; PubMed Central PMCID: PMC1570494.
  65. 65. Kao SY, Calman AF, Luciw PA, Peterlin BM. Anti-termination of transcription within the long terminal repeat of HIV-1 by tat gene product. Nature. 1987;330(6147):489–93. Epub 1987/12/03. pmid:2825027.
  66. 66. Emiliani S, Van Lint C, Fischle W, Paras P Jr., Ott M, Brady J, et al. A point mutation in the HIV-1 Tat responsive element is associated with postintegration latency. Proc Natl Acad Sci U S A. 1996;93(13):6377–81. Epub 1996/06/25. pmid:8692823; PubMed Central PMCID: PMC39030.
  67. 67. Emiliani S, Fischle W, Ott M, Van Lint C, Amella CA, Verdin E. Mutations in the tat gene are responsible for human immunodeficiency virus type 1 postintegration latency in the U1 cell line. J Virol. 1998;72(2):1666–70. Epub 1998/01/28. pmid:9445075; PubMed Central PMCID: PMC124653.
  68. 68. Sonza S, Mutimer HP, O’Brien K, Ellery P, Howard JL, Axelrod JH, et al. Selectively reduced tat mRNA heralds the decline in productive human immunodeficiency virus type 1 infection in monocyte-derived macrophages. J Virol. 2002;76(24):12611–21. Epub 2002/11/20. pmid:12438587; PubMed Central PMCID: PMC136686.
  69. 69. Lin X, Irwin D, Kanazawa S, Huang L, Romeo J, Yen TS, et al. Transcriptional profiles of latent human immunodeficiency virus in infected individuals: effects of Tat on the host and reservoir. J Virol. 2003;77(15):8227–36. Epub 2003/07/15. pmid:12857891; PubMed Central PMCID: PMC165222.
  70. 70. Weinberger LS, Burnett JC, Toettcher JE, Arkin AP, Schaffer DV. Stochastic gene expression in a lentiviral positive-feedback loop: HIV-1 Tat fluctuations drive phenotypic diversity. Cell. 2005;122(2):169–82. Epub 2005/07/30. pmid:16051143.
  71. 71. Yukl S, Pillai S, Li P, Chang K, Pasutti W, Ahlgren C, et al. Latently-infected CD4+ T cells are enriched for HIV-1 Tat variants with impaired transactivation activity. Virology. 2009;387(1):98–108. Epub 2009/03/10. pmid:19268337; PubMed Central PMCID: PMC4474533.
  72. 72. Landry S, Halin M, Lefort S, Audet B, Vaquero C, Mesnard JM, et al. Detection, characterization and regulation of antisense transcripts in HIV-1. Retrovirology. 2007;4:71. Epub 2007/10/04. pmid:17910760; PubMed Central PMCID: PMC2099442.
  73. 73. Pomerantz RJ, Seshamma T, Trono D. Efficient replication of human immunodeficiency virus type 1 requires a threshold level of Rev: potential implications for latency. J Virol. 1992;66(3):1809–13. Epub 1992/03/01. pmid:1738210; PubMed Central PMCID: PMC240948.
  74. 74. Lassen KG, Ramyar KX, Bailey JR, Zhou Y, Siliciano RF. Nuclear retention of multiply spliced HIV-1 RNA in resting CD4+ T cells. PLoS Pathog. 2006;2(7):e68. Epub 2006/07/15. pmid:16839202; PubMed Central PMCID: PMC1487174.
  75. 75. Sarracino A, Gharu L, Kula A, Pasternak AO, Avettand-Fenoel V, Rouzioux C, et al. Posttranscriptional Regulation of HIV-1 Gene Expression during Replication and Reactivation from Latency by Nuclear Matrix Protein MATR3. mBio. 2018;9(6). Epub 2018/11/15. pmid:30425153; PubMed Central PMCID: PMC6234869.
  76. 76. Omoto S, Ito M, Tsutsumi Y, Ichikawa Y, Okuyama H, Brisibe EA, et al. HIV-1 nef suppression by virally encoded microRNA. Retrovirology. 2004;1:44. Epub 2004/12/17. pmid:15601474; PubMed Central PMCID: PMC544868.
  77. 77. Bennasser Y, Le SY, Benkirane M, Jeang KT. Evidence that HIV-1 encodes an siRNA and a suppressor of RNA silencing. Immunity. 2005;22(5):607–19. Epub 2005/05/17. pmid:15894278.
  78. 78. Weinberg MS, Morris KV. Are viral-encoded microRNAs mediating latent HIV-1 infection? DNA Cell Biol. 2006;25(4):223–31. Epub 2006/04/25. pmid:16629595; PubMed Central PMCID: PMC2861419.
  79. 79. Huang J, Wang F, Argyris E, Chen K, Liang Z, Tian H, et al. Cellular microRNAs contribute to HIV-1 latency in resting primary CD4+ T lymphocytes. Nat Med. 2007;13(10):1241–7. Epub 2007/10/02. pmid:17906637.
  80. 80. Kyei GB, Meng S, Ramani R, Niu A, Lagisetti C, Webb TR, et al. Splicing Factor 3B Subunit 1 Interacts with HIV Tat and Plays a Role in Viral Transcription and Reactivation from Latency. mBio. 2018;9(6). Epub 2018/11/08. pmid:30401776; PubMed Central PMCID: PMC6222122.
  81. 81. Moron-Lopez S, Telwatte S, Sarabia I, Battivelli E, Montano M, Macedo AB, et al. Human splice factors contribute to latent HIV infection in primary cell models and blood CD4+ T cells from ART-treated individuals. PLoS Pathog. 2020;16(11):e1009060. Epub 2020/12/01. pmid:33253324; PubMed Central PMCID: PMC7728277.
  82. 82. Soto-Rifo R, Limousin T, Rubilar PS, Ricci EP, Décimo D, Moncorgé O, et al. Different effects of the TAR structure on HIV-1 and HIV-2 genomic RNA translation. Nucleic Acids Res. 2012;40(6):2653–67. Epub 2011/11/29. pmid:22121214; PubMed Central PMCID: PMC3315320.
  83. 83. Yukl SA, Kaiser P, Kim P, Telwatte S, Joshi SK, Vu M, et al. HIV latency in isolated patient CD4(+) T cells may be due to blocks in HIV transcriptional elongation, completion, and splicing. Sci Transl Med. 2018;10(430). Epub 2018/03/02. pmid:29491188; PubMed Central PMCID: PMC5959841.
  84. 84. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–402. pmid:9254694; PubMed Central PMCID: PMC146917.
  85. 85. Strain MC, Lada SM, Luong T, Rought SE, Gianella S, Terry VH, et al. Highly precise measurement of HIV DNA by droplet digital PCR. PLoS One. 2013;8(4):e55943. pmid:23573183; PubMed Central PMCID: PMC3616050.
  86. 86. Kuiken C, Korber B, Shafer RW. HIV sequence databases. AIDS Rev. 2003;5(1):52–61. pmid:12875108; PubMed Central PMCID: PMC2613779.
  87. 87. Clavel F, Guyader M, Guetard D, Salle M, Montagnier L, Alizon M. Molecular cloning and polymorphism of the human immune deficiency virus type 2. Nature. 1986;324(6098):691–5. pmid:3025743.
  88. 88. Kaiser P, Joshi SK, Kim P, Li P, Liu H, Rice AP, et al. Assays for precise quantification of total (including short) and elongated HIV-1 transcripts. J Virol Methods. 2017;242:1–8. Epub 2016/12/31. pmid:28034670; PubMed Central PMCID: PMC5560404.
  89. 89. Fischer M, Wong JK, Russenberger D, Joos B, Opravil M, Hirschel B, et al. Residual cell-associated unspliced HIV-1 RNA in peripheral blood of patients on potent antiretroviral therapy represents intracellular transcripts. Antivir Ther. 2002;7(2):91–103. pmid:12212929.
  90. 90. Kaiser P, Niederost B, Joos B, von Wyl V, Opravil M, Weber R, et al. Equal amounts of intracellular and virion-enclosed hepatitis C virus RNA are associated with peripheral-blood mononuclear cells in vivo. J Infect Dis. 2006;194(12):1713–23. pmid:17109344.
  91. 91. Chang M, Gottlieb GS, Dragavon JA, Cherne SL, Kenney DL, Hawes SE, et al. Validation for clinical use of a novel HIV-2 plasma RNA viral load assay using the Abbott m2000 platform. J Clin Virol. 2012;55(2):128–33. Epub 2012/07/27. pmid:22832059; PubMed Central PMCID: PMC3444162.
  92. 92. Chang M, Wong AJ, Raugi DN, Smith RA, Seilie AM, Ortega JP, et al. Clinical validation of a novel diagnostic HIV-2 total nucleic acid qualitative assay using the Abbott m2000 platform: Implications for complementary HIV-2 nucleic acid testing for the CDC 4th generation HIV diagnostic testing algorithm. J Clin Virol. 2017;86:56–61. Epub 2016/12/13. pmid:27951466; PubMed Central PMCID: PMC5218592.
  93. 93. Raugi DN, Ba S, Cisse O, Diallo K, Tamba IT, Ndour C, et al. Long-term Experience and Outcomes of Programmatic Antiretroviral Therapy for Human Immunodeficiency Virus Type 2 Infection in Senegal, West Africa. Clin Infect Dis. 2021;72(3):369–78. Epub 2021/02/03. pmid:33527119; PubMed Central PMCID: PMC7850514.
  94. 94. Telwatte S, Martin HA, Marczak R, Fozouni P, Vallejo-Gracia A, Kumar GR, et al. Novel RT-ddPCR assays for measuring the levels of subgenomic and genomic SARS-CoV-2 transcripts. Methods. 2021. Epub 2021/04/22. pmid:33882362.
  95. 95. Telwatte S, Kumar N, Vallejo-Gracia A, Kumar GR, Lu CM, Ott M, et al. Novel RT-ddPCR assays for simultaneous quantification of multiple noncoding and coding regions of SARS-CoV-2 RNA. J Virol Methods. 2021;292:114115. Epub 2021/03/06. pmid:33667568; PubMed Central PMCID: PMC7923865.
  96. 96. Peruski AH, Wesolowski LG, Delaney KP, Chavez PR, Owen SM, Granade TC, et al. Trends in HIV-2 Diagnoses and Use of the HIV-1/HIV-2 Differentiation Test—United States, 2010–2017. MMWR Morb Mortal Wkly Rep. 2020;69(3):63–6. Epub 2020/01/24. pmid:31971928; PubMed Central PMCID: PMC7367036 Journal Editors form for disclosure of potential conflicts of interest. No potential conflicts of interest were disclosed.
  97. 97. Heitzinger K, Sow PS, Dia Badiane NM, Gottlieb GS, N’Doye I, Toure M, et al. Trends of HIV-1, HIV-2 and dual infection in women attending outpatient clinics in Senegal, 1990–2009. Int J STD AIDS. 2012;23(10):710–6. Epub 2012/10/30. pmid:23104745; PubMed Central PMCID: PMC3726192.
  98. 98. Kutyavin IV, Afonina IA, Mills A, Gorn VV, Lukhtanov EA, Belousov ES, et al. 3’-minor groove binder-DNA probes increase sequence specificity at PCR extension temperatures. Nucleic Acids Res. 2000;28(2):655–61. Epub 1999/12/22. pmid:10606668; PubMed Central PMCID: PMC102528.
  99. 99. Damond F, Gueudin M, Pueyo S, Farfara I, Robertson DL, Descamps D, et al. Plasma RNA viral load in human immunodeficiency virus type 2 subtype A and subtype B infections. J Clin Microbiol. 2002;40(10):3654–9. Epub 2002/10/02. pmid:12354861; PubMed Central PMCID: PMC130845.
  100. 100. Damond F, Collin G, Descamps D, Matheron S, Pueyo S, Taieb A, et al. Improved sensitivity of human immunodeficiency virus type 2 subtype B plasma viral load assay. J Clin Microbiol. 2005;43(8):4234–6. Epub 2005/08/06. pmid:16081987; PubMed Central PMCID: PMC1233899.
  101. 101. Avettand-Fenoel V, Damond F, Gueudin M, Matheron S, Melard A, Collin G, et al. New sensitive one-step real-time duplex PCR method for group A and B HIV-2 RNA load. J Clin Microbiol. 2014;52(8):3017–22. Epub 2014/06/13. pmid:24920771; PubMed Central PMCID: PMC4136173.
  102. 102. MacNeil A, Sankalé JL, Meloni ST, Sarr AD, Mboup S, Kanki P. Genomic sites of human immunodeficiency virus type 2 (HIV-2) integration: similarities to HIV-1 in vitro and possible differences in vivo. J Virol. 2006;80(15):7316–21. Epub 2006/07/15. pmid:16840312; PubMed Central PMCID: PMC1563694.
  103. 103. Szojka Z, Karlson S, Jansson M, Medstrand P. Quantification of HIV-2 DNA in Whole Blood. Bio Protoc. 2019;9(20):e3404. Epub 2019/10/20. pmid:33654905; PubMed Central PMCID: PMC7853964.
  104. 104. Soares RS, Matoso P, Calado M, Sousa AE. Strategies to quantify unspliced and multiply spliced mRNA expression in HIV-2 infection. J Virol Methods. 2011;175(1):38–45. Epub 2011/05/05. pmid:21540056.
  105. 105. Malatinkova E, Kiselinova M, Bonczkowski P, Trypsteen W, Messiaen P, Vermeire J, et al. Accurate episomal HIV 2-LTR circles quantification using optimized DNA isolation and droplet digital PCR. J Int AIDS Soc. 2014;17(4 Suppl 3):19674. Epub 2014/11/15. pmid:25397424; PubMed Central PMCID: PMC4225365.
  106. 106. Simon F, Matheron S, Tamalet C, Loussert-Ajaka I, Bartczak S, Pépin JM, et al. Cellular and plasma viral load in patients infected with HIV-2. Aids. 1993;7(11):1411–7. Epub 1993/11/01. pmid:7904166.
  107. 107. Thiébaut R, Matheron S, Taieb A, Brun-Vezinet F, Chêne G, Autran B. Long-term nonprogressors and elite controllers in the ANRS CO5 HIV-2 cohort. Aids. 2011;25(6):865–7. Epub 2011/03/02. pmid:21358376.
  108. 108. Esbjörnsson J, Jansson M, Jespersen S, Månsson F, Hønge BL, Lindman J, et al. HIV-2 as a model to identify a functional HIV cure. AIDS Res Ther. 2019;16(1):24. Epub 2019/09/06. pmid:31484562; PubMed Central PMCID: PMC6727498.
  109. 109. Telwatte S, Lee S, Somsouk M, Hatano H, Baker C, Kaiser P, et al. Gut and blood differ in constitutive blocks to HIV transcription, suggesting tissue-specific differences in the mechanisms that govern HIV latency. PLoS Pathog. 2018;14(11):e1007357. Epub 2018/11/16. pmid:30440043; PubMed Central PMCID: PMC6237391.