HIV-1 DNA is Maintained in Antigen-Specific CD4+ T Cell Subsets in Patients on Long-Term

Antiretroviral Therapy Regardless of Recurrent Antigen Exposure

William J Hey-Nguyena,b, Michelle Baileya,b, Yin Xua,b, Kazuo Suzukia,b,c, David Van Bockela,b,c,

Robert Finlaysona,d, Andrew Leigh Browne, Andrew Carrc, David A Coopera,b, Anthony D

Kellehera,b,c, Kersten K Koelscha,b,c and John J Zaundersa,b,c .

Running title: HIV-1 DNA in Antigen-Specific CD4+ T Cells

aThe Kirby Institute, UNSW Australia, Sydney, Australia; bCentre for Applied Medical

Research, St Vincent’s Hospital Sydney, Sydney, Australia; cSt Vincent’s Hospital Sydney,

Sydney, Australia; dTaylor Square Private Clinic, Sydney, Australia. eInstitute of Evolutionary

Biology, University of Edinburgh, UK.

Kersten Koelsch and John Zaunders contributed equally to this work.

Address correspondence to William J. Hey-Nguyen: willheyco@gmail.com

Immunovirology and Pathology Laboratory, Kirby Institute,

Level 6, Wallace Wurth Building High Street, UNSW Australia, Kensington NSW 2052

Abstract word count: 250

Text word count: 3,759

Keywords: HIV reservoirs, Antigen-specific CD4 T cells, HIV DNA

1

Abstract:

Background:

Memory CD4+ T cells (mCD4s) containing integrated HIV DNA are considered the main barrier

to a cure for HIV infection. Here we analysed HIV DNA reservoirs in antigen-specific subsets of

mCD4s to delineate the mechanisms by which HIV reservoirs persist during ART.

Methods/Results:

HIV Gag, Cytomegalovirus (CMV) and Tetanus Toxoid (TT) specific mCD4s were isolated

from peripheral blood samples obtained from 11 individual subjects, 2-11 years after

commencing ART. Antigen-specific mCD4s were identified by the sensitive OX40 assay and

purified by cell sorting. Total HIV DNA levels were quantified by real-time PCR, and clonal

viral sequences generated from mCD4 subsets and pre-ART plasma samples. Quantitative results

and sequence analysis were restricted to 5 and 3 study participants respectively, likely due to the

low frequency of the antigen-specific mCD4s and relatively low HIV DNA proviral loads.

Median HIV Gag-, CMV-, and TT-specific mCD4s were 0.61%, 2.46% and 0.78% of total

mCD4s, and contained a median of 2.50, 2.38, and 2.55 log10 copies of HIV DNA per 106 cells

respectively. HIV DNA sequences derived from antigen-specific mCD4s clustered with

sequences derived from pre-ART plasma samples. There was a trend towards increased viral

diversity in clonal viral sequences derived from CMV-specific relative to TT-specific mCD4s.

Discussion/Conclusions:

Despite limitations, this study provides direct evidence that HIV reservoirs persist in memory

CD4+ T cell subsets maintained by homeostatic proliferation (TT) and adds to growing evidence

against viral evolution during ART. Similar future studies require techniques that sample diverse

HIV reservoirs and with improved sensitivity.

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Introduction:

Understanding how the pool of memory CD4+ T cells (mCD4s) containing HIV DNA are

maintained during antiretroviral therapy (ART) is believed crucial to the development of

strategies aimed at purging this reservoir. It is currently unclear whether low levels of ongoing

viral replication which may continually replenish reservoirs continues during ART, while it is

also thought that the long half-life, homeostatic capacity, and antigen driven proliferation of

mCD4s may contribute to reservoir maintenance with little to no disruption of latency.

Delineating the contribution of CD4+ T cell subsets to viral reservoirs has been an important

strategy by which our understanding of reservoir establishment and persistence has progressed.

However, most studies have focused on bulk populations of mCD4s subsets separated by cell

surface phenotype [1-4]. To the best of our knowledge, this manuscript presents the third study

to characterize HIV reservoirs within antigen-specific subsets of mCD4s, an approach we and

others hypothesized will advance our understanding of HIV reservoir establishment and

persistence.

Relatively early in the characterisation of HIV reservoirs Douek et al. observed that, shortly

following ART initiation, HIV-specific mCD4s contained higher levels of total HIV DNA than

other CD4+ T cells [5]. Since this initial observation, only one other study has investigated HIV

reservoirs within antigen-specific mCD4s [6], and in contrast to that observed by Douek et al.,

found that HIV-specific mCD4s isolated from patients with a long-term treated HIV infection

were not preferentially infected by HIV [6]. Jones et al. did however measure relatively high

levels of HIV DNA in Influenza-specific mCD4s in patients receiving annual Influenza

vaccinations, and hypothesized that the resulting antigen driven activation led to an accumulation

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of HIV DNA within these cells [6]. These studies however may have been limited by low purity

of the isolated cell subsets (not described in [5] and ranging from 43.4% to 96.6% in [6]), likely

resulting from the reliance on cytokine production by target cell subsets in response to recall

antigens (IFN-γ in [5] and combined IFN-γ/TNF-α/IL-2 in [6]), and purification by magnetic

bead based separation techniques in [5].

In the present study we utilized the CD25/OX40 co-expression assay [7] followed by flow

cytometric cell sorting, which does not rely on cytokine expression, includes regulatory T cells

(Tregs), Th17 cells and BCL-6+ putative peripheral follicular T helper cells (Tfh) cells [8], and

isolates antigen-specific mCD4s to a high level of purity. We aimed to clarify the relative

contribution of various antigen-specific mCD4s to HIV DNA reservoirs by quantifying proviral

HIV DNA load in highly purified cell populations, and to extend this analysis by investigating

proviral sequences within these cells. We hypothesized that comparing HIV DNA reservoirs

within cytomegalovirus (CMV)-specific mCD4s, a model of chronic antigen exposure and the

regular activation/turnover of mCD4s, and Tetanus Toxoid (TT)-specific mCD4s, a model for

homeostatic proliferation, would help elucidate the pathways involved in HIV reservoir

establishment and maintenance.

Materials & Methods:

Sample Collection

Study participants were recruited through the Immunology and Infectious Diseases Ambulatory

Care Unit at St. Vincent’s Hospital or Taylor Square Private Clinic, Darlinghurst, Australia.

Eligible participants were required to have commenced ART and maintained a pVL <50

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copies/mL for at least 2 years (measurements taken every 3-8 months using Roche Amplicor or

Roche Taqman assays). Participants provided written informed consent, approved by the St.

Vincent’s Hospital Sydney Human Research Ethics Committee (HREC/12/SVH77). 200mL

whole blood (WB) samples were collected by venepuncture into vacutainers containing sodium

heparin (BD Biosciences). Samples were split for either antigenic stimulation of WB, or the

isolation of peripheral blood mononuclear cells (PBMC) by ficoll-paque separation as described

previously [9].

Reagents

Staphylococcal Enterotoxin B (SEB; Sigma-Aldrich) was used at a final concentration of

1µg/mL. CMV lysates were generated by propagating CMV (isolate AD169) on Human

Foreskin Fibroblasts (MRC-5) cells, concentrating and purifying culture supernatant, then lysing

viral particles by freeze-thaw cycles. TT (Statens Serum Institut) was supplied at a concentration

of 762 Limit of Flocculation units (Lf) per mL (equivalent to 1.905mg/mL) and used at a final

concentration of 2Lf/mL (5µg/mL) or 0.4Lf/mL (1µg/mL) for WB and PBMC cultures

respectively. The HIV-1 Consensus B Gag peptides – Complete Set reagent was obtained

through the AIDS Research and Reference Reagent Program (catalogue number 8117), Division

of AIDS, NIAID, NIH. All 123 Gag peptides were pooled and used at a final concentration of

2µg/mL for each peptide.

CD25/CD134(OX40) Assay

Sodium heparin anticoagulated WB was mixed with equal parts IMDM (Life Technologies) as

previously described [7], or, PBMC were suspended in RPMI (Life Technologies) supplemented

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with 10% human AB serum (Lonza). Approximately 120mL WB/IMDM or ~60x106 PBMC

were cultured in T25 or T75 tissue culture flasks (BD Biosciences), at a density of 0.25-

0.35mL/cm2 for WB/IMDM, or a concentration of 1.5-6x106 PBMC/mL and a cell density

between 0.375-1.5x106 PBMC/cm2 for PBMC suspensions. Preliminary experiments were

performed to optimise antigen concentrations and to compare fresh WB or PBMC. Antigens

were added at the specified concentrations (see above), and cultures incubated at 37°C for 44-48

hours in a humidified atmosphere of 5% CO2 in air. Negative control cultures comprised

WB/IMDB or PBMC suspensions with no antigen, and positive control cultures contained SEB

(control cultures were incubated in 24-well plates).

Cell sorting

At the end of the 44-48 hour incubation, WB samples were processed to isolate PBMC by ficoll-

paque separation then all samples were stained with CD3-PerCP-Cy5.5, CD4-PE-Cy7, CD25-

allophycocyanin, CD134-PE, CD20-APC-Cy7 (all BD Biosciences) and CD45RA-ECD

(Beckman Coulter). Samples were incubated for 15 minutes at room temperature, washed with

PBS then resuspended in 0.5% paraformaldehyde (Proscitech). Antigen-specific mCD4s

(CD3+CD20-CD4+CD25+CD134+), memory (CD45RA-) and naïve (CD45RA+) CD3+CD4+ T

cells, and B cells (CD20+CD3-) were isolated by FACS on a FACSAriaTM II cell sorter (BD

Biosciences). The purity of sorted samples was assessed by acquiring a small proportion of the

purified sample on a FACSAriaTM II cell sorter (BD Biosciences) and flow cytometric analysis in

FlowJo (Tree Star Inc.).

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Quantification of total HIV DNA

Genomic DNA was extracted from isolated cell populations using the direct lysis method [10].

Total HIV DNA levels were quantified by quantitative polymerase chain reaction (qPCR)

targeting a fragment within the pol gene (primers/probes listed in Supplementary Table S1) [11],

and normalized for total genomic DNA using the TaqMan β–actin detection kit (Life

Technologies). Reactions contained 25µL iQ Supermix (Bio-Rad Laboratories), 1µM mf299 and

mf302, 100nM ri15 and ri16, 10µL DNA template, and dH2O to a final volume of 50µL.

Reactions were incubated at 95°C for 3 minutes, followed by 45 cycles of 95°C for 15 seconds

then 60°C for 1 minute. Average qPCR efficiencies (mean ± standard deviation; SD) were

95.3±6.1% and 87.4±4.4% (with R2 values all >0.99) for the total HIV DNA and β-actin qPCR

respectively. The dynamic range of the total HIV DNA assay as determined by the plasmid

(pNL4-3) standard curve (mean±SD) was 3x106 copies [quantitative threshold cycle

(Cq)=17.70±0.47] to 3 copies (Cq=37.22±1.00) per reaction. Mean (±SD) values for the β-actin

positive control were 9.07±1.6ng/µL. Normalised total HIV DNA copies (mean±SD) for two

positive controls, DNA extracted from PBMC isolated from an HIV-infected individual diluted

to DNA concentrations of ~35ng/µL and ~3.5ng/µL, were 749.1±426.2 and 768.1±741.3 copies

per 106 cells respectively.

Clonal sequencing of HIV gag and pol gene fragments

Clonal HIV sequences covering portions of the gag and pol genes were generated using DNA

extracted from purified cell populations, and three sequential historic pre-ART plasma samples

(Supplementary Table S2). Plasma RNA was extracted using the QIAGEN viral RNA mini kit,

7

treated with TURBOTM DNase (Life Technologies), and cDNA synthesized using SuperScript®

VILOTM RT (Life Technologies).

Viral gag and pol genes were amplified by nested PCR using the primers listed in Supplementary

Table S1 and extracted DNA or cDNA generated from pre-ART plasma samples. For the nested

gag PCR, 1st round reactions contained 1µM GP1A and GP1B, 0.14µL Platinum® Taq DNA

Polymerase High Fidelity and 2µL 10X High Fidelity PCR Buffer (Life Technologies), 250µM

dNTPs, 2.5mM magnesium sulfate (MgSO4), 5µL cDNA or DNA, and dH2O to a final volume of

20µL. Reactions were incubated at: 94°C for 5 minutes; followed by 35 cycles of 94°C for 1

minute, 52°C for 1 minute and 72°C for 2 minutes; then 72°C for 10 minutes. The 1 st round

product was diluted 1 in 10 in dH2O before addition to a 2nd round reaction identical to the first

except with the primers GP2A and GP2B. For the nested pol PCR, 1st round reactions contained

0.25µM IBF1 and RT-21, 0.14µL Platinum® Taq DNA Polymerase High Fidelity and 2µL 10X

High Fidelity PCR Buffer (Life Technologies), 250µM dNTPs, 1.8mM MgSO4, 5µL cDNA or

DNA, and dH2O to a final volume of 20µL. Reactions were incubated at: 94°C for 2 minutes;

followed by 50 cycles of 94°C for 15 seconds, 55°C for 30 seconds and 72°C for 90 seconds;

then 72°C for 10 minutes. The 1st round product was diluted 1 in 10 in dH2O before addition of

5µL to a 2nd round reaction containing 0.2µM of primers CWF1 and Frenkel 2, 0.14µL

Platinum® Taq DNA Polymerase High Fidelity and 2µL 10X High Fidelity PCR Buffer (Life

Technologies), 250µM dNTPs, 2mM MgSO4, and dH2O to a final volume of 20µL. 2nd round

reactions were incubated at: 94°C for 2 minutes; followed by 40 cycles of 94°C for 30 seconds,

55°C for 1 minute and 72°C for 90 seconds; then 72°C for 10 minutes.

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Nested PCR products were visualized by agarose gel electrophoresis and purified using either the

Wizard® purification kit or QIAGEN QIAquick Gel Extraction kit. Purified PCR products were

cloned using the TOPO TA cloning kit, individual clones were amplified by PCR using primers

listed in Supplementary Table S1, then resulting PCR products purified using 96-well plates with

DNA binding silica membranes (Sigma-Aldrich or Pall Corporation). PCR products were

prepared for sequencing by performing a sequencing reaction using the BigDye® Terminator

v3.1 kit, then sent to the Australian Genome Research Facility for reaction clean-up and sanger

sequencing. As a negative control to account for PCR-induced nucleotide substitutions

introduced during the generation of clonal PCR products, pNL4-3 plasmid was amplified,

cloned, and sequenced following the methods described above.

Sequence alignment and analyses

Raw sequence files were edited to create consensus contigs using the freely available BioEdit

software version 7.19 for Windows [12]. Sequences were tested for APOBEC induced G to A

hypermutation using Hypermut 2.0 software, freely available at www.hiv.lanl.gov [13]. For

assessment of G to A hypermutation, the most prominent plasma RNA sequence from the

earliest pre-ART sample was used as the reference sequence. Sequences with significant

APOBEC3 mediated G to A hypermutation were excluded from estimations of viral diversity, as

editing by APOBEC3 does not reflect viral replication.

Phylogenetic analysis was performed in MEGA software version 6.0 [14]. Sequences were

aligned as codons using MUSCLE [15]. Evolutionary history was inferred using the Maximum

Likelihood method based on the General Time Reversible model [16], with 1000 bootstrap (bs)

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replicates. Codon-based evolutionary divergence between clonal viral sequences was estimated

using the Nei-Gojobori model [17].

Results:

Study participant characteristics

All patients were recruited through a primary infections cohort (determined as described

previously [18]), and were followed over time with regular collection and storage of PBMC and

plasma samples. Study participant characteristics are detailed in Table 1, and CD4+ T cell counts

and pVL are shown in Supplementary Figure S1. Study participant pVL remained below the

limit of detection for a median of 278 (range 129-587) weeks before the collection of samples for

the current study.

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Table 1: Characteristics of study participants at the time of sample collection.

Study participant

Age (years)

Antiretroviral therapy (ART)

Timing of ART

initiation

Time with undetectable pVL (weeks)

Proximal CD4+ T cell

count (cells/mm3)

HIV DNA

analysis

Viral Sequence analysis

1 (●) 54 3TC/ABC/EFV PHI 400 525 Yes No

2 (■) 39 EFV/FTC/TDF CHI 162 468 No Yes

3 (▲) 48 3TC/ABC/RTV/ SQV

CHI 331 429 Yes Yes

4 ( ) 38 3TC/ABC/DRV/ RTV

PHI 129 1860 No No

5 ( ) 52 3TC/ABC/NVP PHI 587 539 Yes No

6 ( ) 58 3TC/ABC/DRV/ RAL/RTV

PHI 278 850 No No

7 (○) 37 3TC/ABC/EFV/ FSPV/MVC/RTV

PHI 436 907 No No

8 ( ) 56 3TC/ABC/EFV/ MVC

CHI 455 1034 No No

9 (∆) 59 FTC/TDF/RAL PHI 241 930 No No

10 (♦) 34 EFV/FTC/TDF CHI 155 819 Yes Yes

11 (▼) 48 FTC/NVP/TDF CHI 277 676 Yes No

Median(range)

48(34-59)

278(129-287)

819(429-1860)

3TC, lamivudine; ABC, abacavir; DRV, darunavir; EFV, efavirenz; FSPV, fosamprenavir; FTC, emtricitabine; MVC, maraviroc; NVP, nevirapine; RAL, raltegravir; RTV, ritonavir; SQV, saquinavir; TDF, tenofovir disoproxil fumarate; pVL, plasma viral load.

Identification and Isolation of Antigen-specific CD4+ T cells

Antigen-specific memory CD4+ T cell responses are reported as the percentage of CD4+ T cells

induced to co-express CD25 and CD134/OX40 (see Figure 1a for the gating strategy and Figure

1b for results). All assay responses are reported as median (range). Responses for the assay

negative control (no antigen) were 0.031% (0.009-0.068%) for PBMC and 0.033 (0.007-0.077%)

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for WB. Responses for the positive control (SEB) were 4.74% (3.0-13.6%) for PBMC and

12.45% (5.2-19.6%) for WB. For test samples, a positive response was defined as >2 SD

(calculated for all negative controls collected during the study and assessed retrospectively)

greater than the negative control for the individual. All 11 participants had detectable CMV-

specific mCD4s of 2.46% (0.903-6.03%), 9/11 participants had discernible HIV Gag-specific

memory CD4+ T cell responses of 0.61% (0.097-1.58%) and all 11 participants had TT-specific

mCD4s responses of 0.78% (0.102-5.44%).

Cellular yields and sort purity are displayed in figure 1c/d. Yields for the antigen-specific

memory CD4+ T cell subsets were: 184,700 (24,540-487,800) for CMV (n=11), 57,800 (16,580-

235,600) for HIV Gag (n=9) and 66,630 (12,320-641,700) for TT (n=11). The majority were

isolated to high levels of purity (>90% for 26/31 populations), however approximately half

(15/31) yielded less than 100,000 cells.

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Figure 1: The stimulation and purification of antigen-specific CD4+ T cell subsets and control populations. (a) Gating strategy for the isolation of antigen-specific memory CD4+ T cells and control populations. Doublets were excluded by comparing FSC-A with FSC-W profile (not shown). Lymphocytes were gated by Forward Scatter Area (FSC-A) and Side Scatter Area (SSC-A) profile. B cells were identified as CD20+CD3dim/neg. CD4+ T cells were divided into memory (CD45RA-) and naïve (CD45RA+) subsets. Antigen-specific memory CD4+ T cells were identified as CD3+CD20-CD4+CD25+CD134+ cells. (b) Antigen-specific memory CD4+ T cell responses. Post cell sorting yields (c) and purity (d). Unique symbols represent individual study participants (see Table 1). Box plots display median with interquartile range, minimum, and maximum values. PBMC; peripheral blood mononuclear cells. WB; whole blood.

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HIV DNA levels in antigen-specific CD4+ T cell subsets

6 of the 11 study participants were excluded from the analysis as total HIV DNA levels were not

detected by qPCR in the majority (2 or more) of the antigen-specific memory CD4+ T cell

subsets tested. The results for the remaining 5 study participants are displayed in Figure 2. As

expected, total HIV DNA levels (log10 copies per 106 cells; median, range) were elevated in

mCD4s (2.629, 2.225-4.095) compared to naïve CD4+ T cells (2.375, 1.427-3850, p=0.06) and

total lymphocytes (1.134, 0-1.904, p=0.06), however did not differ significantly between the

three antigen-specific subsets: CMV (2.385, 1.892-3.734), HIV Gag (2.504, 1.775-3.7) and TT

(2.549, 1.961-3.921).

Figure 2: Total HIV DNA levels were similar in CMV-, HIV Gag-, and TT-specific CD4+ T cell subsets. Unique symbols represent individual study participants (see Table 1). Box plots display median with interquartile range, minimum, and maximum values. ND, not detected. Paired values were compared using Wilcoxon signed rank tests. Open symbols indicate samples that were detected and quantified but were below the dynamic range of the plasmid standard curve.

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Clonal HIV DNA sequences from antigen-specific CD4 T cells

Viral gag and pol sequences were generated from 2 or more of the antigen-specific memory

CD4+ T cell subsets in 3 of the study participants (Figures 3 and S2), as well as the assay control

(pNL4-3) including 33 and 23 clonal sequences for the gag and pol nested PCRs respectively.

Supplementary Figure S3 displays clear phylogenetic separation of the sequences generated from

each study participant and pNL4-3.

Upon phylogenetic analysis, we observed signs of clustering between pre-ART plasma

sequences and cell-associated viral sequences during ART, derived from: lymphocytes [Figure

3c, bs = 0.54], mCD4s (Figure 3c, bs = 0.41) and CMV-specific mCD4s (Figure 3a, bs = 0.14).

However, considering background nucleotide substitution rates and bs values, this clustering was

most likely not significant. We also identified a group of viral gag sequences derived primarily

from TT-specific mCD4s with clear signs of APOBEC3 mediated G to A hypermutation (Figure

3c – green circle).

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Figure 3: Viral sequences from pre-ART plasma samples clustered with cell-associated viral sequences obtained during suppressive ART. Phylogenetic analysis of gag (a/c) & pol (b/d) viral sequences from study participants 2 (a/b) & 10 (c/d). Red circles highlight clusters of pre-ART plasma viral sequences (see Table S1 for timing of plasma collection) with viral sequences derived from cellular HIV reservoirs during long-term suppressive ART. The green circle highlights a cluster of viral clones with evidence of G to A hypermutation primarily derived from TT-specific memory CD4+ T cells. Evolutionary history was inferred by using the Maximum Likelihood method based on the General Time Reversible model [6]. Trees are drawn to scale, with branch length measured in the number of substitutions per sites.

16

Open symbols indicate replication deficient sequences as determined by the presence of stop codons or significant APOBEC3 mediated G to A hypermutation.

17

To assess viral diversity within sets of clonal sequences, we calculated the number of

synonymous changes between each pair of clonal sequences using the Nei-Gojobori model

(Figure 4) [17]. To establish a baseline level resulting from PCR-induced nucleic acid

substitutions we also performed this analysis for the assay control pNL4-3 (Supplementary

Figure S4). To determine whether the nucleotide substitutions observed in viral populations

derived from study participant samples were greater than PCR-induced levels, the distribution

and number of synonymous changes relative to that observed for pNL4-3 control sets, were

compared by Mann-Whitney tests. Viral diversity appeared to increase over time prior to the

initiation of ART (see sequence diversity in pre-ART plasma derived populations in Figure 4).

For proviral gag sequences, a pattern of higher sequence diversity emerged in viral populations

derived from mCD4s relative to naïve CD4+ T cells, and CMV-specific mCD4s compared to

HIV Gag- and TT-specific mCD4s.

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Figure 4: Increased sequence diversity in viral populations from CMV- relative to HIV Gag- and TT-specific memory CD4+ T cells. Viral gag (a) and pol (b) sequence diversity in populations derived from pre-ART plasma & CD4+ T cell subsets during suppressive ART. Column and whisker graphs display median, interquartile range, minimum and maximum values for study participant 2 (green), 3 (blue) and 10 (maroon). n/a, not available. *Indicates populations with genetic diversity indistinguishable from the negative control (pNL4-3) as compared by Mann-Whitney tests.

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Discussion:

With the goal of improving our understanding of how HIV reservoirs are established and

maintained, this study aimed to compare and contrast HIV DNA reservoirs within antigen-

specific memory CD4+ T cell subsets. Although the findings of this study are limited by small

numbers and the methods used, the key results were: total HIV DNA was detected/quantified in

similar amounts in CMV-, TT-, and HIV Gag-specific mCD4s; proviral sequences derived from

pre-ART plasma and immune cell subsets during long-term suppressive ART clustered together

during phylogenetic analysis; replication deficient viral sequences were found in TT-specific

mCD4s; and viral diversity appeared elevated in CMV- relative to TT- and HIV Gag-specific

mCD4s.

There are several differences between our study and the investigation of Influenza-specific

mCD4s [6] that may explain why we did not observe an increased HIV DNA burden in cells we

hypothesised were regularly activated. It is possible that, as CMV-specific mCD4s may be

partially resistant to HIV infection [19, 20], accumulation of HIV reservoirs within this cellular

subset is limited despite regular activation and cellular turnover. While the study of Influenza-

specific memory CD4+ T deliberately perturbed the steady state by vaccination, we examined a

system with a stable HIV reservoir and without exogenously or actively stimulating antigen-

specific mCD4s of interest. Further plausible explanations arise from the limitations faced by

both studies. The measurement of HIV DNA levels in the present study was limited by a small

sample size, likely due to the low cell yields of antigen-specific memory CD4+ T subsets. While

the current study examined highly pure subsets of mCD4s, the purity of isolated cell populations

observed by Jones et al. were substantially lower, with mean (range) purities of: HIV Gag-

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71.6% (43.3-92.3%), TT- 55.3% (48.6-78.4%) and Influenza-specific mCD4s 94.5% (90.8-

96.6%). The relatively high purity of the Influenza-specific memory CD4+ T cell subset in that

study may explain their observation of increased HIV DNA levels within this subset, as the cells

contaminating the HIV Gag- and TT-specific memory CD4+ T cell populations likely contained

HIV DNA at a lower frequency. Of note however, median levels of HIV DNA measured by

Jones et al. for HIV Gag- and TT-specific mCD4s (~log10 2.7 copies / 106 CD4+ T cells) were

similar to that observed for all antigen-specific memory CD4+ T cell subsets in the current study

(~log10 2.4-2.6 copies / 106 CD4+ T cells). The use of, at least in parts, differing methods in these

two studies highlights the difficulty of selecting techniques to balance specificity and efficiency

when designing studies investigating HIV reservoirs in small CD4+ T cell subsets, and it is our

belief that cell populations of interest must be isolated with high levels of specificity and purity

to produce meaningful results.

In agreement with the study by Jones et al. [6], but in contrast with the original observation by

Douek et al. [5] we found that HIV DNA reservoirs were not disproportionately elevated in HIV

Gag-specific mCD4s. While further studies are needed for clarification, it appears probable that

while HIV-specific mCD4s are preferentially infected during untreated infection, they may also

be more susceptible to cell death, and therefore be less likely to seed long-lived viral reservoirs.

Although it is currently unclear whether HIV DNA reservoirs accumulate within regularly

activated subsets of mCD4s, potentially reflecting low levels of ongoing viral replication during

ART, several observations made in this study indicate that ongoing viral replication is not a

significant contributor to the maintenance of HIV reservoirs. The phylogenetic similarities

21

between pre-ART viral sequences and proviral sequences derived from cellular reservoirs during

long-term ART agrees with substantial literature that has found viral reservoirs do not evolve

over time during ART [21-26]. While the trend towards increased sequence diversity in CMV-

relative to HIV Gag- and TT-specific mCD4s may reflect antigen driven proliferation and viral

replication during ART, this finding was not supported by higher levels of total HIV DNA in the

CMV-specific population and is subject to several limitations. This analysis was restricted to 3

study participants, and, as we measured very low HIV DNA copy numbers within sorted cell

subsets there is the risk of total HIV DNA copy number in test samples influencing the diversity

of viral populations. We did not however, find a significant correlation between total HIV DNA

copy number in test samples and measured viral diversity (Pearson correlation coefficient of

0.18, p=0.52). There are also biological explanations for this finding. It is possible that, as

reservoirs are seeded pre-ART, regularly activated CD4+ T cells such as those specific to CMV,

are more susceptible to repeated rounds of reservoir seeding, and the observed viral diversity was

established pre-ART. Although we measured limited viral diversity in HIV Gag-specific mCD4s,

which are also highly activated during untreated HIV infection, as discussed previously, these

cells may be more susceptible to HIV induced death and therefore less likely to repeatedly seed

HIV reservoirs prior to the initiation of ART.

Given the growing evidence that clonal expansion plays a role in the maintenance of HIV

reservoirs [27-30] we were interested in assessing whether antigen driven proliferation of

mCD4s may be involved. While we identified a group of replication deficient viral sequences in

TT-specific mCD4s in one patient, the methodology used means we cannot be sure these

sequences represent a clonal expansion. Nevertheless, it is intriguing this group of sequences was

22

identified in mCD4s unlikely to encounter their cognate antigen, indicating that homeostatic

proliferation is the probable driver. Future studies of antigen-specific mCD4 subsets may play an

important role in elucidating the drivers of clonal expansion of HIV reservoirs.

In general, there are technical difficulties faced when investigating HIV reservoirs in very small

subsets of cells, and this study was no exception. Despite relatively large blood collection

volumes, the low frequency and cellular yields of targeted memory CD4+ T cell populations, and

low levels of HIV DNA within these cell subsets, limited our ability to quantify and genetically

characterise HIV proviral reservoirs. While the selection of patients initiating ART in primary

HIV infection and with long-term successfully treated infection, which likely played a role by

limiting the size and diversity of viral reservoirs [24, 29, 31, 32], such selection criteria were

essential to the aims of this study. Leukapheresis is a potential strategy for improving cell yields,

but this would pose a significantly higher burden on patients, and, at this stage it is not clear that

this method would isolate lymphocytes sufficient for the analysis attempted by this study. Newer

technologies including digital PCR may somewhat improve accuracy when quantifying samples

with low copy numbers and therefore assist future studies [33, 34]. Furthermore, other methods

such as single-proviral sequencing that has been successfully used to genetically characterise

viral populations of HIV [24, 29, 35, 36], or modern single-cell analysis techniques, should be

considered. Given recent evidence of HIV reservoir activity within lymph nodes and B cell

follicles [37, 38], it is also important to consider whether sampling peripheral blood is the best

approach to characterise HIV reservoirs.

23

While this study yielded interesting results by delineating cellular subsets contributing to HIV

reservoirs, several limitations of this approach were highlighted during this body of work.

Nevertheless, the observations made here in general agreed with the growing evidence that

cellular proliferation, whether antigen driven or via homeostatic mechanisms, is involved in the

persistence of the HIV DNA reservoir.

Acknowledgements:

We would like to express our gratitude to: all the patients who volunteered their time for this

study; Karen McRae for the collection of patient samples; Tracey Barrett for excellent

administrative support; and Philip Cunningham, Professor Bill Rawlinson and Dr Stuart Turville

for providing reagents and assisting in the generation of the CMV lysate reagent.

This study was funded by a Project grant from the National Health and Medical Research

Council (NHMRC) Australia; APP1010185 Memory CD4 T cells that harbour the reservoir of

latent HIV infection: their antigen specificity, function and frequency of antigen-driven

reactivation (JZ and KK). AK and JZ are supported by Fellowships from the NHRMC and WHN

was supported by an Australian post-graduate award.

Sequence Data:

Nucleotide sequence alignments were deposited in GenBank with the accession numbers:

MH557412-MH557792.

24

Supplementary Information:

Table S1: Primers used to quantify HIV DNA and amplify HIV DNA for clonal sequencing.

PCR Primer/probe Sequence (5’ → 3’) 5’ mod 3’ mod bp Binding

site*

HIV pol qPCR

mf299 GCA CTT TAA ATT TTC CCA TTA GTC CTA 27 2536-2562

ri15 CAG [G]A[A] T[G]G [A]TG G 6-FAM BHQ-1 13 2590-2602

ri16 CTG [T]C[A] A[T]G [G]CC A 6-FAM BHQ-1 13 2619-2631

mf302 CAA ATT TCT ACT AAT GCT TTT ATT TTT TC 29 2634-2662

HIV gag Nested PCR

GP1A CCC TTC AGA CAG GAT CAG 18 989-1006

GP1B CCA CAT TTC CAA CAG CCC 18 2022-2039

GP2A GCA CAG CAA GCA GCA GCT 18 1132-1149

GP2B GTG CCC TTC TTT GCC ACA 18 1972-1989

HIV pol Nested PCR

IBF1 TGA TGA CAG CAT GYC ARG GAG T 22 1826-1847

RT-21 CTG CTA TTA ADT CTT TTG CTG GG 23 3509-3531

CWF1 GAA GGA CAC CAA ATG AAA GAY TG 23 2044-2066

Frenkel 2 GTA TGT CAT TGA CAG TCC AGC 21 3301-3321

Colony PCRM13F GTA AAA CGA CGG CCA G 16 n/a

M13R CAG GAA ACA GCT ATG AC 17 n/a

Degenerate bases: Y = C or T; R = A or G; D = A, G or T. bp = base pairs. Brackets indicate locked nucleic acids. Mod = modification; 6-FAM = 6-Carboxyfluorescein; BHQ-1 = Black Hole Quencher 1. *Relative to HIV B reference genome (HXB2). n/a = not available. PCR = Polymerase Chain Reaction. qPCR = quantitative PCR.

25

Table S2: The timing of pre-ART plasma sample collection.

Study participant # and symbol

Estimated months since HIV infection for:

Pre-ART plasma samplesThe collection of peripheral

blood and cell sorting1 2 3

2 (■) 0.7 39.7 64.5 161.1

3 (▲) 3.3 20.3 35.0 106.1

10 (♦) 0.6 11.7 22.3 111.3

26

Figure S1: CD4+ T cell count and plasma viral load (pVL) of each study participant. ▼ indicates the timing or ART initiation; ■ indicates the timing of when screening samples were collected; □ indicates the timing of plasma sample collection for HIV RNA sequence analysis; ♦ indicates the timing of sample collection for purification of antigen-specific CD4+ T cells.

27

Figure S2: Phylogenetic analysis of clonal gag viral sequences in study participant 3. Evolutionary history was inferred by using the Maximum Likelihood method based on the General Time Reversible model [6]. Trees are drawn to scale, with branch length measured in the number of substitutions per sites. Open symbols indicate replication deficient sequences as determined by the presence of stop codons or significant APOBEC3 mediated G to A hypermutation.

28

Figure S3: Phylogenetic analysis of all clonal gag (a) and pol (b) viral sequences indicates no cross-contamination. Unique symbols represent individual study participants (see Table 1). Evolutionary history was inferred using the Maximum Likelihood method based on the General Time Reversible model [6], with 1000 bootstrap replicates. Trees are drawn to scale, with branch length measured in the number of substitutions per site.

29

Figure S4: Baseline numbers of PCR-induced synonymous changes between pairs of clonal sequences. Sets of clonal sequences were generated following nested PCR amplification of pNL4-3 plasmid. The number of synonymous changes between each pair of clonal sequences was estimated using the Nei-Gojobori model [31].

30

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Reprint Requests:

36

Please address reprint requests to William Hey-Nguyen.

Email: willheyco@gmail.com.

Mailing address: Immunovirology and Pathology Laboratory, Kirby Institute, Level 6, Wallace

Wurth Building High Street, UNSW Australia, Kensington NSW 2052.

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