Values measured by digital PCR do not vary with cycle number.

<p>Plasmid templates were emulsified into droplets and thermally cycled for 30 to 50 cycles before analysis. No significant differences in measured <i>pol</i> (<b>A</b>) or 2-LTR (<b>B</b>) copy numbers were observed over this range of cycling times. No positive events were observed after 20 cycles (not shown). Similar results were obtained using dilutions of infected CD4+ T cells into uninfected PBMC (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055943#pone.0055943.s001" target="_blank">Fig. S.1</a>). Error bars indicate the observed standard deviation between wells.</p

Effect of sequence variation.

<p>Patient isolates with previously determined <i>pol</i> sequences that differ from the consensus primer/probe set in at least two positions were analyzed by ddPCR and by qPCR. Both assays were conducted in parallel using the mismatched consensus primer/probe set and a patient-specific matched primer/probe set. Use of consensus primers and probe resulted in an underestimate of copy number by one to two log₁₀ by qPCR, with complete loss of detection in the extreme case of 5 total mismatched bases. The underestimate was largely mitigated (mean 57% reduction in log₁₀ copy number change) by ddPCR in all cases. These 4 cases reflect the most extreme mismatches observed in 84 patients, suggesting that sequence variation is unlikely to significantly impact ddPCR assay results in clinical studies. All samples analyzed were HIV-1 subtype B.</p

Assay limit of detection and quantification.

<p>CD4 cells infected in vitro were serially diluted into a background of PBMC DNA. The number of replicate wells was increased from 3 to 36 in proportion to the dilution, allowing accurate measurement below one copy per 10⁶ cells. The shaded area indicates concentrations below the theoretical limit of detection for a single well.</p

Correlation of ddPCR and qPCR measurements.

<p>(<b>a</b>) Pol copy numbers measured by ddPCR and qPCR were significantly correlated (Pearson R² = 0.64, slope = 0.98±0.08). The correlation weakened at low copy numbers, primarily due to a rapid increase in the variance of the qPCR assay (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055943#pone.0055943.s003" target="_blank">Fig. S3</a> (a)). For copy numbers measured in the bottom tertile (<230 HIV DNA copies/10⁶ cells) by ddPCR, the correlation was not significant (R² = 0.08, P = 0.12). In the central tertile, the correlation was weak but significant (R² = 0.15, P = 0.03). In the top tertile, the correlation was strong (R² = 0.53, P<0.0001). (<b>b</b>) 2-LTR copy numbers measured by both methods were significantly but weakly (R² = 0.14, P = 0.002) correlated.</p

Transcriptomic Analysis Implicates the p53 Signaling Pathway in the Establishment of HIV-1 Latency in Central Memory CD4 T Cells in an In Vitro Model

<div><p>The search for an HIV-1 cure has been greatly hindered by the presence of a viral reservoir that persists despite antiretroviral therapy (ART). Studies of HIV-1 latency <i>in vivo</i> are also complicated by the low proportion of latently infected cells in HIV-1 infected individuals. A number of models of HIV-1 latency have been developed to examine the signaling pathways and viral determinants of latency and reactivation. A primary cell model of HIV-1 latency, which incorporates the generation of primary central memory CD4 T cells (T_CM), full-length virus infection (HIV_NL4-3) and ART to suppress virus replication, was used to investigate the establishment of HIV latency using RNA-Seq. Initially, an investigation of host and viral gene expression in the resting and activated states of this model indicated that the resting condition was reflective of a latent state. Then, a comparison of the host transcriptome between the uninfected and latently infected conditions of this model identified 826 differentially expressed genes, many of which were related to p53 signaling. Inhibition of the transcriptional activity of p53 by pifithrin-α during HIV-1 infection reduced the ability of HIV-1 to be reactivated from its latent state by an unknown mechanism. In conclusion, this model may be used to screen latency reversing agents utilized in shock and kill approaches to cure HIV, to search for cellular markers of latency, and to understand the mechanisms by which HIV-1 establishes latency.</p></div

Differentially expressed genes from the p53 signaling pathway during HIV-1 latency.

<p>Gene expression changes overlaid upon the KEGG <i>p53 signaling pathway</i> for all differentially expressed genes with an FDR corrected p-value <0.05. The shade of red indicates the degree of upregulation while the shade of blue indicates the degree of downregulation.</p

Functional analysis of genes modulated in HIV-1 latency.

<p>(A) Protein interaction network (PIN) generated using differentially expressed genes (N = 826) after a log₂FC > 0.5 filter was applied (N = 64). Any single nodes not attached to a main hub were removed for clarity. Abbreviations for the type of interaction are as follows: <i>B</i>–Binding, <i>C</i>–Cleavage, <i>CM</i>–Covalent Modification, <i>GR</i>–Group Relation, <i>+P</i>–Phosphorylation, <i>TR</i>–Transcription Regulation, <i>T</i>–Transformation. Edge coloring of Green indicates activation, Red indicates inhibition, Black indicates unspecified, while Blue is utilized for a group relation. Arrows indicate the direction of interactions. The color gradient of log₂FC for (A) is included with red indicating upregulation and blue indicating downregulation. (B) RT-qPCR validation of a panel of p53 related genes modulated in HIV-1 latency. Fold change is plotted on the log₂ scale with error bars representing standard deviation. Significance for RT-qPCR results was determined with a paired <i>t</i>-test (*p<0.05, **p<0.01, ***p<0.001) and all RNA-Seq results were significant (EdgeR’s FDR corrected <i>p</i>-value < 0.05). (C) Dot plots of gene expression data (i.e., counts per million) from RNA-Seq results for <i>TNFRSF10B</i> (DR5) and <i>FAS</i> (CD95). Abbreviations: <i>UI</i>, uninfected; <i>LI</i>, latently infected. (D) Histograms of DR5 (<i>TNFRSF10B</i>) and CD95 (<i>FAS</i>) protein surface expression from a representative donor. Red line indicates UI and blue line indicates LI. Geometric mean fluorescence intensity (gMFI) is indicated at the right top corner of the histogram and the grey histogram represents unstained control. (E) Surface expression was measured by FACS for DR5 (6 donors) or CD95 (5 donors). Significance was determined with a paired <i>t</i>-test (<i>p</i>-values provided).</p

Effect of pifithrin-α upon establishment of latency.

<p>(A) Time-line of experimental procedures for pifithrin-α treatment of the latently infected (LI) condition. For the majority of experiments pifithrin-α was maintained in culture from day 10 to 17. (B) CD4 and p24 Gag staining from a representative donor throughout the experiment. Numbers in dot-plots represent percentages. (C) p24 Gag positive CD4 negative cells from 5 independent donors at day 13 and day 17 in cells treated (purple symbols) or untreated (black symbols) with pifithrin-α. (D) p24 Gag positive CD4 negative cells at day 19 following reactivation of LI cells with αCD3/αCD28 beads at day 17 in cells previously treated (purple symbols) or untreated (black symbols) with pifithrin-α (day 10 to 17). As a negative control, p24 Gag positive CD4 negative cells were also assessed in unstimulated cells treated with pifithrin-α in a similar manner. (E) In a deviation from the time-line presented in (A), pifithrin-α was added only during the reactivation period from day 17 to 19. p24 positive CD4 negative cells were then determined at day 19 following reactivation of LI cells that were treated (salmon symbols) or untreated (black symbols) with pifithrin-α. As a negative control, p24 Gag positive CD4 negative cells were also assessed in unstimulated cells treated with pifithrin-α in a similar manner. (F) HIV-1 integration analysis in LI cells at day 17 using Alu-PCR with 250 ng of genomic DNA. As a negative control, HIV-1 integration was also assessed in uninfected cells. (G) Correlation of integrated HIV-1 with the percentage of p24 Gag positive CD4 negative cells after reactivation with αCD3/αCD28 beads. Correlation was determined using the Pearson correlation coefficient. Black symbols represent untreated samples and purple symbols represent cells treated with pifithrin-α from day 10 to 17. (H) Reactivation index calculated as the percentage of cells expressing p24 (day 19) after αCD3/αCD28 reactivation divided by the number of integrated copies before reactivation (day 17). Throughout this figure each donor is represented with a different symbol and significance was determined with a paired <i>t</i>-test (<i>p</i>-values provided).</p

Markers of CD4 T cell activation are modulated following αCD3/αCD28 stimulation.

<p>The fold change of a panel of host gene markers of CD4 T cell activation was assessed pre- and post-stimulation with αCD3/αCD28 beads by (A) RNA-Seq and (B) RT-qPCR. Fold changes were calculated for both uninfected (white bars) and latently infected (black bars) cells. Fold changes for all genes assessed by RNA-Seq were significant (FDR corrected <i>p</i>-value <0.05). Fold changes for <i>IL2</i> and <i>KLF2</i> assessed by RT-qPCR were significant in a paired <i>t</i>-test at the following levels: **<i>p</i><0.01 ***<i>p</i><0.001. Fold change is plotted on the log₂ scale with error bars representing standard deviation. The horizontal dotted line in each figure indicates a log₂ fold change cut off of 1 (i.e., actual fold change cut off of 2). Abbreviations are as follows: <i>UI</i>, uninfected; <i>UIA</i>, uninfected activated; <i>LI</i>, latently infected; <i>LIA</i>, latently infected activated.</p

HIV-1 splicing variants are upregulated following αCD3/αCD28 stimulation.

<p>(A) The abundance of HIV-1 unspliced (US), singly spliced (SS), and multiply spliced (MS) transcripts in the LI and LIA conditions was estimated by mapping reads over the two major splice sites D1 and D4 (see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006026#sec010" target="_blank">Materials and Methods</a> for details). No more than 4 reads were identified to map to the HIV-1 genome in samples from the uninfected conditions (UI and UIA), which may reflect expression from endogenous retroviral elements in the human genome. The significance of increases in each class of HIV reads upon activation was assessed using a one-tailed paired t-test but only an increase in MS reads was identified as significant (<i>p</i> = 0.015). (B) Fold changes upon activation of US RNA (US-Gag), MS RNA (MS-Tat/Rev) and polyadenylated RNA were determined by RT-qPCR. Significance of fold changes was confirmed using a one-tailed paired t-test and all RT-qPCR measurements were identified as significant at the <i>p</i><0.001 level. Fold change is plotted on the log₂ scale with error bars representing standard deviation. (C) Surface CD4 and intracellular HIV-1 p24 Gag expression were measured by flow cytometry as indicated in Materials and Methods after isolation and after reactivation with αCD3/αCD28 beads for 48 hours.</p