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

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

Genes responsive to p53 activity.

<p>Genes responsive to p53 activity.</p

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

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

Generation of latently infected cultured T_CM.

<p>(A) Time-line of experimental procedure. A full description of the methodology is provided in Materials and Methods. (B) Analysis of infection in a representative donor. At the time points indicated, CD4 expression and HIV-1 p24 Gag expression was measured by flow cytometry as indicated in Materials and Methods. X-axis indicates HIV-1 p24 Gag staining and Y-axis CD4 staining. At day 17, cells were sorted based on surface CD4 expression. Staining of the cells pre- and post-sorting is indicated. Numbers in dot-plots represent percentages.</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

Comparison of genes dysregulated in latency across four <i>in vitro</i> models of HIV-1 latency.

<p>The comparison between DEGs across four models of HIV-1 latency was performed by constructing a Venn Diagram with tool Venny 2.1.0. All differentially expressed genes were separated into (A) downregulated and (B) upregulated genes and then compared across the following models: the current study (RNA-Seq results from the cultured T_CM model of HIV-1 latency [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006026#ppat.1006026.ref020" target="_blank">20</a>] for 4 donors), Iglesias-Ussel et al. [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006026#ppat.1006026.ref009" target="_blank">9</a>] (microarray results generated from a primary CD4 T cell model for 4 donors), Mohammadi [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006026#ppat.1006026.ref016" target="_blank">16</a>] (RNA-Seq results from a primary CD4 T cell model for a single donor), and Krishnan and Zeichner [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006026#ppat.1006026.ref014" target="_blank">14</a>] (microarray results generated by overlapping DEGs from three HIV-1 infected cell line models).</p

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<p>The greatest obstacle to a cure for HIV is the provirus that integrates into the genome of the infected cell and persists despite antiretroviral therapy. A “shock and kill” approach has been proposed as a strategy for an HIV cure whereby drugs and compounds referred to as latency-reversing agents (LRAs) are used to “shock” the silent provirus into active replication to permit “killing” by virus-induced pathology or immune recognition. The LRA most utilized to date in clinical trials has been the histone deacetylase (HDAC) inhibitor—vorinostat. Potentially, pathological off-target effects of vorinostat may result from the activation of human endogenous retroviruses (HERVs), which share common ancestry with exogenous retroviruses including HIV. To explore the effects of HDAC inhibition on HERV transcription, an unbiased pharmacogenomics approach (total RNA-Seq) was used to evaluate HERV expression following the exposure of primary CD4⁺ T cells to a high dose of vorinostat. Over 2,000 individual HERV elements were found to be significantly modulated by vorinostat, whereby elements belonging to the ERVL family (e.g., LTR16C and LTR33) were predominantly downregulated, in contrast to LTR12 elements of the HERV-9 family, which exhibited the greatest signal, with the upregulation of 140 distinct elements. The modulation of three different LTR12 elements by vorinostat was confirmed by droplet digital PCR along a dose–response curve. The monitoring of LTR12 expression during clinical trials with vorinostat may be indicated to assess the impact of this HERV on the human genome and host immunity.</p