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      What do we measure when we measure cell-associated HIV RNA

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          Abstract

          Cell-associated (CA) HIV RNA has received much attention in recent years as a surrogate measure of the efficiency of HIV latency reversion and because it may provide an estimate of the viral reservoir size. This review provides an update on some recent insights in the biology and clinical utility of this biomarker. We discuss a number of important considerations to be taken into account when interpreting CA HIV RNA measurements, as well as different methods to measure this biomarker.

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          HIV: Shock and kill.

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            The Depsipeptide Romidepsin Reverses HIV-1 Latency In Vivo

            Pharmacologically-induced activation of replication competent proviruses from latency in the presence of antiretroviral treatment (ART) has been proposed as a step towards curing HIV-1 infection. However, until now, approaches to reverse HIV-1 latency in humans have yielded mixed results. Here, we report a proof-of-concept phase Ib/IIa trial where 6 aviremic HIV-1 infected adults received intravenous 5 mg/m2 romidepsin (Celgene) once weekly for 3 weeks while maintaining ART. Lymphocyte histone H3 acetylation, a cellular measure of the pharmacodynamic response to romidepsin, increased rapidly (maximum fold range: 3.7–7.7 relative to baseline) within the first hours following each romidepsin administration. Concurrently, HIV-1 transcription quantified as copies of cell-associated un-spliced HIV-1 RNA increased significantly from baseline during treatment (range of fold-increase: 2.4–5.0; p = 0.03). Plasma HIV-1 RNA increased from <20 copies/mL at baseline to readily quantifiable levels at multiple post-infusion time-points in 5 of 6 patients (range 46–103 copies/mL following the second infusion, p = 0.04). Importantly, romidepsin did not decrease the number of HIV-specific T cells or inhibit T cell cytokine production. Adverse events (all grade 1–2) were consistent with the known side effects of romidepsin. In conclusion, romidepsin safely induced HIV-1 transcription resulting in plasma HIV-1 RNA that was readily detected with standard commercial assays demonstrating that significant reversal of HIV-1 latency in vivo is possible without blunting T cell-mediated immune responses. These finding have major implications for future trials aiming to eradicate the HIV-1 reservoir. Trial Registration clinicaltrials.gov NTC02092116
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              Activation of HIV Transcription with Short-Course Vorinostat in HIV-Infected Patients on Suppressive Antiretroviral Therapy

              Introduction One of the major barriers to a cure for human immunodeficiency virus (HIV) infection are long lived latently infected memory CD4+ T-cells that persist in patients on suppressive antiretroviral therapy (ART) [1], [2]. One approach currently being investigated to eliminate latently infected cells is to induce production of virus from latently infected cells making the recently activated latently infected cell susceptible to death from virus-induced cytolysis or induction of HIV-specific T-cells [3]. Histone deacetylase inhibitors (HDACi) can activate HIV production efficiently in nearly all latently infected cell lines [4]–[9] and in many but not all primary CD4+ T-cell models of latency [10]. Using resting CD4+ T-cells from HIV-infected patients on cART ex vivo, HDACi induce both virus transcription and production of free virus [11], although the amount of virus produced from resting CD4+ T-cells is significantly less than that induced by a T-cell mitogen [12]. The pan HDACi vorinostat, the first HDACi to be licensed for the treatment of cutaneous T-cell lymphoma [13] is a less potent activator of latent HIV than other HDACi such as romidepsin [11], [12], [14], but has been clearly shown to induce virus production from resting CD4+ T-cells from HIV-infected patients on cART ex vivo by some groups in both the absence [15] or presence [8], [16] of activated feeder cells while other groups have shown minimal virus production [12], [17]. There has been recent data suggesting that HDACi may only activate HIV transcription through stimulation of a host gene promoter leading to the production of chimeric host-HIV transcripts, or read-through transcripts, and not true CA-US HIV RNA raising a concern that HDACi are unable to induce virion production [12]. However, studies using other models of ex vivo stimulation of resting CD4+ T-cells from HIV-infected patients on ART have not supported these findings [11]. Furthermore, recent data from a clinical trial of the HDACi romidepsin clearly demonstrated that virus could be produced in vivo following intravenous administration of this HDACi to HIV-infected patients on ART [18]. Recently, vorinostat was demonstrated to activate HIV transcription in vivo in resting memory CD4+ T-cells in HIV-infected subjects on ART who had been selected based upon ex vivo increase in HIV transcription by vorinostat [16], [19]. We hypothesized that a multi-dose course of vorinostat would increase HIV transcription in CD4+ T-cells in blood from unselected HIV-infected patients on suppressive ART. We aimed to determine the safety and tolerability of short course vorinostat in HIV-infected patients on ART and to determine the effect on cell associated unspliced (CA-US) HIV RNA in CD4+ T-cells and the number of latently infected cells in blood and rectal tissue. Results Multiple doses of vorinostat were well tolerated and induced an increase in histone acetylation and cell associated unspliced HIV RNA Participants' median baseline CD4+ T-cell count was 721 (IQR 621, 907) cells/µl and duration of virus suppression was 5.0 (IQR 3.9, 7.5) years ( Table 1 ). All enrolled subjects completed the study as planned. Adverse events were mild or moderate in severity ( Tables S1 and S2 ) and there were no significant interactions with ART ( Table S3 ). 10.1371/journal.ppat.1004473.t001 Table 1 Baseline characteristics of study participants. Patient ID Gender (M = Male, F = Female) Age (years) Baseline CD4 (cells/µL) Duration of virological suppression (years) Regimenc NNRTI or PI based regimen VOR001 M 49.8 710 5.0b TDF+3TC+EFV once daily NNRTI VOR002 M 51.2 494 7.7 TDF/FTC once daily+NVP twice daily NNRTI VOR003 M 56.6 479 4.0b TDF/FTC/EFV once daily NNRTI VOR004 M 40.8 725 13.4 ABC/3TC once daily+NVP twice daily NNRTI VOR006 F 41.0 743 11.0 TDF/FTC/EFV once daily NNRTI VOR008 M 49.2 863 5.6 TDF/FTC/EFV once daily NNRTI VOR009 M 54.9 703 7.5 TDF once daily+3TC once daily+NVP twice daily NNRTI VOR010 M 49.3 371 4.4 TDF/FTC once daily+NVP twice daily NNRTI VOR011 M 55.6 1136 7.4 TDF/FTC/EFV once daily NNRTI VOR013 M 45.0 1098 5.9b TDF/FTC+DRV+r once daily PI VOR014 M 43.6 717 3.5 TDF/FTC/EFV once daily NNRTI VOR015 M 40.4 538 3.7 TDF/FTC/EFV once daily NNRTI VOR016 M 43.2 951 4.9b AZT+TDF+LPV+r twice daily PI VOR017 M 47.5 1335 4.5b ABC/3TC once daily+NVP once daily NNRTI VOR018 M 48.4 561 2.7 ABC/3TC+ATV+r once daily PI VOR019 M 42.9 855 3.5 TDF/FTC/EFV once daily NNRTI VOR020 M 45.4 694 4.0b ABC/3TC once daily+TDF once daily+DRV+r twice daily+RAL twice daily PI/INI VOR021 M 35.0 658 3.6 TDF/FTC+ATV+r once daily PI VOR022 M 52.7 1094 7.9 AZT+3TC+LPV+r twice daily PI VOR023 M 53.7 727 7.5 TDF/FTC+NVP SR once daily NNRTI Summary Value a 19 Male (95%) 47.9 (43.1–52.0) 721 (610–907) 5.0 (3.9–7.5) a Values represent n (% with characteristic) or median (interquartile range). b Patient had a single HIV RNA viral load to 0.05 for all comparisons). 10.1371/journal.ppat.1004473.g002 Figure 2 Individual changes in CA-US HIV RNA in blood and tissue. A) Fold change in CA-US HIV RNA following vorinostat in CD4+ T-cells from blood (left panel) and rectal tissue (right panel) compared to baseline. The maximum fold change in CA-US HIV RNA on study (solid column) and change at day 84 (open column) is shown for CD4+ T-cells from blood; and change at day 14 for rectal tissue is shown for each participant (upper panel) and the median (IQR) change for all participants (lower panel). The grey dashed line indicates 1-fold change. B) Time to reach maximum fold increase in CA-US HIV RNA for each participant. Grey shaded box represents the time on vorinostat. (C) Correlation between baseline CA-US HIV RNA and peak CA-US HIV RNA (left panel) and day 84 CA-US HIV RNA (right panel). 10.1371/journal.ppat.1004473.g003 Figure 3 Effects of vorinostat on CA-US HIV RNA, HIV DNA and HIV RNA in blood and tissue. Changes in (A) CA-US HIV RNA (red), (B) plasma HIV RNA (green) and (C) HIV DNA (blue) is shown for each study participant (solid circle) and the median (IQR) at each time point in CD4+ T-cells from blood (left panel) and rectal tissue (right panel). Open circles represent data when at least one of the replicates were below the lower limit of detection (LLOD). The mean fold change in each parameter is also shown using a generalised estimating equation analysis (middle panel; boxes represent the median, 50th and 75th percentiles and whiskers represent the range). Grey shaded box represents the time on vorinostat. *p LLOD) in plasma HIV RNA at more than one time point during the study (peak 160 copies/ml at day 7) with a significant increase in CA-US HIV RNA (peak at day 28) and marked increase in PD-1 expression on CD8+ T-cells ( Figure 4 ). This patient had had evidence of long term durable control of HIV RNA on ART with plasma HIV RNA 500 cells/µL and documented subtype B HIV-1 infection. We excluded individuals with significant acute illness, hepatic or cardiac disease, diabetes, malignancy, transplantation or recent use of immunomodulatory agents. We initially excluded patients receiving protease inhibitor regimens but this was modified once further data had become available and subsequently there were no exclusions on the basis of antiretroviral regimen. Participants provided informed consent and The Alfred Human Research Ethics Committee approved the study. The study is registered at ClinicalTrials.gov (NCT01365065). Participants received vorinostat 400 mg orally once daily for 14 days. Blood was collected at 0, 2, 8 and 24 hours, and on days 7, 14, 21, 28 and 84. Rectal biopsies were performed at baseline and on day 14. Participants were monitored for clinical and laboratory adverse events, graded according to the National Cancer Institute Common Terminology Criteria for Adverse Events (Version 4.0). A Data Safety Monitoring Board reviewed safety and tolerability data after the first and tenth participants had completed dosing. Study endpoints The primary study objective was to evaluate the effect of vorinostat on HIV transcription. We measured CA-US HIV RNA because this is the first product of HIV transcription and is required to ultimately synthesise MS-HIV RNA, viral proteins and single stranded viral RNA needed for new virion production [46], [47]. Secondary efficacy endpoints were a sensitive measure of plasma HIV RNA with a lower limit of detection (LLOD) of 0.3 copy per mL [20] cell associated and integrated HIV DNA (a measure of the total number of infected CD4+ T-cells); and histone (H3, H4 and lysine) acetylation (a pharmacodynamic marker of vorinostat activity). Safety endpoints were plasma HIV RNA measured using a commercial assay with a LLOD of 20 copies per ml (TAQMAN v2, Roche), adverse events, serious adverse events, dose limiting toxicity, CD4+ T-cell count and plasma trough concentrations of antiretroviral agents. Because HDACi can activate DNA viruses we quantified cytomegalovirus (CMV) and Epstein-Barr virus (EBV) DNA at baseline and day 28. Measurement of trough concentrations of antiretroviral agents Trough concentrations of non-nucleoside reverse transcriptase inhibitors (NNRTI) or protease inhibitors (PI) in blood were performed at baseline and day 14 using a validated high performance liquid chromatography (HPLC) assay. Histone acetylation Thawed PBMC were permeabilised, fixed with 90% methanol, then stained using antibodies to acetylated (Ac) histone (H)3, and Ac lysine (Millipore, Billerica, MA) and Ac H4 (kind gift from Dr Jeff Lifson, National Cancer Institute Frederick, Frederick, MD) and associated isotype controls with secondary staining with either goat-anti-rat PE or goat anti-mouse-FITC (Invitrogen). Lymphocytes were gated by size and data expressed as MFI above isotype control. Fold changes were determined by comparison of MFI at each time point above baseline MFI. CA-US HIV RNA and HIV DNA in CD4+ T-cells CD4+ T-cells were isolated from stored peripheral blood mononuclear cells (PBMC) using a CD4+ T-cell isolation kit and magnetic-activated cell sorting (MACS) columns (Miltenyi Biotec, Teterow, Germany; purity >95%) and RNA and DNA extracted (Allprep isolation kit, Qiagen). For quantification of CA-US RNA, a semi-nested real time quantitative (q) PCR was used with a first round amplification of 15 cycles to ensure that following second round amplification the assay was in the linear range between 1 to 46,000 input copies, as previously described by Pasternak et al [48]. The second round used primers to gag [46]. HIV RNA copy numbers were standardised to cellular equivalents using an 18s RNA real time LUX PCR primer set (Invitrogen). The LLOD for CA-US HIV RNA was 1 copy per well. PCR amplification of cDNA for CA-US HIV RNA was performed in quadruplicate with an intra-assay coefficient of variation (CV) of 32%. In all assays, a no reverse transcriptase (RT) control was used. If there was any amplification from the no RT control, ie. evidence of DNA contamination, a second stored sample was re-extracted. If contaminating DNA persisted, the reading was excluded. Repeat extraction was required for only 2 of a total of 200 samples analysed for this study. HIV DNA was quantified as previously described [49]. PCR for HIV DNA was performed in triplicate for all samples with an intra-assay CV of 21%. Integrated DNA was measured in total CD4+ T-cells as previously described [50]. HIV RNA and HIV DNA from rectal tissue Single cell mononuclear cell suspensions were obtained from rectal biopsies [51]. Cells were stained using a cocktail of antibodies to CD3, CD8, CD45 and CD4 (Multitest, BD Biosciences, Franklin Lakes, NJ) and sorted for CD45+CD3+ cells using high speed flow cytometry (FACSAria, BD Biosciences). CA-US HIV RNA and HIV DNA were quantified as above. Inducible virus quantification by Tat Rev Inducible Limiting Dilution Assay (TILDA) CD4+ T cells were isolated from PBMCs from study participants by negative magnetic selection (StemCell), and stimulated with phorbol myristate acetate (PMA; 100 ng/mL) and ionomycin (1 µg/mL) for 12 h. Serial dilutions of the stimulated cells were placed in a 96 well plate directly in RT-PCR buffer using 1 in 10 dilutions (4 times) and with 24 replicates at each dilution. MS HIV RNA was quantified by semi nested real time PCR with primers in tat and rev as previously described [52] with some minor modifications. The frequency of positive cells was calculated using the maximum likelihood method [53] and this number was then expressed as a frequency of cells with inducible MS HIV RNA per million CD4+ T-cells. Phenotyping for immune activation Immune activation and differentiation were quantified as previously described [54]. In brief, one million thawed PBMC were stained with either an activation or differentiation panel for 15 minutes at 37°C prior to fixation in formaldehyde. Both panels included CD3 V450 (Becton Dickinson); CD4 PE-Texas Red (Invitrogen); CD8 Qdot605 (Invitrogen). Activation panel included HLA-DR FITC; PD-1 AF647; CD38 PE; CCR5 PE-Cy5; 45RA PE-Cy7 (all Becton Dickinson); CCR7 APC eFluor-780 (eBioscience). Differentiation panel included CD45RA PE; CD28 PE-Cy5; CCR7 PE-Cy7; CD31 FITC (all Becton Dickinson); CD57 AF647 (Biolegend); CD27 AF780 (eBioscience). For Tregs, PBMC were surface stained with CD4 PerCP, CD127 PE and CD25 FITC (all Becton Dickinson) followed by intracellular staining using eBioscience FoxP3 staining kit and FoxP3 APC as per manufacturer's instructions. Data was acquired on a BD LSR-Fortessa and analysed using FlowJo version 10. Intracellular cytokine staining Thawed PBMC were rested for 12 hours prior to stimulation of 1.5 million cells each with Brefeldin A (Sigma Aldrich) and either gag peptides (1 ug/peptide/mL; NIH AIDS reagent program); Staphylococcal enterotoxin B (SEB; 1 ng/mL) or Dimethyl sufoxide (DMSO) for 6 hours. Cells were then surface stained with CD3 AlexaFluor700, CD8 Pacific Blue, CCR7 PE-CF594, PD-1 PE-Cy7 (all BD Biosciences), CD4 Qdot 605 (Invitrogen), CD45RA Brilliant Violet 650, CD19 Brilliant Violet 510 (Biolegend), CD27 APCe780, and aqua fluorescent reactive dye (Invitrogen), permeabilised with Saponin and stained intracellularly with IL-2 PerCP-Cy5.5, IFNγ APC and TNFα Alexa Fluor 488 (all BD Biosciences) prior to fixation. Cells were acquired within 24 hrs using a BD LSR-II and analysed using FlowJo version 9 and 10. Gene microarray and bioinformatic analyses Blood was collected directly into Paxgene tubes and cells lysed for RNA extraction as per manufacturer's instructions (Qiagen, Valencia, CA). Reverse transcription reactions were performed to obtain cDNAs which were hybridized to the Illumina Human HT-12 version 4 Expression BeadChip according to the manufacturer's instructions and quantified using an Illumina iScan System. The data were collected with Illumina GenomeStudio software. Analysis of the genome array output data was conducted using the R statistical language [55] and the LIMMA statistical package [56] from Bioconductor [57]. First, arrays displaying unusually low median intensity, low variability, or low correlation relative to the bulk of the arrays were tagged as outliers and were discarded from the rest of the analysis. Quantile normalization followed by a log2 transformation using the Bioconductor package LIMMA was applied to process microarrays. The LIMMA package was used to fit a linear model to each probe and to perform a (moderated) Student's t test on various differences of interest. For data mining and functional analyses, genes that satisfied a p-value (0.05) were selected. Probes that did not map to annotated RefSeq genes and control probes were removed. When indicated, the expected proportion of false positives, the false discovery rate (FDR), was estimated from the unadjusted p-value using the Benjamini and Hochberg method [58]. The full dataset was composed of 9 patients with 8 time points each. Samples were stratified into the following groups: Day 0 (baseline), 2 hours, 8 hours, Day 1, Day 14 (all on vorinostat); and Day 84 (off vorinostat). One set of analyses compared each time point to baseline (to show the persistent effect of vorinostat over time). The other set compared baseline gene expression at 2 hours, 8 hours and 1 day (to isolate the early effects of vorinostat following the initial dose). In 5 individuals, the additional time points including Day 7 and Day 7+ 2 hours were collected to determine if changes seen 2 hours after the first dose were the same as those seen at 2 hours following the 7th dose. Heatmaps of genes differentially expressed between different groups and baseline were produced ( Figure S3 ). Two ANOVA (F-test) heatmaps comparing the groups of interest: 2 hours, 8 hours, Day 1 ( Figure 6A ) and Day 0+2 hours, Day 7, Day 7+2 hours ( Figure 8A ) were produced. The top 50 statistically significant genes are shown as symbols and plotted on the row names of the heatmaps. Gene expression within each heatmap is represented as gene-wise standardized expression (Z-score), with p-value<0.05 chosen as the significant level. Gene Set Enrichment Analysis (GSEA) [59] was performed on the various contrasts of interest. GSEA is a statistical method to determine whether members of a particular gene set preferentially occur toward the top or bottom of a ranked-ordered gene list where genes are ranked by the strength of their association with the outcome of interest. More specifically, GSEA calculates a net enrichment score (NES) that reflects the degree to which a set of genes is over-represented among genes that are differently expressed. We apply a nominal p-value cutoff of 0.05 when plotting the top enriched pathways on a checkerboard figure. The NES and p-value rankings usually go hand in hand i.e.: the top 10 NES pathway scores equate with the top 10 significant p-values). Since gene expression is tightly regulated, we do not apply statistical cutoffs on the actual FC's of genes when performing pathway enrichment. We try to include as many genes as possible to capture enriched and coregulated transcripts within a pathway that are an indication of relative pathway activity. The significance of an observed NES is obtained by permutation testing: resorting the gene list to determine how often an observed NES occurs by chance. Leading Edge analysis was performed to examine the particular genes of a gene set contributing the most to the enrichment. Two different databases were used: Ingenuity Pathway Analysis software (Ingenuity H Systems, www.ingenuity.com) was used to mine canonical pathways while MSigDB (www.broadinstitute.org/msigdb; Broad Institute, Cambridge, MA) was used to mine chromatin and splicing pathways. A list of significant pathways ranked by p-value and NES is provided ( Dataset S1 ). Linear regression analysis was performed between CA-US HIV RNA at 2 hours and the gene expression 2 hours after the first dose of vorinostat versus baseline (n = 9). CA-US HIV RNA was plotted as a continuous variable and correlated with distinct gene expression profiles at low and high levels of CA-US HIV RNA ( Figure 7A ). About 2000 features passed the p-value cut off of <0.05. Pathway analysis was performed on the regression features and a checkerboard figure with some of the top resulting pathways was produced ( Figure 6B ). A radial plot ( Figure S4C ) illustrating the different enrichment scores in PBMC cell specific subsets [60] between samples collected at day 0+2 hours and day 7+2 hours is shown. Checkerboard figures were used as a representation of the pathway analysis results representing the top genes and the top pathways for a specific contrast. Checkerboard plots show the top 10 enriched pathways on one axis and leading edge analysis (genes contributing to that enrichment) on the corresponding axis. This approach allows quick visualization of what genes are up regulated (red) or down regulated (blue) in the respective pathway at the specified contrast. Checkerboard analysis was also performed on a cell subset level [60] and the same plots generated together with a subset enrichment heat map displaying the contrasts on the x-axis and the subset on the y-axis ( Figure 9C ). Statistical methods A sample size of 20 patients gave 80% power to detect an increase in CA-US HIV RNA of 40 copies/million CD4 T cells (primary endpoint) and an increase in plasma HIV RNA using the single copy assay of 0.4 log (secondary efficacy endpoint) at a p<0.05 level of significance. Categorical variables were summarised using frequency and percentage whilst continuous variables were summarised using mean and standard deviation (SD) or median and inter-quartile range (IQR) as appropriate. Spearman rank correlation coefficients were calculated between virologic and immunologic measurements. Intra-individual comparisons of CA-US RNA and HIV DNA between baseline and post-baseline time points were performed using parametric summary measures of replicate PCR data and a parametric paired t-test as PCR replicate data were derived using a standard curve and thus approximated a normal distribution. Bonferroni adjustment was made for multiple comparisons. Whilst we were prepared to presume normality at the level of the individual replicate data directly derived from the standard curve, we were less prepared to extend this assumption of approximate normality to the more sparse, more severely skewed summary data items not directly derived from the standard curves, opting for the more conservative non-parametric approach. As such, comparisons of fold change MFI for acetylation, CA-US RNA, fold change in CA-US RNA, HIV DNA, SCA, activation and differentiation markers, integrated DNA, TILDA and ICS between baseline and subsequent time points across all patients used a non-parametric Wilcoxon signed rank test. For each statistical test, a sensitivity analysis was run consisting of parallel non-parametric testing where parametric analysis was chosen, and conversely parallel parametric testing where non-parametric methodologies were used. In each analysis there was no difference in the pattern of significance or, with regards to the modeling, the direction of the coefficients. Comparisons in CA-US HIV RNA, HIV DNA, SCA, integrated DNA and ICS between pre-vorinostat, on vorinsotat and off vorinostat time periods were also performed using a Generalised Estimating Equations (GEE), using a Gaussian family structure, a link identity function and an exchangeable within-group correlation structure. A robust variance estimator was used secondary to the small sample size and the deviations from normality exhibited in the summary measure data. We further extended the GEE modeling to estimate fixed effects for assay to correct for intra-assay variability by using approximations proposed by Sutradhar and Rao [61] for GEE and further developed by Feddag et al [62]. Outcome variables were log10 transformed. All reported p values were two-tailed. A Bonferroni deflation of significance was applied for multiple comparisons, otherwise p<0.05 was considered significant. All analyses were conducted in Stata version 12 (StataCorp, College Station, Texas). Supporting Information Figure S1 Changes in latently infected cells. Latently infected cells were quantified in purified total CD4+ T-cells by measuring (A) integrated HIV DNA per million CD4+ T-cells and (B) inducible virus by the Tat Rev Inducible Limiting Dilution Assay (TILDA) as CA-MS RNA positive cells per million CD4+ T-cells. (EPS) Click here for additional data file. Figure S2 Fold changes in CA-US RNA. Fold changes in CA-US HIV RNA in CD4+ T-cells from blood is shown for each study participant in a separate graph. The mean of 4 replicates for each time point was used to calculate fold change above the baseline mean. (EPS) Click here for additional data file. Figure S3 Expression of activation markers on CD4 and CD8 T-cells from blood and rectal tissue. Flow cytometry was used to quantify CD4 and CD8+ T-cells (upper row) and expression of markers of T-cell activation in CD4+ and CD8+ T-cells from blood (middle three rows) and rectal tissue (bottom row). Black lines indicate change in activation markers of the patient who had a viral rebound (shown in Figure 4 ). Grey shaded box represents the time on vorinostat. (EPS) Click here for additional data file. Figure S4 Vorinostat induced a dynamic and prolonged change in host gene expression evident within two hours. Gene expression heatmaps of DEG using matched donor supervised analysis (n = 9) comparing baseline to multiple time points collected during the study period including at (A) two hours, (B) eight hours, (C) day one, (D) day 14 and (E) day 84. The top 50 genes were selected as differentially expressed with p<0.05. Red and blue correspond to up- and down-regulated genes respectively. (EPS) Click here for additional data file. Figure S5 Gene and pathway changes following the first and seventh doses of vorinostat. Venn diagrams show shared (A) gene expression and (B) pathways between matched donor paired samples two hours after the first dose (left hand circle) and two hours after the 7th daily dose of vorinostat (right hand circle). (C) Radial plot (Nakaya modules) illustrating selective enrichment of gene expression in PBMC cell subsets of the day 0+2 hour (red) and day 7+2 hour (green) timepoints. The radial plot shows a wedge of color that points outward (increased expression) or inward (decreased expression). Genesets induced in a specific subset were significantly enriched (adjusted p-value<0.05 denoted by *) among genes upregulated or downregulated with respect to the enrichment score (NES) between groups. All subsets with the exception of myeloid dendritic cells (MDC) were enriched in the same direction between day 0+2 hour and day 7+2 hour timepoint. pDC = plasmacytoid dendritic cells; NK = natural killer cells. (TIF) Click here for additional data file. Table S1 Adverse events possibly, probably or definitely related to vorinostat. (DOCX) Click here for additional data file. Table S2 Adverse events unrelated to vorinostat. (DOCX) Click here for additional data file. Table S3 Plasma trough concentrations of antiretroviral agents. (DOCX) Click here for additional data file. Table S4 Components of the Super Elongation Complex (SEC) are upregulated two hours following vorinostat treatment. (DOCX) Click here for additional data file. Dataset S1 Complete gene set enrichment lists. (ZIP) Click here for additional data file.
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                Author and article information

                Contributors
                a.o.pasternak@amc.uva.nl
                b.berkhout@amc.uva.nl
                Journal
                Retrovirology
                Retrovirology
                Retrovirology
                BioMed Central (London )
                1742-4690
                29 January 2018
                29 January 2018
                2018
                : 15
                : 13
                Affiliations
                ISNI 0000000404654431, GRID grid.5650.6, Laboratory of Experimental Virology, Department of Medical Microbiology, , Academic Medical Center of the University of Amsterdam, ; Meibergdreef 15, 1105 AZ Amsterdam, The Netherlands
                Author information
                http://orcid.org/0000-0002-4097-4251
                Article
                397
                10.1186/s12977-018-0397-2
                5789533
                29378657
                4c2f0417-e648-4d46-af06-5134215f35d3
                © The Author(s) 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 24 November 2017
                : 16 January 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100007553, Aids Fonds;
                Award ID: 2012025
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                © The Author(s) 2018

                Microbiology & Virology
                cell-associated hiv rna,virological biomarker,hiv persistence,hiv reservoir,antiretroviral therapy,hiv cure

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