NK response correlates with HIV decrease in pegylated-IFN-α2a-treated ART-suppressed subjects

Abstract

We previously reported that pegylated interferon-α−2a (Peg-IFN-α2a) added to antiretroviral therapy (ART)-suppressed HIV-infected subjects resulted in plasma HIV control and integrated HIV DNA decrease. We now evaluated whether innate NK cell activity or PBMC transcriptional profiles were associated with decreases in HIV measures. Human peripheral blood was analyzed prior to Peg-IFN-α2a administration (ART, baseline), after 5 weeks of ART+Peg-IFN-α2a, and after 12 weeks of Peg-IFN-α2a monotherapy (primary endpoint). After 5 weeks of ART+Peg-IFN-α2a, immune subsets frequency was preserved, and induction of IFN-stimulated gene (ISGs) was noted in all subjects except for a subset where the lack of ISGs induction was associated with increased expression of miRNAs. Viral control during Peg-IFN-α2a monotherapy was associated with a) higher levels of NK cell activity and IFN-γ-induced protein 10 (IP-10) on ART (pre-immunotherapy), and b) downmodulation of NK cell KIR2DL1 and KIR2DL2/DL3 expression, transcriptional enrichment of expression of genes associated with NK cells in HIV controller subjects, and higher ex vivo IFN-α-induced NK cytotoxicity after 5 weeks of ART+Peg-IFN-α2a. Integrated HIV DNA decline after immunotherapy was also associated with gene expression patterns indicative of cell-mediated activation and NK cytotoxicity. Overall, an increase in innate activity and NK cell cytotoxicity were identified as correlates of Peg-IFN-α2a-mediated HIV control.

Introduction

Interferon-α (IFN-α) is a type-I IFN produced by leukocytes as part of the host’s Toll-like receptor (TLR)-mediated antiviral response (1). Type-I IFNs I modulate cellular antiviral immune responses in vivo either directly by activation of antiviral host restriction factors (2) or indirectly via stimulation of innate natural killer (NK) cell-mediated responses (3-6). Clinical trials with IFN-α support modulation of cell-mediated responses as an outcome of immunotherapy leading to increased perforin expression in NK and CD8⁺ T cells (7, 8), increases in CD16⁺CD56⁺ NK cell numbers (9), and activation of CD56⁺ NK cells (10). Activation of innate host restriction factors and NK responses have been associated with control of human immunodeficiency virus (HIV) and lysis of autologous HIV-infected CD4⁺ T cell targets ex vivo (11, 12) suggesting that IFN-α immunotherapy may activate similar mechanisms in vivo to control HIV infection.

Several human clinical trials where IFN-α immunotherapy was administered without antiretroviral therapy (ART) in HIV-infected viremic individuals support a predominantly anti-HIV effect without advancement of disease progression (13-23). In the absence of ART, temporal reductions of HIV plasma viral load following administration of pegylated IFN-α2a (Peg-IFN-α2a) were also associated with activation of baseline levels of host restriction factors indicating a role for IFN-induced gene induction as part of the anti-HIV mechanism of action (13). Short-term treatment with Peg-IFN-α2a added to ART in acute infection also lead to a decrease in HIV reservoirs (24).

We and others have tested the potential for Peg-IFN-α2a immunotherapy to reduce the size of the HIV reservoir (measured as the integrated HIV DNA levels) in chronic ART-suppressed HIV or hepatitis C virus (HCV)/HIV-infected subjects (25-27). Together, these studies suggest that IFN-α can suppress plasma viral load and act to decrease CD4⁺ T cell integrated HIV DNA levels, as reported in our clinical study (25). However, the mechanisms underlying the in vivo responses described for our clinical study remain undefined. We now describe innate activity, NK cytotoxicity and gene expression as correlates of retained plasma viral load suppression and CD4⁺ T cell integrated HIV DNA reduction after ART interruption and Peg-IFN-α2a monotherapy.

Subjects, materials and methods

Participants.

Fresh whole blood samples were obtained from 20 HIV-infected subjects on suppressive ART [with >6 months at ≥400 CD4⁺ T cells/mm³ (nadir ≥200 cells/mm³) and undetectable HIV viral load measurement of <50 copies/ml at screening] participating in an open label longitudinal study () investigating the antiviral activity of 90 or 180 μg/week Peg-IFN-α2a when administered with ART for 5 weeks, followed by ART interruption and Peg-IFN-α2a monotherapy for 12 weeks as primary endpoint. The details and clinical findings of this study have been published elsewhere (25).

Briefly, and as described in our prior publication (25), 9 of 20 subjects maintained plasma HIV viral load <400 copies/ml by the 12-week study primary endpoint (primary endpoint responders, R). Of the remaining 11/20 subjects, 7 exhibited virologic failure (viral load >400 copies/ml) prior to the 12-week study primary endpoint, and 4 discontinued the study because of moderate depression scores (n=3), and grade 3 neutropenia (n=1); according to study design all of these subjects were considered to have achieved primary endpoint failure (primary endpoint non-responders, N). In addition, and as described in our prior publication (25), responders at primary endpoint had a significant drop in the levels of integrated HIV DNA in peripheral blood mononuclear cells (PBMC) as detected by Alu-Gag PCR between baseline (ART) and primary endpoint (Peg-IFN-α2a). Levels of integrated viral DNA did not change in non-responders.

In the current study, results are described for the 20 subjects participating in over three time-points. All available peripheral blood samples were used as obtained prior to Peg-IFN-α2a initiation (baseline, ART only), after 5 weeks of ART+Peg-IFN-α2a, and after 12 weeks of Peg-IFN-α2a monotherapy (primary endpoint).

Ethics statement.

Written informed consent was obtained from patients according to the directives of the institutional review boards at the Wistar Institute, University of Pennsylvania, Philadelphia FIGHT, and Drexel University. All study participants were adults.

Sample usage.

Fresh blood samples were used for clinical assessment, immune subset characterization by flow-cytometry, and for plasma and PBMC isolation. PBMC were isolated as previously described, using a standard Ficoll-hypaque density gradient centrifugation (28). Plasma was cryopreserved and used for cytokine assessment. All available PBMC were either used fresh for ex vivo assessment of constitutive and/or IFN-α-induced innate functionality [flow cytometry-based assays for signal transducer and activator of transcription 1 (STAT-1) phosphorylation, CD107a expression, and standard ⁵¹Cr release assay], or after cryopreservation for host gene expression studies in PBMCs.

Phenotypic characterization of innate and adaptive cell subsets.

Immunophenotypic characterization of NK, dendritic cells (DC), and T cell subsets was performed by same day whole blood 5-color staining as previously described (28, 29) using the following combinations of directly fluorochrome-conjugated anti-human cell surface monoclonal antibodies (Abs): a) CD56-fluorescein isothiocyanate (FITC), CD3-PerCP-Cy5.5, CD94-allophycocyanin (APC), CD16-APC-Cy7, b) CD56-FITC, CD25-phycoerythrin (PE), CD3-PerCP-Cy5.5, HLA-DR-APC, CD16-APC-Cy7, c) CD158a (killer cell immunoglobulin like receptor, two Ig domains and long cytoplasmic tail 1, KIR2DL1)-FITC, CD158b (killer cell immunoglobulin-like receptor, two domains, long cytoplasmic tail 2/3, KIR2DL2/DL3)-PE, CD3-PerCP-Cy5.5, CD94-APC, CD56-APC-Cy7, d) CD1c (blood dendritic cell antigen 1, BDCA1)-FITC, CD303 (BDCA2)-PE, CD304 (BDCA4)-PE, CD11c-PerCP-Cy5.5, CD197 (C-C chemokine receptor type 7, CCR7)-APC, CD19-APC-Cy7, e) CD95-FITC, CD25-PE, CD3-PerCP-Cy5.5, CD38-APC, CD4-APC-Cy7, and f) CD3-PerCP-Cy5.5, HLA-DR-APC, CD4-APC-Cy7. Isotypes used: immunoglobulin G1k (IgG1k)-FITC, IgG1k-PE, IgG1k-PerCP-Cy5.5, IgG1k-APC, IgG1k-APC-Cy7. All antibodies were from Becton Dickinson (BD) Biosciences (San Diego, CA), except BDCA1-FITC, BDCA2-PE which were from Miltenyi Biotec (San Diego, CA). Stainings “a-c” allowed for the assessment of markers of activation/inhibition (HLA-DR, CD94, CD25, KIRs) on NK cell subsets (30, 31) identified as: i) CD3⁻CD56^bright, CD3⁻CD56^dim, ± CD16, and ii) CD3⁻CD56⁻CD16⁺, CD3⁻CD56⁺CD16⁺, and CD3⁻CD56⁺CD16⁻. Staining “d” allowed for the assessment of maturation markers (CCR7) on DC subsets (32) {identified as: BDCA2⁺BDCA4⁺ [plasmacytoid DC (PDC)], and CD19⁻BDCA1⁺CD11c⁺ [myeloid DC (MDC)]}. Finally, stainings “e” and “f” allowed for the assessment of markers of activation (i.e. CD38, CD95, CD25, HLA-DR) on T cells (CD3⁺CD4⁺, CD3⁺CD4⁻). Cells were analyzed on LSRII cytometer (BD Biosciences) by collecting >200000 events and data were analyzed using FloJo software (Version 8.8.4, Tree Star, Ashland, OR). Gating was originally done on singlets, and then on “live lymphocyte” (for NK and T cells) or “all live cell” (for DC) gates defined by size and granularity in forward scatter (FSC) and side scatter (SSC). Thresholds were set by isotype-matched negative controls and unstained cells. Results were expressed as mean fluorescent intensity (MFI), % and cells/mm³.

Assessment of the in vitro role of IFN-α on STAT-1 phosphorylation within PBMC cell subsets.

Freshly isolated PBMC (2×10⁶/ml) were stained for: a) CD3-FITC, CD14-FITC, CD19-APC, CD20-APC, CD16-Pacific Blue, CD56-PECy7, b) CD14-FITC, BDCA2-APC, BDCA4-APC, CD3-Pacific Blue, or c) corresponding isotypes (IgG1k-FITC, IgG2ak-FITC, IgG1-APC, IgG1k-Pacific Blue, IgG1k-PECy7) for 30min at 4°C, washed with 1xPBS at 1500rpm for 5min and re-suspended in warm 1xPBS. PBMCs were then treated for 10 min at 37°C with medium, IFN-α (5000U/ml, PBL, Piscataway, NJ) or IFN-γ (10ng/ml, R&D Systems, Minneapolis, MN). Cells were then fixed with paraformaldehyde (5% final concentration) for 10min at 37°C, washed and permeabilized with PhosFlow buffer (BD Biosciences) for 30min at room temperature (RT). PBMCs were then washed in FACS washing buffer at 2200rpm for 10min, stained with an Ab against p-STAT-1 (p-STAT-1-PE) or corresponding isotype IgG2ak-PE for 1hr at RT, washed with FACS washing buffer and analyzed in the Cyan Cytometer as described above. Staining “a” allowed for the assessment of NK cell subsets (identified as: Lin3⁻CD56⁺CD16⁺, Lin3⁻CD56⁺CD16⁻, or Lin3⁻CD56⁻CD16⁺, with Lin3 consisting of CD3, CD14, CD19, and CD20), while staining “b” allowed for the identification of monocytes (identified as CD3⁻CD14⁺), and PDC (identified as: CD3⁻CD14⁻BDCA2⁺BDCA4⁺). STAT-1 phosphorylation was expressed as MFI of p-STAT-1 (constitutive or in vitro IFN-α or IFN-γ-induced defined as: in vitro IFN-α or IFN-γ-induced MFI of p-STAT-1/constitutive MFI of p-STAT-1) for all the above described cell subsets. All antibodies were from BD Biosciences except BDCA2-APC, BDCA4-APC and IgG1-APC which were purchased from Miltenyi Biotec.

Assessment of constitutive CD107a expression on NK cells.

Fresh PBMC (1×10⁶) were incubated with CD107a-PE or corresponding isotype (IgG1k-PE) for 4hrs at 37 ºC, blocked with 10% serum for 10min at RT, and stained for 30 min on ice with CD3-FITC, CD14-FITC, CD19-FITC, CD20-FITC, CD56-PE-Cy7, CD16-Pacific Blue or corresponding isotypes IgG1k-FITC, IgG2ak-FITC, IgG1k-PE-Cy7, IgG1k-Pacific Blue. All Abs were from BD Biosciences. PBMC were washed with 2ml FACS washing buffer at 1500 rpm at 4°C for 5min, lysed with 1ml BD FACS Lyse for 10min at 37°C, washed again with 2ml FACS washing buffer and re-suspended in 100μl FACS washing buffer. Cells were analyzed on Cyan cytometer as described above. This staining allowed for the assessment of markers of CD107a (marker of degranulation) and NKp46 (activating receptor) on NK cell subsets (identified as: Lin3⁻CD56^bright, Lin3⁻CD56^dim ± CD16, with Lin3 consisting of CD3, CD14, CD19, and CD20).

⁵¹Cr release assay for NK cytotoxicity.

NK function was assessed measuring constitutive and IFN-α (5000U/ml)-induced NK cell-mediated cytotoxicity by standard ⁵¹Cr release assay, as previously described, using fresh PBMC preparations as effectors cells against the tumor derived erythroblastoid cell line K562 (29, 33). Briefly, viable fresh PBMC preparations from whole blood [effector cells, (E)] were treated for 18 hrs at 37C° with medium alone or IFN-α (5000 Units/ml, PBL). Erythroblastoid major histocompatibility complex (MHC)-null K562 cells, which served as targets (T), were labeled with Na₂⁵¹CrO₄ (~ 50μCi) for 1.30hr at 37°C, washed and re-suspended at a concentration of 1×10⁵ cells/ml in culture medium. Effectors and labeled-K562 targets were cultured in triplicate to yield the desired E: T ratios in 0.2 ml volume (usually 50:1, 25:1, 12.5:1, and 6.25:1) in round bottomed 96-well plates and incubated for 4hrs. Percent lysis was determined by the following formula: [(experimental counts-spontaneous released counts) / (total counts-spontaneous released counts)] × 100. Results were expressed as area under the curve (AUC) for E:T ratios of 50:1, 25:1, 12.5:1 and 6.25:1 for both constitutive and in vitro IFN-α-induced NK function.

Cytokines assessment in plasma.

Cryopreserved plasma was used for cytokine profile definition by using the Human Cytokine 30-Plex Panel (Invitrogen’ Multiple Bead Immunoassay kit) with the Luminex 100 or 200 dual laser detection system. This panel allowed for the quantitative determination of epidermal growth factor (EGF), eotaxin, basic fibroblast growth factor (FGF-basic), granulocyte colony-stimulating factor (GCSF), granulocyte-macrophage colony-stimulating factor (GM-CSF), hepatocyte growth factor (HGF), IFN-α, IFN-γ, interleukin 1 receptor a (IL-1ra), IL-1β, IL-2, IL-2r, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12 (p40/p70), IL-13, IL-15, IL-17, IP-10, monocyte chemoattractant protein 1 (MCP-1), monokine induced by IFN-γ (MIG), macrophage inflammatory protein 1α (MIP-1α), MIP-1β, regulated on activation normal T cell expressed and secreted (RANTES), tumor necrosis factor α (TNF-α) and vascular endothelial growth factor (VEGF).

Microarray.

Isolation of total RNA and DNA from cryo-preserved PBMCs was performed using Tri-reagent (Sigma Aldrich, St Louis, MO) according to the manufacturer’s instructions. For the gene expression microarrays, amplified cRNA was generated from 100ng RNA using the TargetAmp Nano-g Biotin-aRNA Labeling Kit (Epicentre, Madison WI), and then hybridized to the HumanHT-12 v4 Expression BeadChips (Illumina, San Diego, CA). An additional 200ng of RNA were used for miRNA assays using TaqMan OpenArray Human miRNA Panels (Life technologies, Grand Island, NY) with Megaplex RT and PreAmp Human Pools Set v3.0 (Life technologies), where 100ng were used for each of the two primers, A and B.

Raw gene expression microarray data were quantile normalized and log₂ scaled. Non-informative probes, either expressed at background level or showing <1.2 fold change between all samples pairs, were removed prior to further analysis. For miRNA data pre-processing, cycle threshold (Ct) values were converted to expression levels. The small nucleolar RNAs (RNU), RNU44 and RNU48, were used as endogenous controls (housekeeping genes) to normalize the expression levels of the samples and compute relative amounts for each miRNA (ΔCt). First, the average Ct of the RNUs (RNUavg) was calculated. Ct values were then restricted to <24, as suggested by the manufacturer, and the maximum ΔCt value was designated as ΔCt24 (where ΔCt24 = 24 - RNUavg). The ΔCt values were then converted to absolute expression levels by calculating 2^{ΔCt24 – ΔCt}. ΔCt values exceeding ΔCt24 were considered unreliable and were floored to the ΔCt24 value for the comparative analyses. miRNAs with ΔCt values at the ΔCt24 level across all samples were filtered out. Expression levels were log₂ scaled for further analysis.

Microarray data analysis.

Expression level comparisons between ART and 5 weeks of ART+Peg-IFN-α2a was done using paired t-test with correction for multiple testing done according to Storey et. al. (34). Genes that passed false discovery rate (FDR)<5% were called significant and used for hierarchical clustering of samples with standardized Euclidean distance and average linkage. Significance of difference of changes due to 5 weeks combined administration of ART+Peg-IFN-α2a between primary endpoint responders (R) and primary endpoint non-responders (N) was calculated using unpaired t-test, and genes that passed FDR<5% and miRNAs with p<0.05 were considered. QIAGEN’s IPA®, (QIAGEN Redwood City,www.qiagen.com/ingenuity) was used for information linking miRNAs and their targets with experimentally confirmed and predicted by TargetScan with high/moderate confidence hits were reported.

Genes that were significantly detected above the microarray background were annotated using the Interferome database (35) and those known to be stimulated at least 10 fold by IFN-α were considered as IFN-stimulated genes (ISGs). The top 30 of those ISGs was then used to illustrate differences in expression amongst ISG responders/primary endpoint responders (RR), ISG responders/primary endpoint non-responders (RN), and ISG non-responders/primary endpoint non-responders (NN).

Unpaired t-test was used for the assessment of a gene signature on ART that can distinguish, primary endpoint responders (R) from primary endpoint non-responders (N). Principal component analysis was then performed using expression of genes that passed p<0.001 threshold.

Association of gene expression changes after 5 weeks of ART+Peg-IFN-α2a, n=11) and by primary endpoint (Peg-IFN-α2a, n=6) with integrated HIV DNA changes was tested using Pearson correlation, and genes with p<0.05 were considered for enrichment analysis. Enrichment analysis was done using Ingenuity pathway analysis (IPA) and significantly enriched biological functions that passed a p-value<0.001 threshold, were considered. Functions with predicted activation (z-score>1 calculated by IPA based on direction of correlation of member genes) were then manually categorized into major classes with maximum z-score assigned to each of the class. Expression of the genes involved in the functional categories was shown on an expression heatmap with shared genes that belong to multiple categories illustrated within a category with the fewest genes.

In addition, gene set enrichment analysis (GSEA) was also performed on the genes ranked by significance and direction of change between ISG responders/primary endpoint responders (RR) and ISG non-responders/primary endpoint non-responders (NN); baseline expression of primary endpoint responders (R) and primary endpoint non-responders (N); as well as correlation of week 5 and week 8 changes with HIV DNA changes. Results that passed FDR<15% were considered significant.

Analysis of the microarray data was done using MATLAB 7.10.0. The data were submitted to gene expression omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE107549.

Statistical Analysis.

Phenotypic and functional data are reported as means with standard deviation (Stdev). The effect of 5 weeks of ART+Peg-IFN-α2a on study variables was assessed by paired t tests, and two-sided p values <0.05 were considered statistically significant. Comparisons between groups were performed by non-paired t tests with a cutoff of p<0.05. Analysis was done by using R.2.5.1.

Results

Study schema and patient’s groups.

Study schema, time-points used for analysis and response groups definitions used in the current study are shown in Fig 1. The number of patients used in the current study for analysis of the different sets of data (immune variables, integrated DNA, gene expression) were dependent on samples availability, and is shown along with the different response groups used in the current study in Supplemental Table 1.

Figure 1.

Study time points used for sample analysis. (A) Time points of the open label longitudinal study () used for sample analysis are indicated by red arrows. ART is represented by yellow boxes, Peg-IFN-α2a by green boxes and no ART by white boxes. Primary endpoint responders (R) and primary endpoint non-responders (N) were defined according to study protocol. (B) Response groups definitions used in the current study.

Innate cell subsets and inflammatory proteins changes after 5 weeks of dual treatment with Peg-IFN-α2a and ART.

Consistent with the expected leukopenic effects of Peg-IFN-α2a immunotherapy, 5 weeks of ART+Peg-IFN-α2a, resulted in reductions in whole blood cell count, neutrophil count and CD4⁺ T cell count (p=0.0012, p=0.0074, p=0.0009 respectively, Fig 2A-C). The reduction in CD4⁺ T cell count did not reflect a selective loss of CD4⁺ T cell percentage (%) within PBMC (Fig 2D). As with CD4⁺ T cell frequencies, no change was detected in T cell activation, or in the major NK, or DC subsets (Fig 2E-I). However, a clear reduction in STAT-1 phosphorylation was noted in all leukocytes (NK, DC, monocytes) after immunotherapy when challenged ex vivo with exogenous IFN-α (Supplemental Fig 1A-B), in contrast to a retention of IFN-γ-induced STAT-1 phosphorylation observed in monocytes and PDC (Supplemental Fig 1B). With regards to soluble plasma cytokine changes, we detected an increase in plasma levels of IL-8 (p=0.0093) and MCP-1 (p=0.0005) after 5 weeks of ART+Peg-IFN-α2a (Fig 2J-K). None of the significant changes detected in clinical parameters, leukocyte cell subset distribution, or ex vivo IFN-α-induced STAT-1 phosphorylation after 5 weeks of ART+Peg-IFN-α2a were associated with HIV plasma viral load control or changes in integrated proviral DNA as measured between primary endpoint responders (R) and non-responders (N) (data not shown).

Figure 2.

Changes in clinical and immune variables after 5 weeks of combined administration of ART+Peg-IFN-α2a. Whole blood measures are shown at baseline (ART) and 5 weeks after Peg-IFN-α2a and ART. Cell count (cells/mm³) is shown for (A) Total whole blood cells, (B) neutrophils, (C) CD4⁺ T cells. Lymphocyte frequencies are shown for: (D) CD3⁺CD4⁺, (E) CD3⁺CD4⁺CD38⁺, and (F) CD3⁺CD4⁻CD38⁺. Frequencies of innate cell subsets are shown for (G) CD3⁻CD56^dimCD16⁺, (H) CD19⁻BDCA1⁺CD11c⁺, and (I) CD19⁻BDCA2⁺BDCA4⁺. Plasma levels are shown for (J) IL-8, and (K) MCP-1. Available data are shown for study subjects together with the mean of distribution and significant (<0.05) p values.

Gene expression analysis identifies gene signatures that inform clinical outcomes.

Gene expression [mRNA and micro RNA (miRNAs)] was analyzed in PBMC samples obtained on ART and after 5 weeks of ART+Peg-IFN-α2a. A total of 1436 probes were found to be significantly differentially expressed between these two time-points (FDR<5%). Hierarchical clustering of the samples based on these 1436 probes separated them into two main arms based on the presence or absence of immunotherapy (i.e. ART versus 5 weeks of ART+Peg-IFN-α2a, Fig 3A). Overall, there was no separation between primary endpoint responders (R) and non-responders (N) with regards to overall gene expression within ART or after 5 weeks of ART+Peg-IFN-α2a, although in 4 subjects (008, 009, 013, and 029: indicated with a star in Fig 3A) the samples after 5 weeks of ART+Peg-IFN-α2a clustered with their corresponding samples on ART, suggesting that these 4 subjects had no detectable gene modulation after ART+Peg-IFN-α2a treatment.

Figure 3.

Gene profiles after Peg-IFN-α2a immunotherapy between primary endpoint responders (R) and primary endpoint non-responders (N). (A) Hierarchical clustering of all subjects before and after 5 weeks of Peg-IFN-α2a and ART using the 1436 probes that were significantly differentially expressed between these time points at FDR<5%. Star (*) and red fonts indicate subjects with gene expression profiles that are similar on ART and after 5 weeks of ART+ Peg-IFN-α2a, suggesting that the overall IFN response is muted in this subset of subjects. Green boxes illustrate responders as defined by primary endpoint outcome (R), while orange boxes illustrate non-responders as defined by primary endpoint outcome (N). (B) Gene expression fold changes for the top 30 known ISGs after 5 weeks of ART+Peg-IFN-α2a (i.e. gene expression on ART+Peg-IFN-α2a / gene expression on ART) are shown for RRs, RNs and NNs. (C) Heatmap of expression changes for the 77 mRNA probes that were significantly more upregulated (FDR<5%) in RR vs NN patients. 12 miRNA significantly more upregulated in NN vs RR patients (p<0.05) are shown along with their predicted or experimentally confirmed target genes (purple boxes). All genes targeted by at least one miRNA are highlighted. Codes used: ISG responders/primary endpoint responders (RR) shown with black shapes; ISG responders/primary endpoint non-responders (RN) shown with grey shapes; ISG non-responders/primary endpoint non-responders (NN) shown with white shapes. Genes annotated by number are found together with miRNA lists in the same order in Supplemental Table 2. (D) GSEA analysis investigating whether other relevant gene sets are differentially induced between the RR and NN groups, using a gene set that was recently reported to characterize a state of activation of NK cells from HIV-1 controllers (HIC).

To further investigate the lack of response observed in these 4 subjects, the average expression fold change after 5 weeks of ART+Peg-IFN-α2a treatment (i.e. ART+Peg-IFN-α2a ISG expression / ART ISG expression) was calculated for the top 30 known IFN-α stimulated genes (shown in other studies to be stimulated at least 10 fold, as shown in Interferome database (35).The following groups were defined based on ISG expression change and primary endpoint outcome: a) subjects who showed a response (as defined by modulation of ISG) to 5 weeks of ART+Peg-IFN-α2a treatment and a response at primary endpoint (ISG responders/primary endpoint responders, RR), b) subjects who showed a response (as defined by modulation of ISG) to 5 weeks of ART+Peg-IFN-α−2a treatment but did not show a response at primary endpoint (ISG responders/primary endpoint non-responders; RN), and c) subjects who did not show a response (as defined by modulation of ISGs) to 5 weeks of ART+Peg-IFN-α−2a treatment and did not show a response at primary endpoint (ISG non-responders/primary endpoint non-responders; NN). Results clearly showed that induction of ISG expression alone was not able to segregate persons that controlled viral load under monotherapy at primary endpoint, yet the lack of ISGs induction was only observed within persons failing to control plasma viral load at primary endpoint (Fig 3B).

Apart from the 30 gene ISG induction criteria, a comparison of the changes in the expression levels for genes and miRNAs after 5 weeks of ART+Peg-IFN-α2a treatment between the RR and RN or between the RR and NN groups was performed. No significant differences in the response were found between the RR and RN groups (FDR>95% for all genes, only 5 genes with nominal p<0.001). In contrast, comparison between the RR and NN groups identified 111 gene probes with significant differences (FDR<5%, Supplemental Table 2) with the majority (85 probes corresponding to 77 unique genes) being upregulated after 5 weeks of ART+Peg-IFN-α2a treatment in the RR group (Fig 3C). Of the 77 genes found to be significantly upregulated after 5 weeks of ART+Peg-IFN-α2a treatment in the RR group when compared to the NN group, 46 genes (60%) were previously described to be stimulated at least 10 fold by IFN-α based on information from the Interferome database (35), representing an enrichment of 34 fold over the total prevalence of such genes (1.7%) amongst all the genes detected by microarrays.

In addition, 12 significantly differentially changed miRNAs (nominal p<0.05) were found to be more upregulated in the NN group with 9 of them having prior evidence of targeting at least one gene from the upregulated list in the RR group (noted below). A combined heatmap for the 77 genes and the 12 miRNAs is shown in Fig 3C with all predicted or experimentally confirmed target genes for the miRNAs highlighted. Among the identified matches between the expression of a target gene and the putative regulation by the corresponding miRNA, high confidence matches were noted for IFN-induced protein 44 like (IFI44L) (36), Ral guanine nucleotide dissociation stimulator like 1 (RGL1) (37), plant homeodomain finger protein 11 (PHF11) (38) and mitochondrial ribosomal protein L17 (MRPL17) (39) and miR-19b (40, 41), as well as for membrane associated ring-CH-type finger 1 (MARCH1) (42) and miR-155 (43, 44), and IFN alpha and beta receptor subunit 1 (IFNAR1) (45) and miR-370 (43, 46). In addition, experimentally confirmed matches were found for TLR7 (1) and miR17 (47, 48), IFN-induced protein with tetratricopeptide repeats 5 (IFIT5) (49, 50) and let7e (43, 51), and ankyrin repeat and FYVE domain containing 1 (ANKFY1) (52, 53) and miR-155 (43).

We also tested by GSEA whether other relevant gene sets are differentially induced between the RR and NN groups, including a gene set that was recently reported to characterize a state of activation of NK cells from HIV-1 controllers (HIC) (54). We found that the genes that were upregulated in the RR as compared to the NN group were significantly associated with the NK genes reported to be upregulated in HIC patients when compared to progressor patients [normalized enrichment score (NES)=1.6, p=0.0062, FDR=0.89%, (Fig 3D)]. Briefly, the 21 genes detected to be enriched in RR when compared to NN were: complement C3a receptor 1 (C3AR1), MX dynamin like GTPase 2 (MX2), placenta specific 8 (PLAC8), hematopoietic SH2 domain containing (HSH2D), tripartite motif containing 22 (TRIM22), IFI44L, nucleic acid binding protein 1 (NABP1), GTPase, IMAP family member 8 (GIMAP8), sphingosine-1-phosphate receptor 1 (S1PR1), zinc finger protein 143 (ZNF143), cytokine inducible SH2 containing protein (CISH), DExD/H-box helicase 58 (DDX58), tetratricopeptide repeat and ankyrin repeat containing 1 (TRANK1), GIMAP4, C-X3-C motif chemokine receptor 1 (CX3CR1), GIMAP6, GIMAP7, tubulin delta 1 (TUBD1), grancalcin (GCA), tRNA nucleotidyl transferase 1 (TRNT1), and ZNF518A. Of these genes, 4 were significantly upregulated in RR group when compared to NN group [IFI44L (P=0.02), MX2 (p=0.02), PLAC8 (p=0.04) and C3AR1 (p=0.01)].

In addition, analysis of the baseline pre-immunotherapy gene expression between groups controlling or not plasma viral load upon primary endpoint identified a 30 gene signature with significant differential expression (p<0.001, Supplemental Fig 2A). Principal component analysis using the expression of these genes confirmed a separation of the RR group using pre-immunotherapy gene expression alone (Supplemental Fig 2B). Baseline gene expression differentiating primary endpoint responders (R) from primary endpoint non-responders (N) was also explored by GSEA. Results revealed one gene set [GSE18791] (55) to be significant in association with genes that were more upregulated in R when compared to N (NES=2.1, p<0.001, FDR=4.7%). This gene set was identified as a regulatory network underlying the antiviral state transition during the first 18 hours following the in vitro infection of dendritic cells with Newcastle Disease virus (55). Interestingly, we also identified an association of genes that were upregulated in R compared to N with the gene set of HALLMARK_INTERFERON_ALPHA_RESPONSE, (NES=1.87, p<0.001) but with FDR=43.8%.

In summary, microarray data analysis allowed for the identification of gene signatures at baseline (on ART) and after 5 weeks of ART+Peg-IFN-α2a with regards to responder and non-responder outcomes.

Enrichment in expression of genes that are associated with activation of NK and cell-mediated responses in subjects with decreased levels of integrated HIV DNA following Peg-IFN-α2a immunotherapy.

Results from our study (25) indicated that primary endpoint responders (R) when compared to primary endpoint non-responders (N) exhibited a significant drop in the levels of integrated HIV DNA in PBMC between baseline (ART) and primary endpoint (Peg-IFN-α2a). We evaluated whether these changes were associated with specific gene expression patterns. A total of 1260 probes (992 unique genes) identified after 5 weeks of ART+Peg-IFN-α2a and 880 probes (703 unique genes) identified at primary endpoint (Peg-IFN-α2a) were analyzed for functional enrichments using IPA. Significantly enriched functions (p<0.001) were then manually categorized into major classes. Only enriched categories with predicted activation state (z-score>1 calculated by IPA based on direction of correlation of member genes) were considered. As demonstrated in Fig 4A, and Tables I-II, this analysis showed that subjects who experienced reduction in integrated HIV DNA exhibited an enrichment in genes associated with leukocyte proliferation and survival, leukocyte chemotaxis and recruitment, leukocyte activation, cytotoxicity/cell mediated response, natural killer cytotoxicity and antibody-dependent cytotoxicity (ADCC). In contrast, subjects without decreases in integrated HIV DNA showed enrichment for genes associated with leukocyte cell death and cancer/neoplasia. No enrichment for ISG gene groupings were detected in association with change in integrated HIV levels. Importantly, a similar pattern in gene enrichment was observed in data from two independent time-points between subjects analyzed either after 5 weeks of ART+Peg-IFN-α2a or during Peg-IFN-α2a monotherapy (Fig 4, and Tables I-II showing same genes across both time-points). Interestingly, GSEA gene ontology analysis after 5 weeks of ART+Peg-IFN-α2a or during Peg-IFN-α2a monotherapy further supported an association between immune responses and reduction in HIV. Briefly, subjects who experienced reduction in integrated HIV DNA had an enrichment in genes associated with immune functions such as NK cell mediated immunity (NES=−1.68, p=0.01, FDR=6%), regulation of leukocyte mediated cytotoxicity (NES=−1.46, p=0.04, FDR=18%) and T cell proliferation (NES=−1.93, p=0.001, FDR=0.4%). In addition, GSEA REACTOME pathways analysis showed an enrichment in these subjects of genes associated with IFN signaling (NES=−2.21, p<0.001, FDR=0.01%), HIV life cycle (NES=−1.85, p<0.001, FDR=2%), antiviral mechanism of IFN-stimulated genes (NES=−1.56, p=0.01, FDR=11%), and apoptosis (NES=−2.1, p<0.001, FDR=0.07%).

Figure 4.

Functional gene expression categories in association with change in integrated HIV DNA on CD4⁺ T cells. (A) Heatmap of the genes with changes at 5 weeks of ART+Peg-IFN-α2a (at which time ART was interrupted; top), and after 12 weeks of Peg-IFN-α2a immunotherapy (bottom) that were significantly associated with HIV DNA changes per circulating CD4⁺ T cells. Together with these genes, functional categories significantly enriched among those genes are also shown. Enriched category color indicates predicted category activation score (z-score by IPA) with orange for functions predicted to be more active in primary endpoint responders (R) or purple for functions more active in primary endpoint non-responders (N). (B) Expression of genes from each functional category that were significantly associated with HIV DNA changes per circulating CD4⁺ T cells at 5 weeks of ART+Peg-IFN-α2a, and after 12 weeks of Peg-IFN-α2a immunotherapy were averaged and plotted for every patient. Methods used for this analysis are described in “Subject, material and methods” section. Briefly, for changes after 5 weeks of ART+Peg-IFN-α2a or at primary endpoint (Peg-IFN-α2a) versus ART, in HIV DNA per circulating CD4⁺ T cells: Genes were selected to have significant Pearson correlation (nominal p<0.05) with log2 ART+Peg-IFN-α2a / ART [or primary endpoint (Peg-IFN-α2a) / ART] ratio of the HIV DNA measurement. 1260 probes (992 unique genes) for visit ART+Peg-IFN-α2a and 880 probes (703 unique genes) for primary endpoint (Peg-IFN-α2a) were analyzed for functional enrichments using IPA and significantly enriched functions (p<10⁻³) were then manually categorized into major classes. Expression of the genes involved in the functional categories then shown on a heatmap with shared genes that belonged to multiple categories shown within the category with the fewest genes. Only enriched categories with predicted activation (z-score>1 calculated by IPA based on direction of correlation of member genes) were considered.

Table I.

Functional categories by Ingenuity pathway analysis that were significantly enriched among genes with changes at 5 weeks of ART+Peg-IFN-α2a when compared to ART.

#	Function group	N	Top 10 positive genes	Top 10 negative genes	IPA function	P	Z
1	Cell viability of thymocytes	6	PTK2B, MYB	FASLG, CD8A, CBFB, CD3E	cell viability of thymocytes	0.0062	2.19
2	Leukocyte cell death	104	HSF1, ODC1, CAPN1, SLC11A2, MAP2K5, XAB2, NAA38, TBCCD1, ALS2, GMDS	CSNK1A1, CD226, FBXO5, MSH2, CD70, GSTM1, BUB3, HNRNPK, ZNF626, CRBN	cell death of natural killer cells	0.0103	1.40
					cell death of tumor cell lines	0.0002	1.53
					apoptosis of lymphoid organ	0.0082	1.59
					death of embryo	0.0082	1.91
					sensitivity of cells	0.0107	2.74
3	B cell neoplasm	43	TCIRG1, BMF, PARL, ABL1, NF1, BSCL2, STEAP3, DDB1, MYB, ETV6	MSH2, CD70, CRBN, RASSF1, RBM4, CCND2, CD244, CD80, CD52, NUP98	B-cell lymphoproliferative disorder	0.0003	1.72
					B-cell non-Hodgkin's disease	0.0127	1.72
					B-cell neoplasm	0.0092	1.94
					quantity of B lymphocytes	0.017	0.32
4	Cancer	24	RAB44, RCC2, HSF1, ZNF839, FZD1, POU2F1, TRPC4AP, CRIP2, GPSM1, CDK5RAP3	PHRF1, SLC7A6OS, ORC4, CSNK1A1, GNPTAB, CD226, COX17, HINFP, WDR82, TMEM44	breast or colorectal cancer	0.0047	1.40
					cancer	6E-07	1.70
					digestive organ tumor	4E-06	1.71
					digestive system cancer	3E-05	1.76
					adenocarcinoma	5E-07	1.84
					malignant solid tumor	9E-07	0.62
					gastrointestinal tract cancer	0.0004	0.94
					intestinal tumor	8E-05	1.00
					colorectal cancer	0.0148	1.04
					Gastrointestinal Tract Cancer and Tumors	9E-05	1.07
					epithelial cancer	2E-07	1.31
					intestinal cancer	0.0002	1.31
					abdominal cancer	7E-07	1.35
					organization of actin cytoskeleton	0.0054	1.11
					modification of chromatin	0.0036	1.34
5	neoplasia	358	RCC2, ZNF839, HSF1, FZD1, POU2F1, GPSM1, CRIP2, TRPC4AP, PEX6, ST8SIA3	PHRF1, SLC7A6OS, ORC4, CSNK1A1, GNPTAB, CD226, COX17, HINFP, WDR82, TMEM44	colorectal neoplasia	0.0092	0.59
					large intestine neoplasm	0.0001	0.72
					abdominal neoplasm	8E-08	0.76
					malignant neoplasm of large intestine	0.0003	1.04
					incidence of tumor	0.019	1.24
					neoplasia of epithelial tissue	8E-08	1.25
					tumorigenesis of tissue	2E-08	1.35
6	Transcription of RNA	108	HSF1, FZD1, POU2F1, CDK5RAP3, ODC1, CARM1, MAP2K5, R3HDM1, XAB2, CD46	PHRF1, HINFP, MSH2, EID2, CCL5, RTF1, CRLF3, DHPS, HNRNPK, SKAP1	transcription of RNA	0.0002	−1.48
					transcription	0.0006	−1.44
					expression of RNA	0.0006	−1.34
7	Leukocyte chemotaxis & recruitment	10	PIP5K1C, PTK2B, FERMT3, MYADM	CCL5, SKAP1, RAP2A, SPN, CD2, CD34, TGFBR2, RASSF5, SH2D1A, DEPTOR	adhesion of mononuclear leukocytes	0.0125	−1.98
8	Natural killer ADCC, cytotoxicity	13	ARRB2, PTK2B, CEACAM1, FERMT3, MMP25	CD226, MSH2, KLRD1, CD244, KIR2DL4, IL2RG, GZMA, RAB27A, NCR3, PTGDR	antibody-dependent cell-mediated cytotoxicity	0.0001	−2.61
					cytotoxicity of natural killer cells	2E-07	−1.53
					cytolysis of lymphocytes	4E-05	−1.45
					degranulation of natural killer cells	0.0023	−0.69
9	Natural Killer cell activation	14		CD226, CCL5, CD244, NCR3, FASLG, CD200, SPN, CASP8, CD2, KLRB1	activation of natural killer cells	0.0002	−2.19
10	Inhibition of lymphocytes	9	CD46, LEPR	CD244, CD80, NCR3, FASLG, KLRB1, PIM1, CLEC2D, LAG3	inhibition of leukocytes	0.0136	−2.33
					inhibition of lymphocytes	0.0072	−2.13
11	Leukocyte proliferation and survival	144	HSF1, POU2F1, CDK5RAP3, ODC1, CAPN1, NCSTN, DNASE1L1, SLC11A2, PIP5K1C, TCIRG1	TSTA3, ORC4, CSNK1A1, COX17, SLC14A1, CD226, HINFP, MSH2, EID2, CD70	cell survival	0.0192	−1.88
					proliferation of cells	0.002	−2.77
					proliferation of immune cells	0.0005	−2.73
					proliferation of lymphatic system cells	0.0006	−2.66
					proliferation of blood cells	0.0009	−2.57
					quantity of T lymphocytes	0.0009	−2.52
					quantity of thymocytes	0.0071	−2.42
					proliferation of lymphocytes	0.0001	−2.40
					quantity of hematopoietic progenitor cells	0.0036	−2.35
					development of blood cells	0.0196	−2.18
					metabolism of DNA	0.0016	−2.11
					differentiation of lymphocytes	0.0065	−2.04
					cell proliferation of T lymphocytes	5E-06	−1.94
					quantity of lymphoid tissue	0.0066	−1.93
					double-stranded DNA break repair	0.0127	−1.89
					differentiation of mononuclear leukocytes	0.0209	−1.88
					T cell development	0.0136	−1.85
					development of lymphocytes	0.0132	−1.84
					expansion of T lymphocytes	0.0007	−1.84
					repair of DNA	0.0006	−1.81
					quantity of lymphocytes	0.0165	−1.79
					homeostasis of leukocytes	0.0092	−1.75
					Lymphocyte homeostasis	0.01	−1.75
					quantity of blood cells	0.0014	−1.69
					differentiation of T lymphocytes	0.0032	−1.65
					T cell homeostasis	0.0057	−1.61
					function of leukocytes	0.0011	−1.60
					function of blood cells	0.002	−1.60
					expansion of lymphocytes	0.0005	−1.59
					quantity of leukocytes	0.0113	−1.57
					quantity of lymphoid organ	0.0003	−1.56
					quantity of lymphatic system cells	0.0068	−1.55
					response of mononuclear leukocytes	0.0176	−1.50
					function of T lymphocytes	0.0055	−1.46
					quantity of mononuclear leukocytes	0.0181	−1.46
					proliferation of naive T lymphocytes	0.0169	−1.39
					function of lymphocytes	0.0005	−1.33
					function of lymphatic system cells	0.0006	−1.33
					expansion of lymphoid cells	0.0002	−1.27
12	Cytotoxicity/Cell-mediated response	27	CAPN1, CD46, ALDOA, ARRB2, PTK2B, CEACAM1, ABCA3, FERMT3, PILRA, MMP25	TSTA3, CD226, MSH2, RRAGA, CCL5, HPRT1, KLRD1, CD80, CD52, CD244	cytotoxicity of lymphatic system cells	8E-09	−2.56
					cytotoxicity of cells	3E-08	−2.44
					cytotoxicity of lymphocytes	2E-08	−2.42
					cytotoxicity	8E-08	−2.19
					cytolysis of tumor cells	9E-05	−2.16
					cytotoxicity of leukocyte cell lines	0.0005	−1.98
					cytolysis of leukocytes	1E-05	−1.94
					degranulation of cells	0.006	−1.90
					cell-mediated response	0.0207	−3.05
					cytolysis of blood cells	6E-06	−1.26
					cytolysis	1E-07	−0.95
					cytotoxicity of tumor cell lines	0.0151	−0.93
					cytolysis of leukocyte cell lines	3E-05	0.00
13	Leukocyte Activation	13	CD1B, CD46, CD1A, IRF5, ARRB2, CEACAM1, AHNAK, PHC1	CD226, CCL5, HPRT1, KLRD1, CD80, CD244, IL2RG, NCR3, IL23A, GZMA	activation of lymphatic system cells	0.0033	−2.73
					activation of mononuclear leukocytes	0.006	−2.66
					activation of T lymphocytes	0.012	−2.64
					activation of lymphocytes	0.0039	−2.62
14	Killing of leukocytes	12	PIP5K1C, PHC1	CD226, HNRNPK, ITGB3BP, PTPRCAP, LYAR, CD244, NCR3, RAB27A, BRCA1, FASLG	killing of T lymphocytes	0.0003	−2.62
					killing of leukocytes	0.0008	−2.53
					killing of antigen presenting cells	0.0043	−2.22
					killing of phagocytes	0.0063	−2.22
					killing of lymphocytes	0.0004	−2.15
					killing of cells	0.0073	−2.10
					killing of dendritic cells	0.0003	−1.99
					killing of natural killer cells	0.0013	−1.34

#, function number; Function group, general function category; N, number of genes from this analysis that were involved in the function; Top 10 positive genes, top 10 genes associated with the function that were increased; Top 10 negative genes, top 10 genes associated with the function that were decreased; IPA function, name of the function defined by Ingenuity pathway analysis (IPA); P, nominal p-value; Z, z-score of the prediction, positive for increased and negative for inhibited function at 5 weeks of ART+Peg-IFN-α2a when compared to ART in subjects who did not decrease integrated HIV DNA

Table II.

Functional categories by Ingenuity pathway analysis that were significantly enriched among genes with changes after 12 weeks of Peg-IFN-α2a immunotherapy when compared to ART

#	Function group	N	Top 10 positive genes	Top 10 negative genes	IPA Function	p	Z
1	thrombocytopenia & cytopenia	21	TYW1, POLE, SRRT, CD200R1, DCAF17, ODF2, GTF3C2, PLXNA3, ZNF252P, HAGH	CAMK2D, LIPN, NDUFS1, OR11H12, CLTA, GCH1, ZNF436, NMNAT1, APH1B, CASP4	pneumonia	0.0009	1.39
					thrombocytopenia	0.0003	2.71
					hemorrhagic disease	0.001	2.88
					cytopenia	4E-06	1.36
2	Cancer	156	TYW1, POLE, SRRT, CD200R1, DCAF17, ODF2, GTF3C2, PLXNA3, ZNF252P, HAGH	CAMK2D, LIPN, NDUFS1, OR11H12, CLTA, GCH1, ZNF436, NMNAT1, APH1B, CASP4	replication of cells	0.0013	1.91
					breast cancer	0.0006	1.94
					mammary tumor	0.0003	1.53
					leukemia	0.0003	1.61
					organismal death	8E-06	2.04
					morbidity or mortality	2E-06	1.99
					breast or ovarian cancer	0.0006	1.94
					cancer of secretory structure	0.0014	1.39
					breast or colorectal cancer	7E-06	1.53
3	neoplasia	34	TYW1, POLE, SRRT, CD200R1, DCAF17, ODF2, GTF3C2, PLXNA3, ZNF252P, HAGH	CAMK2D, LIPN, NDUFS1, OR11H12, CLTA, GCH1, ZNF436, NMNAT1, APH1B, CASP4	tumorigenesis of malignant tumor	0.0006	1.43
					soft tissue neoplasm	0.0001	2
4	HIV replication & production of HIV	36	TYW1, POLE, SRRT, CD200R1, DCAF17, ODF2, GTF3C2, PLXNA3, ZNF252P, HAGH	CAMK2D, LIPN, NDUFS1, OR11H12, CLTA, GCH1, ZNF436, NMNAT1, APH1B, CASP4	production of HIV-1	0.0013	1.2
					infection of phagocytes	0.0003	1.33
					production of HIV	7E-05	1.2
					viral life cycle	0.0007	1.2
					production of virus	0.0024	1.54
					replication of HIV	0.0005	1.49
					infection of mammalia	2E-08	2.96
					replication of virus	4E-06	1.39
5	Bacterial infection	18	TYW1, POLE, SRRT, CD200R1, DCAF17, ODF2, GTF3C2, PLXNA3, ZNF252P, HAGH	CAMK2D, LIPN, NDUFS1, OR11H12, CLTA, GCH1, ZNF436, NMNAT1, APH1B, CASP4	anthrax	0.0009	1.97
					inflammation of intestine	0.0002	1.23
					Bacterial Infections	6E-05	1.31
6	Natural killer ADCC, cytotoxicity	10	TYW1, POLE, SRRT, CD200R1, DCAF17, ODF2, GTF3C2, PLXNA3, ZNF252P, HAGH	CAMK2D, LIPN, NDUFS1, OR11H12, CLTA, GCH1, ZNF436, NMNAT1, APH1B, CASP4	cytotoxicity of natural killer cells	0.0029	−1.2
7	Th1 response	15	TYW1, POLE, SRRT, CD200R1, DCAF17, ODF2, GTF3C2, PLXNA3, ZNF252P, HAGH	CAMK2D, LIPN, NDUFS1, OR11H12, CLTA, GCH1, ZNF436, NMNAT1, APH1B, CASP4	TH1 immune response	0.0027	−1.4
					quantity of IL-6 in blood	0.0006	−1.4
8	Cytoskeleton and endocytosis	24	TYW1, POLE, SRRT, CD200R1, DCAF17, ODF2, GTF3C2, PLXNA3, ZNF252P, HAGH	CAMK2D, LIPN, NDUFS1, OR11H12, CLTA, GCH1, ZNF436, NMNAT1, APH1B, CASP4	endocytosis	0.0007	−1.5
					organization of cytoskeleton	0.0004	−1.5
					organization of cytoplasm	0.0001	−1.5
9	Myeloid activation and phagocytosis	35	TYW1, POLE, SRRT, CD200R1, DCAF17, ODF2, GTF3C2, PLXNA3, ZNF252P, HAGH	CAMK2D, LIPN, NDUFS1, OR11H12, CLTA, GCH1, ZNF436, NMNAT1, APH1B, CASP4	cytotoxicity of myeloid cells	0.0015	−2
					fusion of phagosomes	0.0001	−1.4
					orientation of macrophages	0.0021	−2
					maturation of bone marrow-derived dendritic cells	0.0003	−1.7
					class switching of B lymphocytes	1E-04	−1.4
					polarization of antigen presenting cells	0.0008	−1.4
					orientation of myeloid cells	8E-05	−1.7
					activation of dendritic cells	0.0023	−1.4
					maturation of dendritic cells	0.0002	−1.7
					development of phagocytes	0.0003	−1.4
					development of myeloid cells	0.0004	−1.3
					maturation of phagocytes	0.0002	−1.7
					binding of NFkB binding site	6E-06	−1.4
					internalization of cells	8E-05	−2.3
					response of lymphatic system cells	0.0018	−1.4
					maturation of leukocytes	0.0025	−1.3
					phagocytosis of cells	0.0028	−2.2
					phagocytosis	0.0005	−2.8
					activation of antigen presenting cells	4E-05	−2.1
					engulfment of cells	0.0011	−2.3
					differentiation of lymphatic system cells	0.001	−1.4
					development of mononuclear leukocytes	0.0007	−1.4
					systemic autoimmune syndrome	2E-05	−1.4
10	Reduced Lentiviral Infection	37	TYW1, POLE, SRRT, CD200R1, DCAF17, ODF2, GTF3C2, PLXNA3, ZNF252P, HAGH	CAMK2D, LIPN, NDUFS1, OR11H12, CLTA, GCH1, ZNF436, NMNAT1, APH1B, CASP4	infection of tumor cell lines	0.0024	−2.1
					infection by HIV-1	0.0002	−1.7
					HIV infection	2E-05	−2
					infection by lentivirus	1E-05	−1.9
					infection by Retroviridae	8E-06	−2
					expression of RNA	4E-05	−1.3
11	Leukocyte chemotaxis & recruitment	27	SLC37A4, ARHGEF7, TNC, NEO1, LCK, ROPN1L, PHB, CAMK1D, STK25, CARMIL2	CAMK2D, GUCY1A3, RHOC, PML, WARS, WAS, GLIPR2, CLEC7A, RALB, NRAS	recruitment of monocyte-derived macrophages	0.0005	−2
					response of connective tissue cells	0.002	−1.4
					cell movement of microglia	0.0018	−1.7
					migration of leukemia cell lines	0.0002	−2.3
					chemotaxis of neutrophils	0.0015	−1.3
					homing of neutrophils	0.0006	−1.3
					recruitment of neutrophils	0.0003	−1.8
					aggregation of blood cells	0.003	−1.6
					recruitment of granulocytes	0.0003	−2.3
					recruitment of phagocytes	0.0011	−2.6
					recruitment of myeloid cells	0.0001	−2.7
					chemotaxis of phagocytes	0.0004	−1.3
					recruitment of leukocytes	0.0005	−2.8
					chemotaxis of leukocytes	0.002	−1.7
					homing of leukocytes	0.0025	−1.9
					leukocyte migration	2E-05	−1.5
					migration of cells	8E-05	−2.4
					cell movement	0.0006	−2.4
12	Leukocyte cell death	33	TRAP1, TNC, NSMCE4A, SPTBN1, SLC37A4, LCK, PARP16, NME4, FANCD2, PHB	CAMK2D, GCH1, NMNAT1, CASP4, GUCY1A3, RHOC, PML, WAS, NCEH1, POLDIP2	cell death of bone marrow-derived macrophages	0.0014	−2.2
					quantity of apoptotic cells	0.0003	−1.7
					cell death of neuroglia	0.0011	−2
					cell death of macrophages	0.0005	−2.1
					cell death of antigen presenting cells	0.0011	−1.5
					cell death of hepatocytes	0.0003	−1.6
					cell death of liver cells	0.0002	−1.4
					cell death of myeloid cells	8E-05	−2.7
					cell death of hematopoietic progenitor cells	0.0003	−2.3
					cell death of fibroblasts	7E-05	−1.7
					cell death of lymphocytes	0.0004	−2.1
					cell death of lymphoid cells	0.0002	−1.8
					cell death of cervical cancer cell lines	1E-05	−1.7
					cell death of lymphatic system cells	1E-04	−2.2
					cell death of epithelial cells	0.0027	−1.6
					cell death of fibroblast cell lines	1E-05	−2
					cell death of connective tissue cells	5E-06	−2.1
					cell death of immune cells	2E-05	−2.8
					cell death of blood cells	6E-06	−3
					cell survival	1E-07	−1.6
13	Apoptosis	24	TRAP1, TNC, LCK, NME4, ITPR3, PRKDC, PRKCZ, LRIG1, HAX1, CHEK2	CASP4, RHOC, PML, NCEH1, WAS, FCGR1A, NRAS, REL, RALB, CCAR1	apoptosis of microglia	0.002	−1.9
					apoptosis of neuroglia	0.0003	−1.7
					apoptosis of macrophages	0.0003	−1.6
					apoptosis of antigen presenting cells	0.0006	−1.5
					apoptosis of phagocytes	0.0027	−1.5
					apoptosis of hematopoietic progenitor cells	0.0004	−2
					apoptosis of myeloid cells	4E-05	−2.3
					apoptosis of leukemia cell lines	0.0025	−1.5
					apoptosis of fibroblast cell lines	0.0022	−2.7
					apoptosis of cervical cancer cell lines	0.0002	−1.8
					apoptosis of lymphocytes	0.0002	−1.8
					apoptosis of lymphoid cells	0.0001	−1.5
					apoptosis of lymphatic system cells	4E-05	−1.8
					apoptosis of leukocytes	3E-05	−1.8
					apoptosis of blood cells	1E-05	−2.1
14	Reactive oxygen & septic shock response	32	CD200R1, TRAP1, LCK, PHB, PRKCZ, APBA3, UCN, RBPJ, UBQLN1, SERPINF1	NDUFS1, GCH1, CASP4, FCGR1B, GUCY1A3, SNAP23, CLEC7A, FCGR1A, NRAS, CD68	constriction of bronchus	0.0005	−2
					secretion of nitric oxide	0.0001	−2.2
					reperfusion injury of kidney	0.0004	−2.4
					reperfusion injury of organ	0.001	−1.5
					reperfusion injury	0.0009	−1.7
					endotoxin shock response	7E-06	−2.5
					toxemia	3E-05	−2.2
					resorption of bone	0.0021	−1.8
					experimentally-induced arthritis	0.0004	−1.6
					septic shock	0.0002	−2.3
					Shock Response	0.0003	−2.3
					production of reactive oxygen species	0.0001	−2.5
					synthesis of reactive oxygen species	5E-06	−2.9
					metabolism of reactive oxygen species	3E-06	−2.8
15	Leukocyte proliferation and survival	38	SRRT, PLXNA3, BCCIP, TRAP1, NUDC, RBM15, ARHGEF7, SPTBN1, CTSF, TNC	GCH1, CAMK2D, NMNAT1, PML, WARS, RHOC, RMI1, WAS, CLEC7A, IFNGR2	differentiation of leukocytes	0.0011	−1.2
					development of leukocytes	0.0002	−1.5
					development of blood cells	0.0002	−1.5
					proliferation of immune cells	0.002	−1.2
					proliferation of cells	7E-05	−2
16	Cytotoxicity/Cell-mediated response	12	LCK, CD96, CD40LG, PRKCA, SMAD3, CREBBP, STAT5B	WAS, IFNGR2, CLEC7A, STX11, FCGR1A, NRAS, REL, C3AR1, TXN, CASP1	cytotoxicity of leukocytes	0.0011	−1.4
					cell-mediated response	0.0001	−1.9
					cytotoxicity of cells	0.0003	−1.5
17	Leukocyte Activation	33	CD200R1, SLC37A4, TNC, ARHGEF7, LCK, MINA, FANCD2, CAMK1D, CABIN1, PRKDC	CASP4, GUCY1A3, ACOT11, PML, WAS, NCEH1, IFNGR2, CLEC7A, SNAP23, RALB	immune response of leukocyte cell lines	0.0001	−1.3
					inflammation of secretory structure	0.0011	−2
					antibody response	0.0004	−1.6
					response of mononuclear leukocytes	0.0004	−2
					immune response of leukocytes	0.0014	−2.4
					activation of T lymphocytes	4E-05	−2.5
					activation of lymphocytes	0.0002	−2.7
					activation of mononuclear leukocytes	0.0002	−2.9
					immune response of cells	7E-05	−2.8
					function of blood cells	2E-06	−1.5
					activation of leukocytes	2E-05	−3.1
					activation of blood cells	8E-05	−3
					inflammatory response	3E-05	−1.8
					activation of cells	0.0003	−2.7

#, function number; Function group, general function category; N, number of genes from this analysis that were involved in the function; Top 10 positive genes, top 10 genes associated with the function that were increased; Top 10 negative genes, top 10 genes associated with the function that were decreased; IPA function, name of the function defined by Ingenuity pathway analysis (IPA); P, nominal p-value; Z, z-score of the prediction, positive for increased and negative for inhibited function after 12 weeks of Peg-IFN-α2a immunotherapy when compared to ART in subjects who did not decrease integrated HIV DNA

Overall, these data indicate that reductions in cell-associated integrated HIV DNA following Peg-IFN-α2a administration are associated with activation of NK and cell-mediated gene expression programs.

Increase in innate activity and cytotoxic responses before and after Peg-IFN-α2a immunotherapy in subjects controlling HIV during Peg-IFN-α2a monotherapy.

We analyzed whether innate variables measured on ART and after 5 weeks of ART+Peg-IFN-α2a were related to plasma viral control after 12 weeks of Peg-IFN-α2a monotherapy.

Consistent with baseline values as indicative of viral control outcomes after immunotherapy, primary endpoint responders (R) had a higher baseline frequency of CD3⁺CD4⁻ T cells expressing CD38 (p=0.0459) and HLA-DR (p=0.0079), higher baseline frequencies of NK cells bearing inhibitory receptors (i.e. CD3⁻CD56^brightKIR2DL2/DL3⁺, p=0.0397) and higher baseline plasma levels of IP-10 (p=0.0395, Fig 5A-D) when compared to primary endpoint non-responders (N).

Figure 5.

Immune correlates of clinical response after Peg-IFN-α2a immunotherapy on ART. Shown are whole blood cell frequency and PBMC functional measures distinguishing primary endpoint responders (R) from primary endpoint non-responders (N). Panels A-D show in N and R groups the following variables on ART: (A) Frequency of CD3⁺CD4⁻CD38⁺, (B) MFI of HLA-DR on CD3⁺CD4⁻, (C) CD3⁻C56^brightKIR2DL2/DL3⁺, and (D) plasma IP-10 (pg/ml). Panels E-H show Δchange [(ART+Peg-IFN-α2a) – (ART)] between N and R groups for: (E) CD3⁻CD56^dimCD16⁻CD25⁺HLA-DR⁺, (F) CD3⁻CD56^dimKIR2DL1⁺, (G) CD3⁻CD56^brightKIR2DL2/DL3⁺, and (H) CD19⁻BDCA1⁺CD11c⁺CCR7⁺. Panels A-D show available data for N an R, together with the mean of distribution, and significant (<0.05) p values. Data in panels E-H are shown as mean of the distribution for each group (left) and per patient for each group (right), together with significant (<0.05) p values.

After 5 weeks of ART+Peg-IFN-α2a, primary endpoint responders (R) also showed changes not observed in primary endpoint non-responders (N) such as a decrease in the frequencies of CD25⁺HLA-DR⁺ NKs [i.e. CD3⁻CD56^dimCD16⁻CD25⁺HLA-DR⁺, p=0.017)] (Fig 5E), and of NK cells bearing inhibitory markers [i.e. CD3⁻CD56^dimKIR2DL1⁺ (p=0.0235), CD3⁻CD56^brightKIR2DL2/DL3⁺ (p=0.0184)] (Fig 5F-G). In addition, primary endpoint responders (R) also showed an increase in the frequencies of mature MDC [i.e. CD19⁻BDCA1⁺CD11c⁺CCR7⁺, p=0.0318)] (Fig 5H). NK cytotoxicity was tested ex vivo at baseline and after 5 weeks of ART+Peg-IFN-α2a showing no detectable effect of treatment on constitutive or in vitro IFN-α-induced cytotoxicity (Fig 6A-B). However, subjects controlling viral rebound during Peg-IFN-α2a monotherapy (primary endpoint responders) had a higher ex vivo IFN-α-induced NK cytotoxic response (R; Fig 6C-D). Supporting higher NK degranulation in vivo, we also detected a trend for higher constitutive expression of CD107a in CD56^dim NK cells (i.e. Lin3⁻CD56^dimCD16⁻CD107a⁺, p=0.099, Fig 6E) in primary endpoint responders (R) when compared to primary endpoint non-responders (N).

Figure 6.

NK cytotoxic responses as a correlate to HIV control after Peg-IFN-α2a immunotherapy on ART. (A) In vitro constitutive NK cytotoxic responses against K562 are shown as AUC over 50:1 to 6:1 at baseline (ART) and 5 weeks of ART+Peg-IFN-α2a, (B) In vitro IFN-α stimulated NK cytotoxic responses against K562 (shown as IFN-α stimulated - constitutive cytotoxicity) are shown as AUC over 50:1 to 6:1 at baseline (ART) and 5 weeks of ART+Peg-IFN-α2a, (C) In vitro IFN-α stimulated NK cytotoxic responses against K562 (shown as IFN-α stimulated - constitutive cytotoxicity) are shown as AUC over 50:1 to 6:1 at 5 weeks of ART+Peg-IFN-α2a in N and R groups, (D) AUC responses are shown at 5 weeks of ART+Peg-IFN-α2a (blue bars) and at 12 weeks of Peg-IFN-α2a monotherapy (red bars) in N and R subjects (study numbers are shown for each group), (E) Frequency of Lin3⁻CD56^dimCD16⁻CD107a⁺ at 5 weeks of ART+Peg-IFN-α2a in the absence of in vitro stimulation with IFN-α in primary endpoint responders (R) and primary endpoint non-responders (N). Panels A-C and E show available data for study subjects, together with the mean of distribution, and significant (<0.05) p values. Panel D shows data in N and R per patient.

Overall, we identify lower KIR expression and higher NK cytotoxicity as correlates of HIV plasma viral load control upon Peg-IFN-α2a monotherapy.

Discussion

In this study we show that increases in innate activity, NK cytotoxicity and gene expression are correlates of integrated HIV DNA decrease and plasma viral load control after ART interruption and continued Peg-IFN-α2a immunotherapy. Specifically, we identify modulations in KIR expression, NK cytotoxicity, and mature MDCs as innate cell changes that are associated with HIV control after ART interruption and Peg-IFN-α2a monotherapy. We also introduce a baseline gene signature able to identify subjects more likely to control viremia after Peg-IFN-α2a monotherapy, as well as a gene signature able to identify subjects that fail to modulate ISGs after adding Peg-IFN-α2a to ART or control viremia after Peg-IFN-α2a monotherapy. Interestingly, we describe that subjects failing to modulate ISG gene expression may include active mRNA expression patterns actively countering gene expression after Peg-IFN-α2a. While future independent validation of described gene signatures is needed, our data following Peg-IFN-α2a treatment are consistent with prior observations with Panobinostat suggesting that induction of ISGs together with changes in NK activation correlate with decreases in integrated HIV DNA after treatment with Panobinostat in well controlled HIV-infected subjects on ART (56). Furthermore, we do find a significant association between genes that were upregulated in PBMC from subjects controlling viral load under Peg-IFN-α2a monotherapy and genes described to be upregulated in isolated NK cells from HIV-1 controllers (54).

Combined administration of ART+Peg-IFN-α2a for 5 weeks resulted in a clear induction of IFN-mediated genes in the majority of subjects, however the levels of antiviral IFN-induced genes alone were not enough to segregate between primary endpoint responders (R) and primary endpoint non-responders (N). The fact that not all subjects that induced upregulation of these genes were primary endpoint responders highlights a potential difference between the dominant antiviral correlates of IFN modulation on or off ART, as induction of ISGs from pre-immunotherapy levels was noted as a correlate of temporal viral control of Peg-IFN-α2a administered to viremic subjects (13). By contrast, our study indicates that when Peg-IFN-α2a is administered in suppressed subjects on ART the activation of innate cellular activity may be a greater correlate to control of HIV than ISG induction. Importantly, two independent NK cell changes (lower inhibitory KIR expression in NK, higher NK cytotoxicity) were correlated with control of plasma viral load while two independent assessments by IPA gene expression supported NK cell activity (among other cell-mediated activity) as being significantly correlated with a decrease in integrated HIV DNA by primary endpoint.

Our data do not exclude a role for ISG gene on HIV control after Peg-IFN-α2a on ART as all primary endpoint responders did increase ISGs. On the other hand, the observation that 4 of the 9 primary endpoint non-responders had gene expression profiles indicative of a refractory response indicates the possibility of identifying subjects that will not control HIV upon Peg-IFN-α2a monotherapy. Importantly, the lack of response in these subjects includes evidence for increases in miRNAs together with the downregulation of their corresponding gene targets. Amongst these miRNA gene targets were genes associated with cancer (e.g. IFI44L (36), RGL1 (37), MARCH1 (42), MRPL17 (39)) and antiviral activity (e.g. IFIT5 (57), TLR7 (1)). Taken together with the upregulation of miRNas (e.g. miR-155, let-7e, miR-370, miR-192, miR-1275) that target IFN-inducible anti-viral effector molecules (43), it is interpreted that these subjects may exemplify a response profile to Peg-IFN-α2a that would predict a return of plasma viral load as was observed upon ART interruption.

Previous data by Hua et al in HIV-1/HCV co-infected patients showed an increase in NK subsets after treatment with Peg-IFN-α2a as well as an association between reduction in viral reservoir and NK cells co-expressing activation markers NKG2D and NKp30 (58). In our study, 5 weeks of ART+Peg-IFN-α2a did not result in changes in the major NK subsets (besides an increase in CD56^bright NK cells) or in activated NK suggesting lack of a direct effect of treatment in these subsets. Furthermore, we observed in subjects able to control plasma viral load a decrease in CD25⁺HLA-DR⁺CD56^dimCD16⁻ NK cells supporting an association between levels of NK activation and HIV burden. Importantly, the observed differences between our study and the study by Hua et al likely reflect differences in study design (e.g patient population studied: HIV-1/HCV in the study by Hua et al versus HIV in our study; treatment duration: average of 11 months in the study by Hua et al versus 5 weeks in our study). Of interest, the most uniform change noted across all subjects receiving Peg-IFN-α2a was a reduction in the ex vivo IFN-α-induced p-STAT-1 suggesting a lower cellular capacity to signal through the type I IFN receptor. Interestingly, constitutive levels of p-STAT-1 in circulating leukocytes were not higher after Peg-IFN-α2a immunotherapy indicating that drug levels in circulation can maintain gene expression changes without sustained higher levels in constitutive p-STAT-1. We also document that the reduction in type I IFN receptor response did not affect type II IFN receptor responses within the same cells indicating a receptor-level mechanism of unresponsiveness. The decrease in type I IFN receptor activity together with the ex vivo data showing higher IFN-induced NK cytotoxicity as a correlate of HIV control suggest that despite noted decreases in IFN-induced p-STAT-1 the retained induction of NK responses was retained within subjects controlling plasma viral load.

Hansen et al (59) have also shown that IFN-α can increase T and NK cells cytotoxicity by boosting the IL-15-induced signaling and cytotoxic activity of these cells. In our study, no increased levels of IL-15 were observed in plasma after 5 weeks of ART+Peg-IFN-α2a. This could be explained because soluble IL-15 can generally not be detected in physiological fluids, as it binds with a very high affinity to IL-15Rα at the surface of antigen-presenting cells. IL-15Rα gene expression was also not detected at a significant level over the background in microarrays in our study. However, GSEA analysis exploring whether IL-15-responsive genes are differentially induced between RR and NN groups using the datasets GSE70214 (60), GSE120904 (database reported by Cezari C et al at the National Center for Biotechnology Information on October 16 2018), GSE22886 (61), GSE22919 (62) and GSE7764 (63) that have been described to be modulated by IL-15, revealed three of these gene sets to be significant (p<0.05 and FDR<5%) in association with both upregulated and downregulated genes (GSE70214, GSE120904, and GSE22886, data not shown). These results suggest that IFN-α may have increased NK cells cytotoxicity by boosting IL-15 effects. Future studies will need to further investigate the connection between Peg-IFN-α2a and IL-15.

In this study, we show an increase in cytotoxic NK response against K562 targets at 5 weeks of ART+Peg-IFN-α2a among subjects able to control plasma viral load. These results support the interpretation that NK cells contribute to a reduction in viral control via direct cell-mediated lysis of infected cells. Consistent with higher cytotoxicity, we also detected a decrease in CD3⁻CD56^dimKIR2DL1⁺ and CD3⁻CD56^brightKIR2DL2/DL3⁺ NK cells in subjects able to control viral load. Although it cannot be excluded that this loss of KIR expression (which is skewed to more differentiated NK cells) could also imply a reduction in more differentiated NK cells in favor of less differentiated NK cell subsets or an expansion of KIR negative NK cells, we interpret that lower inhibitory receptor activity may contribute to an increase in NK-mediated killing. Previous studies have shown that treatment with IFN-α can increase NK cells cytokine secretion, viral suppressive capacity and cytotoxic function (12, 64-67). Gene analysis further supported this interpretation via an enrichment of genes associated with leukocyte activation, proliferation, survival, chemotaxis and recruitment, as well as cytotoxicity/cell mediated response and NK ADCC in persons with reductions in integrated HIV DNA. While our data support higher NK cytotoxic function, they do not address induction of viral suppressive activity. Our study also does not address an additional role for NK cells in viral control as indicated in nonpathogenic simian immunodeficiency virus (SIV) infection where NK cells migrate into lymph node follicles in association with reductions in viral levels within lymph nodes (68).

Finally, unpaired t-test allowed for the identification of a signature that consisted of the top 30 most significantly differentially expressed genes at baseline (Supplemental Fig 2) and was able to segregate primary endpoint responders (R) from primary endpoint non-responders (N). This signature consisted mainly by genes that have been reported to be associated with gene suppression [e.g. DICER1 (69, 70), Wolf-Hirschhorn Syndrome candidate 1 (WHSC1L1) (71)] negative regulation of innate immune responses [e.g. G-patch domain containing 3 (GPATCH3 (72)] or cancer [e.g. annexin A2 pseudogene 1 (ANXA2P1) (73), Rho associated coiled-coil containing protein kinase 1 (ROCK1) (74), hemogen (HEMGN) (75)]. The presence in both primary endpoint responders (R) and primary endpoint non-responders (N) of high levels of expression of genes coding for proteins that participate in transcriptional repression (i.e. WHSC1L1 (71), and DICER1 (69, 70) respectively) underlies uncharacterized associations between gene expression and IFN-α-mediated viral control. Furthermore, no clear segregation was observed between subjects that were able to upregulate ISGs and those that did not, as shown by an upregulation in the RN and NN group of the same genes. Future studies will be needed to validate and reconfirm this baseline gene signature on an independent validation set.

As no T cell responses were measured in our study, our data do not address the effects of Peg-IFN-α2a on T cell-mediated viral control. Paradoxically, recent data from humanized murine models infected with HIV and treated temporally with ART suggest a detrimental role for type I IFNs by acting against CD8⁺ T cell-mediated anti-HIV responses (76, 77). However, the lack of NK effector responses in humanized mice (78), and the absence of qualification of the effects of IFN-β versus IFN-α on the impairment of CD8⁺ T cell responses (79, 80) limit the inference from this animal model data to human responses using Peg-IFN-α2a as described here.

Our study has limitations. First, as a pilot study the sample size is limited, and the uncovered associations and gene signatures described here need to be further validated in larger independent studies. Second, our data do not address an ART interruption without Peg-IFN-α2a monotherapy since as described in our prior publication (25) the food and drug administration (FDA) mandated the removal of that control group. Third, the gene signatures described are obtained in PBMC as opposed to independent cell subsets and the relationship between gene expression and protein levels remains to be determined. Fourth, as no information on treatment history is available on study subjects the role of time to ART initiation on the observed baseline differences between groups remains to be determined. Finally, all measures described were performed in peripheral blood, while no measures in tissue were performed.

Overall, this study provides the first proof-of-concept that in ART-suppressed subjects immunotherapy with type I IFNs resulting in a reduction of HIV measures is associated with activation of NK responses. A randomized trial to formally test whether reductions in HIV measures on ART are the result of IFN-alpha immunotherapy is now underway ().

Key points:

KIRs and NK cytotoxicity correlate with HIV control upon IFN-α immunotherapy; NK activity-associated genes correlate with HIV DNA decline upon IFN-α immunotherapy; A gene signature identifies subjects with no ISGs modulation upon IFN-α immunotherapy

Abbreviations

IFN-α

interferon-α

TLR

Toll-like receptor

NK

natural killer

HIV

human immunodeficiency virus

ART

antiretroviral therapy

Peg-IFN-α2a

pegylated IFN-α2a

HCV

hepatitis C virus

PBMC

peripheral blood mononuclear cells

STAT-1

signal transducer and activator of transcription 1

DC

dendritic cells

FITC

fluorescein isothiocyanate

APC

allophycocyanin

PE

phycoerythrin

KIR2DL1

killer cell immunoglobulin like receptor, two Ig domains and long cytoplasmic tail 1

KIR2DL2/DL3

killer cell immunoglobulin-like receptor, two domains, long cytoplasmic tail 2/3

BDCA1

blood dendritic cell antigen 1

CCR7

C-C chemokine receptor type 7

IgG1k

immunoglobulin G1k

BD

Becton Dickinson

PDC

plasmacytoid DC

MDC

myeloid DC

FSC

forward scatter

SSC

side scatter

MFI

mean fluorescent intensity

RT

room temperature

E

effector cells

MHC

major histocompatibility complex

T

targets

AUC

area under the curve

EGF

epidermal growth factor

FGF-basic

basic fibroblast growth factor

GCSF

granulocyte colony-stimulating factor

GM-CSF

granulocyte-macrophage colony-stimulating factor

HGF

hepatocyte growth factor

IL-1ra

interleukin 1 receptor a

MCP-1

monocyte chemoattractant protein 1

MIG

monokine induced by IFN-γ

MIP-1α

macrophage inflammatory protein 1α

RANTES

regulated on activation normal T cell expressed and secreted

TNF-α

tumor necrosis factor α

VEGF

vascular endothelial growth factor

Ct

cycle threshold

FDR

false discovery rate

ISGs

IFN-stimulated genes

IPA

Ingenuity pathway analysis

GSEA

gene set enrichment analysis

GEO

gene expression omnibus

Stdev

standard deviation

IFI44L

IFN-induced protein 44 like

RGL1

Ral guanine nucleotide dissociation stimulator like 1

PHF11

plant homeodomain finger protein 11

MRPL17

mitochondrial ribosomal protein L17

MARCH1

membrane associated ring-CH-type finger 1

IFNAR1

IFN alpha and beta receptor subunit 1

IFIT5

IFN-induced protein with tetratricopeptide repeats 5

ANKFY1

ankyrin repeat and FYVE domain containing 1

HIC

HIV-1 controllers

NES

normalized enrichment score

C3AR1

complement C3a receptor 1

MX2

MX dynamin like GTPase 2

PLAC8

placenta specific 8

HSH2D

hematopoietic SH2 domain containing

TRIM22

tripartite motif containing 22

NABP1

nucleic acid binding protein 1

GIMAP8

GTPase, IMAP family member 8

S1PR1

sphingosine-1-phosphate receptor 1

ZNF143

zinc finger protein 143

CISH

cytokine inducible SH2 containing protein

DDX58

DExD/H-box helicase 58

TRANK1

tetratricopeptide repeat and ankyrin repeat containing 1

CX3CR1

C-X3-C motif chemokine receptor 1

TUBD1

tubulin delta 1

GCA

grancalcin

TRNT1

tRNA nucleotidyl transferase 1

ADCC

antibody-dependent cytotoxicity

SIV

simian immunodeficiency virus

WHSC1L1

Wolf-Hirschhorn syndrome candidate 1

GPATCH3

G-patch domain containing 3

ANXA2P1

annexin A2 pseudogene 1

ROCK1

Rho associated coiled-coil containing protein kinase 1

HEMGN

hemogen

FDA

food and drug administration