BCL-2 antagonism sensitizes cytotoxic T cell-resistant HIV reservoirs to elimination ex vivo

Abstract

Curing HIV infection will require the elimination of a reservoir of infected CD4+ T cells that persists despite HIV-specific cytotoxic T cell (CTL) responses. Although viral latency is a critical factor in this persistence, recent evidence also suggests a role for intrinsic resistance of reservoir-harboring cells to CTL killing. This resistance may have contributed to negative outcomes of clinical trials, where pharmacologic latency reversal has thus far failed to drive reductions in HIV reservoirs. Through transcriptional profiling, we herein identified overexpression of the prosurvival factor B cell lymphoma 2 (BCL-2) as a distinguishing feature of CD4+ T cells that survived CTL killing. We show that the inducible HIV reservoir was disproportionately present in BCL-2hi subsets in ex vivo CD4+ T cells. Treatment with the BCL-2 antagonist ABT-199 was not sufficient to drive reductions in ex vivo viral reservoirs when tested either alone or with a latency-reversing agent (LRA). However, the triple combination of strong LRAs, HIV-specific T cells, and a BCL-2 antagonist uniquely enabled the depletion of ex vivo viral reservoirs. Our results provide rationale for novel therapeutic approaches targeting HIV cure and, more generally, suggest consideration of BCL-2 antagonism as a means of enhancing CTL immunotherapy in other settings, such as cancer.

Keywords: AIDS/HIV; Adaptive immunity; Cellular immune response; Immunology; T cells.

Figure 1. Transcriptional profiling of target CD4⁺ T cells that survive CTL coculture reveals candidate mechanisms of resistance.

(A) Schematic of peptide-pulse killing assay and flow sorting for transcriptional profiling. (B) PCA showing clustering of cell populations, as indicated. (C) IPA results showing the pathways that were significantly enriched between real bystanders and real survivors. Orange bars, positive Z scores; blue bars, negative Z scores; gray bars, no activity pattern. (D) Top 6 genes by numbers of instances in significant pathways from C. (E) IPA network analysis (subcellular display) showing a significantly enriched network. Interactions with significant pathways from C and with CTL–mediated apoptosis of target cells are also shown. Red shading indicates overexpression in real survivors, and green indicates underexpression, both in comparison with real bystanders. (F) BCL-2 as well as upstream (CASP2) and downstream (PARP) gene expression levels in all 4 conditions. Shown are fragments per kilobase of exon model per million mapped reads (FPKM) from RNA-Seq. FDR-adjusted P values from DESeq analysis are shown.

Figure 2. CD8⁺ CTL preferentially eliminate CD4⁺ T cells with low BCL-2 expression levels.

(A) Schematic of peptide-pulse and killing assay. (B) Representative gating strategy of flow cytometry plots to identify surviving CD4⁺ T cells and CD4/CD8 ratios in either no-treatment or + peptide + CTL conditions. (C) Graph of total BCL-2 MFI (left axis, black line) and CD4⁺ T cell viability normalized to the no-treatment condition (right axis, brown line), following a peptide-pulse killing assay. Total BCL-2 MFI was calculated based on viable CD4⁺ T cells. The dashed line indicates the BCL-2 MFI of an untreated control. (D) Flow cytometry plots depicting BCL-2 gating strategy for BCL-2^hi and BCL-2^lo populations. (E) Graph depicting CD4⁺ T cell counts in BCL-2^hi (right axis, blue) and BCL-2^lo (left axis, black) populations after CTL killing with different concentration peptide-pulsing treatments. Samples were run in triplicate, and shown are median ± range. (F) The data shown are analogous to those in C, but with 2 additions: (a) killing assays were performed in parallel on CD4⁺ cells that had either been activated with anti-CD3/anti-CD28 or were used directly ex vivo (nonactivated); and (b) the markers CD45RA and CCR7 were included in the flow panel to discriminate naive cells (CD45RA⁺CCR7⁺), TCM cells (CD45RA^-CCR7⁺), and TEM cells (CD45RA^–CCR7^–). Statistical significance was determined by t test. **P < 0.01; ****P < 0.0001.

Figure 3. HIV reservoirs are preferentially harbored in BCL-2^hi –expressing CD4⁺ T cells following ex vivo reactivation.

(A) Flow cytometry plots depicting strategy for identifying HIV-expressing cells by gating on populations that were double-positive for the 2 HIV Gag antibodies. Each plot represents 4–8 × 10⁶ events. (B) Flow cytometry plots showing HIV-expressing cells from 6 HIV-infected ART-suppressed donors: unstimulated (top row) and stimulated with PMA/I (bottom row). The numbers adjacent to the Gag⁺ gates indicate the numbers of events detected per million cells. (C) Flow cytometry plot depicting BCL-2 versus Gag expression in ex vivo CD4⁺ T cells from an ART-suppressed donor. (D and E) BCL-2 MFI of Gag⁺ and Gag^- populations in ex vivo CD4⁺ T cells from (D) ART-suppressed donors (the same donors as in B) or (E) 4 ARV-naive donors, after PMA/I stimulation (Wilcoxon’s signed-rank test). (F) Significantly greater differences in BCL-2 expression, between Gag⁺ and Gag^- CD4⁺ T cells, were observed in ART-suppressed donors compared with ART-naive individuals (unpaired t test).

Figure 4. Intact HIV proviruses are preferentially harbored in BCL-2^hi–expressing CD4⁺ T cells ex vivo.

(A) Shown are ddPCR results quantifying HIV DNA in resting ex vivo CD4⁺ T cells from ARV-treated donors that had been flow cytometry sorted based on BCL-2 expression. Intact quantification based on droplets that were double-positive for gag and env signals (represent full-length proviruses); gag, quantification based on any droplet amplified with gag primer/probes; env, quantification based on any droplet that amplified with env primer/probes (Wilcoxon’s matched-pairs signed-rank test, n = 7). (B) Flow cytometry plots depicting sorting based on both memory phenotype and BCL-2 expression, using CD45RA and CCR7 to separate TCM and TEM populations. (C) Intact and gag (see A) ddPCR results on samples from 2 ARV-treated donors, WWH-B008 (corresponds to flow plots in B), and WWH-B011. Note that the difference in presentation and analysis of these ddPCR data versus other ddPCR data in the manuscript is due to the low DNA yield after BCL-2 intracellular staining and flow sorting. Whereas in other experiments, each of 8 ddPCR replicates were treated as individual data points, here the ddPCR software (Quantasoft) generated maximum likelihood estimates of 95% CI (shown) based on the frequency of positive droplets for all 4 to 6 replicates taken together. This analysis method is recommended by the instrument manufacturer for the analysis of rare events.

Figure 5. BCL-2 antagonist ABT-199 failed to drive reductions in ex vivo, latently infected CD4⁺ T cells in HIVE assays.

(A) Schematic of a HIVE assay using ex vivo CD4⁺ T cells from ART-suppressed individual showing endpoints. (B) A representative HIVE assay showing total HIV DNA (left, mean ± SD of 8 replicates) and IUPM (right, ± 95% CI). Statistical significance determined by 1-way ANOVA for ddPCR and a pairwise χ² test for QVOA. Summary data for ABT-199 tested at (C) 1 μM and (D) 100 nM in following HIVE assays. Levels of HIV DNA (left) and IUPM (right) are shown, comparing ABT-199 alone versus no treatment and bryostatin-1 + ABT-199 versus bryostatin-1 (n = 8 for C, n = 6 for D). Dashed lines indicate paired bryostain-1 versus no-treatment conditions. DMSO was added to no-treatment conditions at a matched concentration with +treatment conditions. Statistical significance was determined by Wilcoxon’s matched-pairs signed-rank test.

Figure 6. ABT-199 enables modest reductions in HIV-infected cells by HIV-specific T cell effectors, following reactivation with bryostatin-1.

(A) Schematic of the HIVE assay with ddPCR as the endpoint. (B–E) ddPCR data measuring HIV-env (B and E) or HIV-gag (C and D) in DNA from HIVE assay samples, as indicated. Shown are mean ± SD values of 8 replicates per sample (following exclusion of outliers based on a prespecified criterion; see Methods). P values were calculated by ordinary 1-way ANOVA, with Tukey’s multiple comparisons test if ANOVA test was significant.

Figure 7. ABT-199 enables CTL-mediated reductions in ex vivo HIV reservoirs following reactivation with anti-CD3/anti-CD28.

(A) Schematic of the HIVE assay showing representative endpoints. (B and C) Representative ddPCR data (mean ± SD of 8–12 replicates) from 2 HIVE assays using autologous HSTs (B) and an autologous HIV-specific CTL clone (C). P values were determined by 1-way ANOVA. (D and E) Representative QVOA data showing maximum likelihood estimates of IUPM ± 95% CI (the same HIVE assays in B and C). P values were determined by pairwise χ² test. The representative QVOA plates shown on the right correspond to the no-treatment and the anti-CD3/anti-CD28 + HIV-specific effector + ABT-199 conditions.

Figure 8. Summary data showing that tricombinations reduce ex vivo HIV reservoirs.

(A) Summary ddPCR data for HIV DNA levels following HIVE assays, comparing each of the indicated treatment conditions (n = 10, except for anti-CD3/anti-CD28 + HIV-specific effector + ABT-199, where n = 6 due to insufficient cell numbers). (B) Summary QVOA data quantifying IUPM following HIVE assays, comparing each of the indicated treatment conditions (n = 10). (C) Summary ddPCR for HIV DNA and (D) QVOA data quantifying IUPM comparing anti-CD3/anti-CD28 + ABT-199 versus anti-CD3/anti-CD28 + HIV-specific effector + ABT-199, emphasizing the reduction of IUPM is only seen in combination of all 3 compounds (n = 6 for C and n = 10 for D). Lines in red indicate where autologous HIV-specific CTL clones were used; black lines were HSTs. Statistical significance was determined by Wilcoxon’s matched-pairs signed-rank test.