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. 2014 Sep 1;7(3):320-333.
doi: 10.1007/s12195-014-0336-9.

Quantitative Evaluation and Optimization of Co-Drugging to Improve anti-HIV Latency Therapy

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Free PMC article

Quantitative Evaluation and Optimization of Co-Drugging to Improve anti-HIV Latency Therapy

Victor C Wong et al. Cell Mol Bioeng. .
Free PMC article

Abstract

Human immunodeficiency virus 1 (HIV) latency remains a significant obstacle to curing infected patients. One promising therapeutic strategy is to purge the latent cellular reservoir by activating latent HIV with latency-reversing agents (LRAs). In some cases, co-drugging with multiple LRAs is necessary to activate latent infections, but few studies have established quantitative criteria for determining when co-drugging is required. Here we systematically quantified drug interactions between histone deacetylase inhibitors and transcriptional activators of HIV and found that the need for co-drugging is determined by the proximity of latent infections to the chromatin-regulated viral gene activation threshold at the viral promoter. Our results suggest two classes of latent viral integrations: those far from the activation threshold that benefit from co-drugging, and those close to the threshold that are efficiently activated by a single drug. Using a primary T cell model of latency, we further demonstrated that the requirement for co-drugging was donor dependent, suggesting that the host may set the level of repression of latent infections. Finally, we showed that single drug or co-drugging doses could be optimized, via repeat stimulations, to minimize unwanted side effects while maintaining robust viral activation. Our results motivate further study of patient-specific latency-reversing strategies.

Conflict of interest statement

Conflicts of Interest

V.C.W., L.E.F., N.M.A., Q.X., S.S.D., and K.M.J. declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1. Comparison of TNF-TSA synergy in activating latent HIV proviruses subject to different levels of chromatin repression
(A) Nuclease sensitivity at the HIV LTR was measured relative to the hemoglobin B (HBB) reference gene. Data are reported as the mean ± standard deviation of two measurements. (B) Dose response of activation by TNF of J-Lat 15.4 (red) and J-Lat 10.6 (blue) in the presence and absence of 400 nM TSA. GFP expression was measured by flow cytometry. Data are reported as the mean ± standard deviation of 3 biological replicates (note that some error bars are not visible). (C) Quantification of drug synergy between TNF and TSA using the Bliss independence model of drug interactions. Gray line at Synergy = 1 indicates no detectable drug interaction. (D-E) Heat map of activation (D) and drug synergy (E) of J-Lat 15.4 (left) and J-Lat 10.6 (right) for a matrix of TNF and TSA doses. Activation and synergy values were linearly interpolated to produce a continuous plot.
Figure 2
Figure 2. Comparison of prostratin-SAHA synergy in activating latent HIV proviruses subject to different levels of chromatin repression
A-B) Dose response of activation by prostratin of J-Lat 15.4 (A) and J-Lat 10.6 (B) in the presence of increasing doses of SAHA (0, 1, 2, and 4 uM with darker shade indicating higher dose). Viral activation was assessed by GFP expression, which was measured by flow cytometry. C) Calculation of drug synergy in the presence of 2 uM SAHA. D-E) Dose response of activation by prostratin of J-Lat 8.4 (D) and ACH-2 (E) in the presence of increasing doses of SAHA (same as in A-B). Viral activation was assessed by GFP expression (J-Lat 8.4) or anti-HIV-1 core antigen staining (ACH-2) and measured by flow cytometry. F) Nuclease sensitivity at the HIV LTR relative to the HBB reference gene. Data are reported as the mean ± standard deviation of two measurements.
Figure 3
Figure 3. Level of chromatin repression does not affect drug synergy between a TNF and a pTEF-b inhibitor
A-B) TNF dose response curves for (A) J-Lat 8.4 and (B) J-Lat 10.6 alone (gray line) and with 5mM HMBA (black line). GFP expression was measured by flow cytometry. Data are reported as the mean ± standard deviation of three biological replicates. C-D) Quantification of drug synergy between TNF and HMBA for (C) J-Lat 8.4 and (D) J-Lat 10.6 using the Bliss indepence model of drug interactions.
Figure 4
Figure 4. TNF-TSA drug synergy was not observed for a polyclonal latently infected population
A) Quantification of latent population over time. Gray line indicates the total level of infection as measured by activation with 10 mM PMA and 400 nM TSA. B) Nuclease sensitivity at the HIV LTR relative to the hemoglobin B (HBB) reference gene. Data are reported as the mean ± standard deviation of two measurements. C) Activation of the polyclonal latent population with 10 ng/ml TNF and/or 400 nM TSA. GFP expression was measured by flow cytometry. % activation was normalized to the total observed infection level (indicated in A). Data are reported as the mean ± standard deviation of three biological replicates.
Figure 5
Figure 5. Latent infections in cultured primary memory CD4+ TCM cells indicated patient-specific requirements for co-drugging
A-B) Latent HIV activation induced by A) SAHA alone and B) prostratin in the presence of increasing doses of SAHA (0, 0.25, and 1 uM) in primary cultured TCM cells derived from PBMCs from 3 healthy donors. Percent of infected cells showing activation was measured by intracellular staining for p24-Gag antigen and analyzed by flow cytometry. Results were normalized to the maximal activation measured following CD3/CD28 stimulation and are presented as the mean ± standard deviation for 3 biological replicates (Donors 1 and 2) or a single replicate (Donor 3). C) Calculation of prostratin-SAHA synergy across 3 donors. Results from two independent latent infections are shown for Donors 1 and 2 (compare closed and open circles). Dotted line indicates synergy = 1 (i.e., no drug interactions).
Figure 6
Figure 6. Optimization of drug synergy to reduce off-target toxicity during latency activation
A) Schematic of experimental protocol to mimic multiple stimulations of latent infections. B) Activation of J-Lat 8.4 by TNF and TSA before sorting the unresponsive fraction (1st treatment; light gray bars) and after sorting the unresponsive fraction (2nd treatment; dark gray bars). C-D) Heat maps of (C) activation of J-Lat 8.4 and (D) toxicity in uninfected Jurkat cells for a matrix of TNF and SAHA doses. GFP expression was measured by flow cytometry and toxicity was measured by staining for anti-active caspase 3. Activation and toxicity values were measured for a range of doses and were linearly interpolated to produce a continuous plot (see Supp. Tables S1-2 for data matrix values). E-F) Cumulative activation and F) cumulative toxicity for repeated dosing of a combination TNF+SAHA that maximizes total activation (blue) or optimizes between activation and toxicity (red).
Figure 7
Figure 7. Schematic representations of how different classes of latent HIV integrations are affected by LRA treatment
A) Pharmacological reversal of latency moves virus from latency to a replicative state by increasing transcription factor activation (red) and/or reversing chromatin repression (blue). B-C) Viral integrations can be divided into two classes based on level of chromatin repression at the promoter. B) Viral promoter accessibility due to chromatin repression is much lower than the threshold level needed for viral activation and therefore co-drugging is beneficial (purple) or C) Promoter accessibility is close to the level required for promoter activation due to low chromatin repression and only one drug is required for activation.

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