A hardwired HIV latency program

Abstract

Biological circuits can be controlled by two general schemes: environmental sensing or autonomous programs. For viruses such as HIV, the prevailing hypothesis is that latent infection is controlled by cellular state (i.e., environment), with latency simply an epiphenomenon of infected cells transitioning from an activated to resting state. However, we find that HIV expression persists despite the activated-to-resting cellular transition. Mathematical modeling indicates that HIV's Tat positive-feedback circuitry enables this persistence and strongly controls latency. To overcome the inherent crosstalk between viral circuitry and cellular activation and to directly test this hypothesis, we synthetically decouple viral dependence on cellular environment from viral transcription. These circuits enable control of viral transcription without cellular activation and show that Tat feedback is sufficient to regulate latency independent of cellular activation. Overall, synthetic reconstruction demonstrates that a largely autonomous, viral-encoded program underlies HIV latency—potentially explaining why cell-targeted latency-reversing agents exhibit incomplete penetrance.

Copyright © 2015 Elsevier Inc. All rights reserved.

Figures

Fig. 1. Two models of HIV latency regulation: Cell-state control vs. Autonomous programming

(A) Left: the prevailing hypothesis of HIV proviral latency regulation. As CD4+ T cells relax from an activated state (permissive to infection) to a resting-memory state, the host-cell environment silences HIV gene expression restricting Tat transactivation of the LTR. Right: The alternate hypothesis that HIV Tat positive feedback is robust to changes of the host-cell environment and operates autonomously despite changes in cell state. The overlapping nature of cellular and viral regulatory circuits confounds testing between these hypotheses (i.e. the LTR actuates Tat feedback but doubles as a sensor of the host-cell environment). (B) If cell state and viral circuitry can be orthogonalized (i.e. decoupled), the influence of cellular state on viral latency can be analyzed via an orthogonal 2D graphical correlation. Left: If cellular state dominates regulation of viral latency, resting cells would inhibit viral circuitry while active cells would induce viral gene expression generating a strong correlation between cell state and viral activity. Right: If an autonomous latency circuit regulates latency, both latent and active viral expression could be generated in either resting cells or activated T cells, producing little correlation between cell state and viral activity.

Fig. 2. HIV expression is autonomous to changes in cellular state: transitioning of primary T lymphocytes from activated to resting does not silence HIV expression

(A) Schematic of activation, infection, and long-term observations of relaxing primary CD4+T cells with full-length HIV-d2GFP. Donor-derived primary cells were activated with αCD3/CD28 beads in the presence of rIL-2 for 3 days following which beads were removed and the cells were infected. At indicated time points, cells were collected for flow cytometry based measurement of CD25/CD69 levels and GFP expression. Data shown (in B-E) are representative of duplicate infections performed with cells from two donors. (B) Flow cytometry time course of CD25 and GFP levels taken on indicated days post infection. Dotted line indicates gating for productively infected cells (GFP+). (C–E) Histograms of cellular activation levels CD25 (C) and CD69 (D) of the entire population alongside GFP expression from productively infected cells (cells in GFP+ gate in B) over the course of 13 days post infection (17 days post cellular activation). (F–H) Cellular activation levels and GFP levels for all replicates over the experimental time course. Each dot indicates the time point from an independent infection and represents the geometric mean of the distribution as seen in C-E. Solid line connects the mean of the replicates. CD25 and CD69 normalized to day 0 (maximal); GFP normalized to day 4 when viral activity is first observed. (I) Schematic of FACS based isolation of productively infected cells. 4 days post infection, GFP+ cells were isolated and cultured (repeated for two donors). (J) Histograms of isolated GFP+ cells over time. Numbers indicate the proportion of cells that fall within the gate for positive GFP expression (marked by horizontal black bar). Day 4: Grey histogram shows the infected population prior to FACS-based separation. Viral titer was calibrated to achieve 10% infection (fraction of grey histogram that is GFP+ at day 4). Histogram in green (for Days 4, 9 and 13) shows the GFP expression in the isolated productively infected cells (post sort). All data shown above are from donor 1. See Fig. S1 for results from donor 2 and CD25 expression decline during the experiment.

Fig. 3. Computational analysis predicts that Tat positive-feedback circuitry underlays HIV autonomy to cell state

(A) Schematic of a simplified model of the Tat-feedback circuit. The LTR promoter can toggle between a state where transcriptional elongation is stalled (LTROFF) and a state where elongation proceeds (LTRON) at rates koff and kon, respectively, (Dar et al., 2012; Singh et al., 2010; Singh et al., 2012) and Tat protein transactivates the promoter by enhancing transcriptional elongation at a rate ktransact (Razooky and Weinberger, 2011). Tat protein and mRNA decay at rates δm and δp, respectively. (B) Stochastic Monte-Carlo simulations (“Gillespie” algorithm) of Tat protein levels (in arbitrary number of molecules) in individual cells over time (from reaction scheme in panel A). Each trajectory represents an individual cell; 100 single-cell trajectories shown (initial conditions for all species equal zero at time t=0, except LTRON = 1); see Extended Experimental Procedures for reaction rates. (C) Bee-Swarm plots of circuit activity (Tat levels at t=200) over a range of kon values. Each data point represents a single-cell trajectory, (200 trajectories shown per kon value). The width of the collection of cells (dots) having zero level of Tat (bottom of each kon value simulated) shows that high values of kon do generate less frequent latency (smaller number of dots). Compare for example the spread of red dots (kon=10−3) and black dots (kon=10−2) at 0 (D) Fold change in percentage of trajectories in ON state for two-fold reductions in kon. Circuit activity (%ON) is largely robust to reductions in LTR activity (i.e. kon), over three orders of magnitude. Phase-plane analysis (i.e. sensitivity analysis) from a closed-form analytical solution shows this behavior is robust across the physiological parameter regime (ktransact > kon). See also Fig. S2 and Table S1.

Fig. 4. Synthetic tuning of Tat circuit activity is sufficient to control latent HIV expression in the absence of cellular activation

(A) Schematic of the minimal LTR-Tat-Dendra-FKBP lentiviral circuit. In the absence of Shield-1, the Tat-Dendra-FKBP fusion protein is rapidly degraded, diminishing positive feedback. When Shield-1 is added, FKBP-mediated proteolysis is blocked, allowing Tat levels to increase and enabling strong Tat positive feedback. (B) Flow cytometry histograms of eight isoclonal populations of Jurkat cells infected with LTR-Tat-Dendra-FKBP in the absence of Shield-1 (light gray histograms) or the presence of 1 μM Shield-1 (dark gray histograms). Gating of the Dendra-positive region (right of black-dashed line) was set relative to naïve, un-transduced Jurkat cells. See also fig. S3 and fig. S4. (C) Schematic of the synthetic system (left) and flow cytometry data of the LTR expression in cells transduced with the synthetic circuit (right). The synthetic circuit is composed of an rTta activator constitutively expressed from an SFFV promoter. In the presence of Dox, rTta protein activates the Tet-On promoter to drive expression of the Tat-Dendra fusion protein. Tat transactivates expression from the HIV-1 LTR promoter and LTR activity is measured by mCherry expression. (D) LTR mCherry expression is shown for 11 representative isoclonal populations in the absence of Dox (light gray histograms) or after Dox addition (dark gray histograms). (E) Flow cytometry analysis of a library containing 33 distinct LTR clonal integration sites subjected to Dox and a panel of standard cell-state modifiers: TNFα, phorbol myristate acetate (PMA), PMA-ionomycin, suberanilohydroxamic acid (SAHA/vorinostat), trichostatin A (TSA), or prostratin. Error bars show standard error.

Fig. 5. Tat feedback circuitry is sufficient to control active-versus-latent infection in full-length viruses

(A) Schematic of experiment: A Jurkat cell-line where Tat-Dendra is expressed only in the presence of Dox, “Inducible-Tat Cells”, was infected with full-length ΔTat-Cherry virus in the presence (+) or absence (−) of Dox to score for latency and to score reactivation. Dox- infections were subsequently induced by Dox. (B) Percent of cells actively infected (actively expressing mCherry) two days post infection. 30% of cells were actively infected in the presence of Dox (blue), while only 7% of cells were actively infected in the absence of Dox (red). Upon subsequent Dox incubation of the Dox- infection, 28% of cells reactivated to active infection (purple), indicating virtually all latent cells can be reactivated with Tat induction. (C) Experiment schematic: CEM T cells were infected with either full-length Tat-FKBP Virus or Control Virus in the presence or absence of Shield-1. (D) Percent of cells actively infected (actively expressing Dendra) two days post infection. For the Control Virus infection, 25.8 ± 1.0% of cells exhibit active infection in the presence of 1 μM Shield-1 (blue), while 26.0 ± 2.7% exhibit active infection in the absence of Shield-1 (red). For the Tat-FKBP Virus infection, 17.5 ± 1.7% of cells exhibit active infection in the presence of 1 μM Shield-1 (blue), while 7.5 ± 1.0% of cells exhibit active infection in the absence of Shield-1 (red). Infections were performed in triplicate. Error bars = ±1 standard deviation. Control Virus infection and Tat-FKBP Virus infection are independent experiments (infection titers of the two are different). (E) Comparison of viral circuit versus cell-state activation by quantifying the percentage of delta-Tat virus infections that enter the active state. In the absence of TNFα or Dox, 2% of cells generate active HIV replication. Dox addition increases active infections to ~13%, while TNFα generates 4% actively infected cells. The same can be seen by plotting Tat expression level (Dendra). Again, TNFα by itself leaves expression level unchanged over that in absence of treatment. Addition of Dox leads to > 2-fold increase in expression. Also see Fig. S5 for the experiment repeated with Dox and a panel of cell-state modifiers.

Fig. 6. Tat feedback circuitry is sufficient to autonomously regulate viral expression during the activated-to-resting transition in primary T cells

(A) Experiment schematic: donor-derived primary CD4+ T lymphocytes were activated and infected with LTR-Tat-Dendra-FKBP in either the presence of Shield-1 (blue; wild-type feedback) or without Shield, (red; attenuated feedback) and cells were allowed to relax back to resting (as measured by CD25 surface expression) in presence/absence of Shield-1 (i.e. under wild-type/attenuated feedback). (B) Flow cytometry analysis of viral expression (Dendra fluorescence) in primary CD4+ T lymphocytes during transition from activated to resting in absence of Shield-1 (Attenuated feedback; upper panel) or presence of Shield-1 (Wild-type feedback; lower panel); activated lymphocytes shown as opaque histograms, resting lymphocytes shown as translucent histograms. (C) Plot of the fold change in the number of active infections for varying cellular state (fold change cell activation as measured by CD25 surface expression, see also Fig. S6.). If feedback strength is wild type (blue data points; blue trend line), the fold change in viral activity is uncorrelated with changing cell state. In the presence of attenuated feedback, the percentage of active infections is dependent on cell-state. Each data point is normalized against the percent of active infections in the lowest cell-state activation data point.