Screening for noise in gene expression identifies drug synergies

Abstract

Stochastic fluctuations are inherent to gene expression and can drive cell-fate specification. We used such fluctuations to modulate reactivation of HIV from latency-a quiescent state that is a major barrier to an HIV cure. By screening a diverse library of bioactive small molecules, we identified more than 80 compounds that modulated HIV gene-expression fluctuations (i.e., "noise"), without changing mean expression. These noise-modulating compounds would be neglected in conventional screens, and yet, they synergized with conventional transcriptional activators. Noise enhancers reactivated latent cells significantly better than existing best-in-class reactivation drug combinations (and with reduced off-target cytotoxicity), whereas noise suppressors stabilized latency. Noise-modulating chemicals may provide novel probes for the physiological consequences of noise and an unexplored axis for drug discovery, allowing enhanced control over diverse cell-fate decisions.

Figure 1. Modulating noise in gene expression to enhance threshold crossing

A. Schematic of HIV active replication, proviral latency, and latent reactivation. Inset: The canonical two-state model of transcription for the HIV LTR promoter. The LTR transitions between an ‘off’ state and a transcriptionally permissive ‘on’ state at rates k_on (upward arrow) and k_off (downward arrow), respectively, and transcribes with rate k_m (horizontal arrow) in the on state. Conventional ‘activators’ of LTR (blue) modulate k_on to increase expression rate, whereas adjusting k_m/k_off (purple) primarily modulates expression noise. B. Stochastic simulations of the two-state model for 200 single-cells (black trajectories, upper-left). Increasing k_on (i.e. an “activator”) increases mean expression (blue trajectories, lower-left). Reducing k_off, with decreased k_on (i.e. a pure “noise enhancer”), only changes the noise (purple trajectories, upper-right). Combining a noise enhancer with an activator generates synergy with more trajectories entering into active replication (red trajectories, lower-right). Lowered insets: Promoter activity versus time without treatment, and after treatment with an activator, noise enhancer, or a combination of both. C. Conventional noise-versus-mean plots for quantitative comparison of noise levels (CV²). Theoretical lines of constant burst-size (i.e. k_m / k_off = constant) decrease with mean. Activators increase k_on to increase mean expression along lines of constant burst size (left). Noise enhancers transfer the system to a new higher burst-size line (right). Combining an activator with a noise enhancer results in more frequent transcriptional bursts that also have larger burst size, generating more mRNA and surpassing levels achieved by activator alone. D. Percentage of trajectories in panel C that pass into the active-replication region during the simulation.

Figure 2. Screening for compounds that modulate noise in HIV promoter expression

A. Flow cytometry measurements of LTR-d₂GFP isoclonal cells exposed to 1600 small molecules (in 96-well plate format) for 24 h. Each point represents the mean fluorescence and intrinsic noise (CV²) for ~50,000 live cells under stringent forward-versus-side scatter gating (28). Each color represents a measurement cluster of compounds screened on a single day. Untreated controls (black squares) vary along the constant-burst-size line (diagonal blue line); error bars calculated from standard deviation for 28 measurements. TNF was used as a positive control for each measurement cluster (blue diamond). Noise-modulating compounds increased, or suppressed, noise by at least ±2σ from the constant burst-size line. B. Change in noise and mean for 85 transcriptional noise enhancers identified by secondary filtering through the two-reporter assay (fig. S1). C. Noise and mean changes for transcriptional modulators of the LTR. Transcriptional activators increased mean expression whereas chromatin modifiers increase expression noise with minimal increases in expression mean.

Figure 3. Synergy of noise enhancers with conventional activators to amplify reactivation of full-length latent HIV

A. Percent reactivation of latently infected J-lat cells 24 h after treatment with either TNF alone (solid black bar, upper) or Prostratin alone (solid black bar, lower), or one of the 85 noise enhancers alone (purple bars), or in combination with TNF (red, upper) or Prostratin (red, lower). Synergy (Mean Bliss Score) is calculated relative to TNF-only or Prostratin-only control. The majority of noise enhancers synergize with TNF and Prostratin to enhance latent reactivation. Black arrows identify noise enhancers analyzed in Fig. 4B. The asterisk indicates noise enhancers that increase reactivation but without significant synergy. B. Correlation between a compound’s noise enhancement (from Fig. 2B) and its reactivation synergy with TNF (from panel A). Each data point represents a moving average of 10 compounds. C. Left: Representative flow cytometry histogram for reactivation with TNF + noise enhancer cocktail (red) compared to TNF alone (blue). Noise enhancers amplify mean expression when used in conjunction with an activator (black arrow). Right: For all the noise enhancer + TNF combinations that synergized, the mean GFP positively correlates with reactivation %.

Figure 4. Noise-enhancer cocktails improve HIV reactivation with reduced toxicity and function in primary cells; noise-suppressor cocktails limit reactivation in both cell lines and primary cells

A. Dose-response optimization for noise-enhancer compound V11 in combination with various concentrations of TNF (left) and Prostratin (right) on J-lat cells after 48 h treatment. B. Reactivation percentage versus drug toxicity for conventional reactivation cocktails (e.g. TNF + Prostratin, SAHA + Prostratin) and 21 cocktails containing noise-enhancer compounds with TNF + Prostratin. Measurements were performed in duplicate on J-lat cell line 8.6 after 48 h treatment (28). Viability measurements in uninfected cells indicate that reduced viability resulted from chemical cytotoxicity and not viral reactivation (fig. S10). C. Primary T-cell model of HIV latency 48 h after reactivation with PMA or a subset of noise enhancers [% reactivation percentage is calculated relative to maximal reactivation potential (28)]. Maximal increased activation was observed with PMA and V7 by ~20% or half the available range. D. Stochastic simulations of noise suppression, which reduces noise without altering the mean level but limits reactivation induced by an activator. E. Noise-suppressor molecule S1 decreased TNF-induced reactivation by ~40% in two J-lat cell lines (upper = J-Lat 15.4, lower = J-Lat 8.6), and decreased Prostratin-induced reactivation by ~50% in the primary T-cell model (right).