Abstract
Latent human immunodeficiency virus (HIV) infections occur when the virus occupies a transcriptionally silent but reversible state, presenting a major obstacle to cure. There is experimental evidence that random fluctuations in gene expression, when coupled to the strong positive feedback encoded by the HIV genetic circuit, act as a ‘molecular switch’ controlling cell fate, i.e., viral replication versus latency. Here, we implemented a stochastic computational modeling approach to explore how different promoter activation mechanisms in the presence of positive feedback would affect noise-driven activation from latency. We modeled the HIV promoter as existing in one, two, or three states that are representative of increasingly complex mechanisms of promoter repression underlying latency. We demonstrate that two-state and three-state models are associated with greater variability in noisy activation behaviors, and we find that Fano factor (defined as variance over mean) proves to be a useful noise metric to compare variability across model structures and parameter values. Finally, we show how three-state promoter models can be used to qualitatively describe complex reactivation phenotypes in response to therapeutic perturbations that we observe experimentally. Ultimately, our analysis suggests that multi-state models more accurately reflect observed heterogeneous reactivation and may be better suited to evaluate how noise affects viral clearance.
Highlights
Highly active anti-retroviral therapy (HAART)– remaining one of the biggest obstacles to permanent cure[12]
The HIV promoter has been put forward as a model for noisy gene expression that is influenced by its local epigenetic environment[3]. This promoter consists of a 5′ long-terminal repeat (LTR) region that contains a positioned nucleosome at the transcriptional start site, as well as binding sites for key regulators such as NF-κ B and Sp112,16
We can imagine an ‘ideal’ promoter configuration for HIV reactivation as a fully active provirus with all transcriptional machinery available in order to induce sustained viral mRNA synthesis. Such a configuration is characterized by binding of NF-κ B and Sp1 to their respective sites on the LTR, acetylated histones at the promoter, and a displaced nucleosome-1 (Nuc-1), such that the transcription start site is accessible for continuous binding and initiation of transcription by RNA polymerase II (RNAPII) (Fig. 1A)[16,22]
Summary
Highly active anti-retroviral therapy (HAART)– remaining one of the biggest obstacles to permanent cure[12]. Stochastic fluctuations in Tat expression play an essential role in the replication-versus-latency decision of proviruses because Tat is capable of powering a strong positive feedback loop that auto-stimulates its own expression 50- to 100-fold over basal levels[17,18]. These fluctuations drive phenotypic bifurcation, in which cells with low Tat and high Tat expression co-exist within clonal populations[19,20]. If noise-driven gene expression underlies viral latency, computational models that describe how regulatory mechanisms at the promoter affect heterogeneous viral activation could be used to assess treatment strategies focused on reducing or eliminating the latent reservoir. We demonstrate that the greater parameter space afforded by mathematical models of transcription containing multiple promoter states can reproduce a range of experimentally observed behaviors following virus reactivation that are indicative of the numerous biological mechanisms that maintain latent infections
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