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Transient thresholding in the HIV Tat fate-selection circuit


Human Immunodeficiency Virus type 1 (HIV) is a lentivirus that infects CD4+ T cells, causing progressive immune dysfunction (AIDS). Although effective treatments exist, HIV is able to form a ‘latent reservoir’ in long-lived memory immune cells that is transcriptionally silent and invisible to both the immune system and conventional antivirals. The HIV fate decision between latency and replication is controlled by the sole HIV promoter, LTR, and its gene product and non-cooperative monomeric activator, Tat. It remains unclear how the Tat-LTR positive-feedback circuit generates an activation threshold to prevent ‘leaky’ Tat expression from transactivating the LTR, thereby enabling maintenance of the latent state. Threshold generation in gene regulatory networks (GRNs) is typically achieved through deterministic bistability, which is absent here. Without some form of non-linear activation (e.g. self-cooperativity), expression noise should trigger runaway Tat feedback and thus HIV replication. Despite this, HIV latency is apparently stable, even under strong activating conditions. Using flow cytometry and single-cell imaging, I find that HIV LTR exhibits a transient threshold in response to Tat. The molecular threshold at early times is ~40,000 Tat proteins per cell, but largely disappears after 40 hours, explaining the lack of bistability and hysteresis. Further, I demonstrate that slow ‘toggling’ between active and inactive promoter states can generate an activation threshold without cooperativity. Cellular signaling can modulate toggling frequency and thereby adjust this threshold. These results indicate a potential role for promoter toggling as a mechanism for tunable threshold generation in GRNs. Finally, I propose a class of general stochastic models for multi-step transactivation of a toggling promoter, and argue for its relevance to LTR, which is known to exhibit intrinsic bursts of transcription at multiple time scales. This work may advance the predictive modeling of Tat-LTR and similar GRNs in higher eukaryotes.

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