The sequence of a promoter within a genome does not uniquely determine gene expression levels and their variability; rather, promoter sequence can additionally interact with its location in the genome, or genomic context, to shape eukaryotic gene expression. Retroviruses, such as human immunodeficiency virus-1 (HIV), integrate their genomes into those of their host and thereby provide a biomedically-relevant model system to quantitatively explore the relationship between promoter sequence, genomic context, and noise-driven variability on viral gene expression. Using an in vitro model of the HIV Tat-mediated positive-feedback loop, we previously demonstrated that fluctuations in viral Tat-transactivating protein levels generate integration-site-dependent, stochastically-driven phenotypes, in which infected cells randomly ‘switch’ between high and low expressing states in a manner that may be related to viral latency. Here we extended this model and designed a forward genetic screen to systematically identify genetic elements in the HIV LTR promoter that modulate the fraction of genomic integrations that specify ‘Switching’ phenotypes. Our screen identified mutations in core promoter regions, including Sp1 and TATA transcription factor binding sites, which increased the Switching fraction several fold. By integrating single-cell experiments with computational modeling, we further investigated the mechanism of Switching-fraction enhancement for a selected Sp1 mutation. Our experimental observations demonstrated that the Sp1 mutation both impaired Tat-transactivated expression and also altered basal expression in the absence of Tat. Computational analysis demonstrated that the observed change in basal expression could contribute significantly to the observed increase in viral integrations that specify a Switching phenotype, provided that the selected mutation affected Tat-mediated noise amplification differentially across genomic contexts. Our study thus demonstrates a methodology to identify and characterize promoter elements that affect the distribution of stochastic phenotypes over genomic contexts, and advances our understanding of how promoter mutations may control the frequency of latent HIV infection.
The sequence of a gene within a cellular genome does not uniquely determine its expression level, even for a single type of cell under fixed conditions. Numerous other factors, including gene location on the chromosome and random gene-expression “noise,” can alter expression patterns and cause differences between otherwise identical cells. This poses new challenges for characterizing the genotype–phenotype relationship. Infection by the human immunodeficiency virus-1 (HIV-1) provides a biomedically important example in which transcriptional noise and viral genomic location impact the decision between viral replication and latency, a quiescent but reversible state that cannot be eliminated by anti-viral therapies. Here, we designed a forward genetic screen to systematically identify mutations in the HIV promoter that alter the fraction of genomic integrations that specify noisy/reactivating expression phenotypes. The mechanisms by which the selected mutations specify the observed phenotypic enrichments are investigated through a combination of single-cell experiments and computational modeling. Our study provides a framework for identifying genetic sequences that alter the distribution of stochastic expression phenotypes over genomic locations and for characterizing their mechanisms of regulation. Our results also may yield further insights into the mechanisms by which HIV sequence evolution can alter the propensity for latent infections.