Genetic Selection for Context-Dependent Stochastic Phenotypes: Sp1 and TATA Mutations Increase Phenotypic Noise in HIV-1 Gene Expression

Miller-Jensen, Kathryn; Skupsky, Ron; Shah, Priya S.; Arkin, Adam P.; Schaffer, David V.

doi:10.1371/journal.pcbi.1003135

Title: Genetic Selection for Context-Dependent Stochastic Phenotypes: Sp1 and TATA Mutations Increase Phenotypic Noise in HIV-1 Gene Expression

Journal Article · Thu Jul 11 00:00:00 EDT 2013 · PLoS Computational Biology (Online)

DOI:https://doi.org/10.1371/journal.pcbi.1003135· OSTI ID:1627231

Miller-Jensen, Kathryn ^[1]; Skupsky, Ron ^[2]; Shah, Priya S. ^[2]; Arkin, Adam P. ^[3]; Schaffer, David V. ^[4]

Yale Univ., New Haven, CT (United States). Dept. of Biomedical Engineering; Univ. of California, Berkeley, CA (United States). California Inst. for Quantitative Biosciences
Univ. of California, Berkeley, CA (United States). California Inst. for Quantitative Biosciences
Univ. of California, Berkeley, CA (United States). California Inst. for Quantitative Biosciences; Univ. of California, Berkeley, CA (United States). Dept. of Bioengineering; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Physical Biosciences Division
Univ. of California, Berkeley, CA (United States). California Inst. for Quantitative Biosciences; Univ. of California, Berkeley, CA (United States). Dept. of Bioengineering; Univ. of California, Berkeley, CA (United States). Dept. of Chemical and Biomolecular Engineering

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 Tattransactivating 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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division; National Institutes of Health (NIH)

Grant/Contract Number:: AC02-05CH11231; R01 GM073058; F32 AI072996

OSTI ID:: 1627231

Journal Information:: PLoS Computational Biology (Online), Vol. 9, Issue 7; ISSN 1553-7358

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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