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

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      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.

      Author Summary

      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.

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            Author and article information

            [1 ]Department of Biomedical Engineering, Yale University, New Haven, Connecticut, United States of America
            [2 ]California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, California, United States of America
            [3 ]Department of Bioengineering, University of California, Berkeley, California, United States of America
            [4 ]Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
            [5 ]Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California, United States of America
            Stanford University, United States of America
            Author notes

            The authors have declared that no competing interests exist.

            Conceived and designed the experiments: KMJ RS APA DVS. Performed the experiments: KMJ RS PSS. Analyzed the data: KMJ RS. Wrote the paper: KMJ RS APA DVS.


            Current address: Department of Mathematics and Statistics, University of Southern Maine, Portland, Maine, United States of America

            Role: Editor
            PLoS Comput Biol
            PLoS Comput. Biol
            PLoS Computational Biology
            Public Library of Science (San Francisco, USA )
            July 2013
            July 2013
            11 July 2013
            : 9
            : 7
            23874178 3708878 PCOMPBIOL-D-12-02006 10.1371/journal.pcbi.1003135

            This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

            Pages: 15
            This work was funded by NIH 2 R01 GM073058 (to DVS and APA) and by NIH 1 F32 AI072996 (to KMJ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
            Research Article
            Computational Biology
            Biochemical Simulations
            Gene Expression
            Gene Networks
            Genetic Screens
            Viral Replication
            Viral Latency
            Immunodeficiency Viruses
            Systems Biology

            Quantitative & Systems biology


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