Screening for noise in gene expression identifies drug synergies

Dar, Roy D.; Hosmane, Nina N.; Arkin, Michelle R.; Siliciano, Robert F.; Weinberger, Leor S.

doi:10.1126/science.1250220

Cited by 209 publications

(329 citation statements)

References 49 publications

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“…Cold Spring Harbor Laboratory Press on May 11, 2018 -Published by genome.cshlp.org Downloaded from promising example in HIV treatment with drugs that modulate expression noise (Dar et al 2014), our results suggest that similar approaches may be meaningful in cancer.…”

Section: Wwwgenomeorgmentioning

confidence: 66%

“…Since cell-to-cell variability in gene expression underlies important phenotypic phenomena such as persistence (Balaban et al 2004), competence (Maamar et al 2007), latency (Dar et al 2014), metastasis (Lee et al 2014), responsiveness to fluctuating environments (Acar et al 2005(Acar et al , 2008Blake et al 2006;Kaufmann et al 2007;Levy et al 2012;Vardi et al 2013), and triggering of meiosis (Nachman et al 2007), our results suggest that the probability and efficiency of these processes will be tightly coupled to environmental conditions, with potential evolutionary implications. Gene expression noise is higher at lower growth rates.…”

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confidence: 90%

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Noise in gene expression is coupled to growth rate

et al. 2015

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Genetically identical cells exposed to the same environment display variability in gene expression (noise), with important consequences for the fidelity of cellular regulation and biological function. Although population average gene expression is tightly coupled to growth rate, the effects of changes in environmental conditions on expression variability are not known. Here, we measure the single-cell expression distributions of approximately 900 Saccharomyces cerevisiae promoters across four environmental conditions using flow cytometry, and find that gene expression noise is tightly coupled to the environment and is generally higher at lower growth rates. Nutrient-poor conditions, which support lower growth rates, display elevated levels of noise for most promoters, regardless of their specific expression values. We present a simple model of noise in expression that results from having an asynchronous population, with cells at different cell-cycle stages, and with different partitioning of the cells between the stages at different growth rates. This model predicts non-monotonic global changes in noise at different growth rates as well as overall higher variability in expression for cell-cycle-regulated genes in all conditions. The consistency between this model and our data, as well as with noise measurements of cells growing in a chemostat at well-defined growth rates, suggests that cell-cycle heterogeneity is a major contributor to gene expression noise. Finally, we identify gene and promoter features that play a role in gene expression noise across conditions. Our results show the existence of growth-related global changes in gene expression noise and suggest their potential phenotypic implications.

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Section: Wwwgenomeorgmentioning

confidence: 66%

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confidence: 90%

Noise in gene expression is coupled to growth rate

et al. 2015

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“…For example, some compounds subvert variability in HIV populations. 18 . In M. tuberculosis , targeting LamA could lead to synergistic therapies that shorten treatment duration and increase the rate of cure.…”

Section: Main Textmentioning

confidence: 99%

Deletion of a mycobacterial divisome factor collapses single-cell phenotypic heterogeneity

Rego

Audette

Rubín

2017

Nature

163

177

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Summary Paragraph While microorganisms are often studied as populations, the behavior of single, individual cells can have profound consequences. For example, tuberculosis, caused by the bacterial pathogen Mycobacterium tuberculosis, requires months of antibiotic therapy even though the bulk of the bacterial population rapidly dies. Shorter courses lead to high rates of relapse because subpopulations of bacilli can survive despite being genetically identical to those that are easily killed 1. In fact, mycobacteria create variability every time a cell divides, producing daughter cells with different sizes and growth rates 2, 3. The mechanism(s) that underlie this high-frequency variation and how variability relates to survival of the population are unknown. Here we show that mycobacteria actively create heterogeneity. Using a fluorescent reporter and a FACS-based transposon screen, we find that deletion of lamA, a gene of previously unknown function, decreases the amount of heterogeneity in the population by decreasing asymmetric polar growth. LamA has no known homologs in other organisms, but is highly conserved across mycobacterial species. We find that LamA is a member of the mycobacterial division complex (“the divisome”). It inhibits growth at nascent new poles, creating asymmetry in polar growth. The kinetics of killing individual cells that lack lamA are more uniform and more rapid with rifampicin and certain drugs that target the cell wall. Our results show that mycobacteria encode a non-conserved protein that controls the pattern of cell growth, resulting in a population that is both heterogeneous and better able to survive antibiotic pressure.

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“…This principle was illustrated in a recent study that screened for compounds altering the expression noise of the HIV LTR promoter; the authors found that, in combination with T-cell-activating compounds, noiseenhancing compounds could successfully reactivate HIV in up to 50% of resting T cells (Dar et al, 2014).…”

Section: Intercellular Heterogeneity As a Key Challenge In Medicinementioning

confidence: 99%

Organelles – understanding noise and heterogeneity in cell biology at an intermediate scale

Chang

Marshall

2017

Journal of Cell Science

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Many studies over the years have shown that non-genetic mechanisms for producing cell-to-cell variation can lead to highly variable behaviors across genetically identical populations of cells. Most work to date has focused on gene expression noise as the primary source of phenotypic heterogeneity, yet other sources may also contribute. In this Commentary, we explore organelle-level heterogeneity as a potential secondary source of cellular 'noise' that contributes to phenotypic heterogeneity. We explore mechanisms for generating organelle heterogeneity and present evidence of functional links between organelle morphology and cellular behavior. Given the many instances in which molecular-level heterogeneity has been linked to phenotypic heterogeneity, we posit that organelle heterogeneity may similarly contribute to overall phenotypic heterogeneity and underline the importance of studying organelle heterogeneity to develop a more comprehensive understanding of phenotypic heterogeneity. Finally, we conclude with a discussion of the medical challenges associated with phenotypic heterogeneity and outline how improved methods for characterizing and controlling this heterogeneity may lead to improved therapeutic strategies and outcomes for patients.

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Screening for noise in gene expression identifies drug synergies

Cited by 209 publications

References 49 publications

Noise in gene expression is coupled to growth rate

Noise in gene expression is coupled to growth rate

Deletion of a mycobacterial divisome factor collapses single-cell phenotypic heterogeneity

Organelles – understanding noise and heterogeneity in cell biology at an intermediate scale

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