Promoters maintain their relative activity levels under different growth conditions

Keren, Leeat; Zackay, Ora; Lotan-Pompan, Maya; Barenholz, Uri; Dekel, E.; Sasson, Vered; Aidelberg, Guy; Bren, Anat; Zeevi, Danny; Weinberger, Adina; Alon, Uri; Milo, Ron; Segal, Eran

doi:10.1038/msb.2013.59

Cited by 194 publications

(288 citation statements)

References 86 publications

(283 reference statements)

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“…Harsher conditions, which support slower growth, also display lower protein abundances for most genes and, specifically, lower abundances of RNA polymerases and ribosomes (Pedersen et al 1978;Tyson et al 1979;Ingraham et al 1983; Hwa 2008, 2014;Klumpp et al 2009;Berthoumieux et al 2013;Gerosa et al 2013;Keren et al 2013). However, it is not clear whether noise levels are expected to change globally between conditions and in what direction because many of these changes may have opposing effects ( Fig.…”

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confidence: 99%

“…However, genetically identical cells exposed to the same environment display heterogeneity in gene expression (noise), with important phenotypic consequences (Grossman 1995;Rao et al 2002;Blake et al 2003;Balaban et al 2004;Colman-Lerner et al 2005;Kaern et al 2005;Balázsi et al 2011;Munsky et al 2012; Lee et al 2014). Variability in expression is anti-correlated to population average gene expression, which in turn is tightly coupled to growth rate (Tyson et al 1979;Ingraham et al 1983;Bar-Even et al 2006;Newman et al 2006;Brauer et al 2008;Klumpp et al 2009;Taniguchi et al 2010;Keren et al 2013). However, except for isolated examples (Guido et al 2007), the effects of growth conditions on expression noise have not been systematically investigated.…”

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confidence: 99%

“…Parameters affecting gene expression variability change across conditions in a coordinated manner, e.g., growth rate (Tyson et al 1979;Ingraham et al 1983), mean expression (Berthoumieux et al 2013;Gerosa et al 2013;Keren et al 2013), and concentration of RNA polymerases and ribosomes (Pedersen et al 1978;Ingraham et al 1983;Klumpp and Hwa 2008). Harsher conditions, which support slower growth, also display lower protein abundances for most genes and, specifically, lower abundances of RNA polymerases and ribosomes (Pedersen et al 1978;Tyson et al 1979;Ingraham et al 1983; Hwa 2008, 2014;Klumpp et al 2009;Berthoumieux et al 2013;Gerosa et al 2013;Keren et al 2013).…”

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confidence: 99%

“…(1) Mean-noise curve is unchanged in slow growth. (2) Slower growth is coupled with a global decrease in mean (Brauer et al 2008;Keren et al 2013). Promoters shift left along the black curve, such that the noise of each promoter is higher, but globally, the same mean expression is associated with similar noise levels in both conditions.…”

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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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Noise in gene expression is coupled to growth rate

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“…Traditional strategies have been relying on a limited number of characterized promoters to control gene expression, i.e. strong, weak, and inducible promoters [51]. As mentioned above, besides its efficient endonuclease activity, CRISPR can enable gene expression modulation through the deactivated form of the Cas9 protein, dCas9 [59,79].…”

Section: Dcas9-transcriptional Regulationmentioning

confidence: 99%

Advancing biotechnology with CRISPR/Cas9: recent applications and patent landscape

Ferreira

David

Nielsen

2018

Journal of Industrial Microbiology and Biotechnology

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Clustered regularly interspaced short palindromic repeats (CRISPR) is poised to become one of the key scientific discoveries of the twenty-first century. Originating from prokaryotic and archaeal immune systems to counter phage invasions, CRISPRbased applications have been tailored for manipulating a broad range of living organisms. From the different elucidated types of CRISPR mechanisms, the type II system adapted from Streptococcus pyogenes has been the most exploited as a tool for genome engineering and gene regulation. In this review, we describe the different applications of CRISPR/Cas9 technology in the industrial biotechnology field. Next, we detail the current status of the patent landscape, highlighting its exploitation through different companies, and conclude with future perspectives of this technology.

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Mitochondria and the non‐genetic origins of cell‐to‐cell variability: More is different

2015

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Gene expression activity is heterogeneous in a population of isogenic cells. Identifying the molecular basis of this variability will improve our understanding of phenomena like tumor resistance to drugs, virus infection, or cell fate choice. The complexity of the molecular steps and machines involved in transcription and translation could introduce sources of randomness at many levels, but a common constraint to most of these processes is its energy dependence. In eukaryotic cells, most of this energy is provided by mitochondria. A clonal population of cells may show a large variability in the number and functionality of mitochondria. Here, we discuss how differences in the mitochondrial content of each cell contribute to heterogeneity in gene products. Changes in the amount of mitochondria can also entail drastic alterations of a cell's gene expression program, which ultimately leads to phenotypic diversity. Also watch the Video Abstract.

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Promoters maintain their relative activity levels under different growth conditions

Abstract: Libraries of S. cerevisiae and E. coli promoter reporters measured under different conditions reveal scaling relationships between expression profiles across conditions and suggest that most changes in activity are due to global effects.

Cited by 194 publications

References 86 publications

Noise in gene expression is coupled to growth rate

Noise in gene expression is coupled to growth rate

Advancing biotechnology with CRISPR/Cas9: recent applications and patent landscape

Mitochondria and the non‐genetic origins of cell‐to‐cell variability: More is different

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