Noise in gene expression is coupled to growth rate.
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.
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, Israel;
Leeat
L
Keren
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Biological Sciences, Department of Systems Biology, Columbia University, New York, New York 10027, USA;
David
D
van Dijk
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel;
Shira
S
Weingarten-Gabbay
Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, Israel;
Dan
D
Davidi
Biological Services Unit, Weizmann Institute of Science, Rehovot 76100, Israel.
Ghil
G
Jona
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel;
Adina
A
Weinberger
Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, Israel;
Ron
R
Milo
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel; Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel;
Eran
E
Segal
D002453:Cell Cycle / Q000235:genetics; D004777:Environment; D015870:Gene Expression; D015966:Gene Expression Regulation, Fungal; D059647:Gene-Environment Interaction; D008954:Models, Biological; D011401:Promoter Regions, Genetic; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000254:growth & development* / Q000378:metabolism; D059010:Single-Cell Analysis; D015533:Transcriptional Activation