title: Origin and consequences of the relationship between protein mean and variance.
abstract: Cell-to-cell variance in protein levels (noise) is a ubiquitous phenomenon that can increase fitness by generating phenotypic differences within clonal populations of cells. An important challenge is to identify the specific molecular events that control noise. This task is complicated by the strong dependence of a protein's cell-to-cell variance on its mean expression level through a power-law like relationship (σ2∝μ1.69). Here, we dissect the nature of this relationship using a stochastic model parameterized with experimentally measured values. This framework naturally recapitulates the power-law like relationship (σ2∝μ1.6) and accurately predicts protein variance across the yeast proteome (r2 = 0.935). Using this model we identified two distinct mechanisms by which protein variance can be increased. Variables that affect promoter activation, such as nucleosome positioning, increase protein variance by changing the exponent of the power-law relationship. In contrast, variables that affect processes downstream of promoter activation, such as mRNA and protein synthesis, increase protein variance in a mean-dependent manner following the power-law. We verified our findings experimentally using an inducible gene expression system in yeast. We conclude that the power-law-like relationship between noise and protein mean is due to the kinetics of promoter activation. Our results provide a framework for understanding how molecular processes shape stochastic variation across the genome.
authors: Center for Genome Sciences and Systems Biology, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America; Program in Computational and Systems Biology, Washington University School of Medicine, St. Louis, Missouri, United States of America.
authors: Francesco Luigi Massimo
authors: FL
authors: Vallania
authors: Center for Genome Sciences and Systems Biology, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America; Program in Computational and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri, United States of America.
authors: Marc
authors: M
authors: Sherman
authors: Center for Genome Sciences and Systems Biology, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America; Program in Computational and Systems Biology, Washington University School of Medicine, St. Louis, Missouri, United States of America.
authors: Zane
authors: Z
authors: Goodwin
authors: Center for Genome Sciences and Systems Biology, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America.
authors: Ilaria
authors: I
authors: Mogno
authors: Center for Genome Sciences and Systems Biology, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America.
authors: Barak Alon
authors: BA
authors: Cohen
authors: Center for Genome Sciences and Systems Biology, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America.
authors: Robi David
authors: RD
authors: Mitra
mesh_terms: D015966:Gene Expression Regulation, Fungal / Q000235:genetics; D056084:Genetic Fitness; D008962:Models, Theoretical; D011401:Promoter Regions, Genetic; D014176:Protein Biosynthesis / Q000235:genetics*; D020543:Proteome / Q000378:metabolism; D012333:RNA, Messenger / Q000096:biosynthesis / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics*
chemical_list: D020543:Proteome; D012333:RNA, Messenger