Mammalian protein expression noise: scaling principles and the implications for knockdown experiments
ABSTRACT The abundance of a particular protein varies both over time within a single mammalian cell and between cells of a genetically identical population. Here, we investigate the properties of such noisy protein expression in mammalian cells by combining theoretical and experimental approaches. The gamma distribution model is well-known to describe cell-to-cell variability in protein expression in a variety of common scenarios. This model predicts, and experiments show, that when protein levels are manipulated by altering transcription rates or mRNA half-life, protein expression noise, defined as the squared coefficient of variation, is constant. In contrast, we also demonstrate that when protein levels are manipulated by changing protein half-life, as mean levels increase, noise decreases. Thus, in mammalian cells, the scaling relationship between mean protein levels and expression noise depends on how mean levels are perturbed. Therefore it may be important to consider how common experimental manipulations of protein expression affect not only mean levels, but also noise levels. In the context of knockdown experiments, natural cell-to-cell variability in protein expression implies that a particular cell from the knockdown population may have higher protein levels than a cell from the control population. Simulations and experimental data suggest that approximately three-fold knockdown in mean expression levels can reduce such so-called "overlap probability" to less than ∼10%. This has implications for the interpretation of knockdown experiments when the readout is a single cell measure.
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ABSTRACT: Single-cell studies often show significant phenotypic variability due to the stochastic nature of intra-cellular biochemical reactions. When the numbers of molecules, e.g., transcription factors and regulatory enzymes, are in low abundance, fluctuations in biochemical activities become significant and such "noise" can propagate through regulatory cascades in terms of biochemical reaction networks. Here we develop an intuitive, yet fully quantitative method for analyzing how noise affects cellular phenotypes based on identifying a system's nonlinearities and noise propagations. We observe that such noise can simultaneously enhance sensitivities in one behavioral region while reducing sensitivities in another. Employing this novel phenomenon we designed three biochemical signal processing modules: (a) A gene regulatory network that acts as a concentration detector with both enhanced amplitude and sensitivity. (b) A non-cooperative positive feedback system, with a graded dose-response in the deterministic case, that serves as a bistable switch due to noise-induced ultra-sensitivity. (c) A noise-induced linear amplifier for gene regulation that requires no feedback. The methods developed in the present work allow one to understand and engineer nonlinear biochemical signal processors based on fluctuation-induced phenotypes.The Journal of Chemical Physics 10/2013; 139(14):144108. DOI:10.1063/1.4822103 · 3.12 Impact Factor
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ABSTRACT: We show theoretically and experimentally a mechanism behind the emergence of wide or bimodal protein distributions in biochemical networks with nonlinear input-output characteristics (the dose-response curve) and variability in protein abundance. Large cell-to-cell variation in the nonlinear dose-response characteristics can be beneficial to facilitate two distinct groups of response levels as opposed to a graded response. Under the circumstances that we quantify mathematically, the two distinct responses can coexist within a cellular population, leading to the emergence of a bimodal protein distribution. Using flow cytometry, we demonstrate the appearance of wide distributions in the hypoxia-inducible factor-mediated response network in HCT116 cells. With help of our theoretical framework, we perform a novel calculation of the magnitude of cell-to-cell heterogeneity in the dose-response obtained experimentally.Journal of The Royal Society Interface 09/2014; 11(98). DOI:10.1098/rsif.2014.0383 · 3.86 Impact Factor
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ABSTRACT: Cripto-1 (CR-1) is involved in various processes in embryonic development and cancer. Multiple pathways regulate CR-1 expression. Our present work demonstrates a possible positive feedback circuit where CR-1 induces its own expression. Using U-87 MG cells treated with exogenous CR-1, we show that such induction involves ALK4/SMAD2/3 pathway. Stochasticity in gene expression gives rise to heterogeneity in expression in genetically identical cells. Positive feedback increases such heterogeneity and often gives rise to two subpopulations of cells, having higher and lower expression of a gene. Using flow cytometry, we show that U-87 MG cells have a minuscule subpopulation with detectable expression of CR-1. Induction of CR-1 expression, by exogenous CR-1, increases the size of this CR-1 positive subpopulation. However, even at very high dose, most of the cells remain CR-1 negative. We show that population behavior of CR-1 induction has a signature similar to bimodal expression expected in a transcriptioPLoS ONE 02/2015; 10(2-2):e0116748. DOI:10.1371/journal.pone.0116748 · 3.53 Impact Factor