Efficient Attenuation of Stochasticity in Gene Expression Through Post-transcriptional Control

Swain, Peter S.

doi:10.1016/j.jmb.2004.09.073

Cited by 206 publications

(234 citation statements)

References 33 publications

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“…Noise in expression may be of developmental or mutational origin (McAdams and Arkin 1997;Elowitz et al 2002;Ozbudak et al 2002). Developmental noise is predicted to be counterselected in a stable environment (Swain 2004;Raser and O'Shea 2005;Lehner 2008), although possibly not under unpredictable and/or stressful conditions (Thattai and Van Oudenaarden 2004;Acar et al 2008;Ratcliff and Denison 2010). Mutational noise stems from the constant input of random genetic changes and is mostly deleterious (Eyre-Walker and Keightley 2007).…”

Section: Previous Modelsmentioning

confidence: 99%

Gene Functional Trade-Offs and the Evolution of Pleiotropy

Guillaume¹,

Otto

2012

Genetics

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Pleiotropy is the property of genes affecting multiple functions or characters of an organism. Genes vary widely in their degree of pleiotropy, but this variation is often considered a by-product of their evolutionary history. We present a functional theory of how pleiotropy may itself evolve. We consider genes that contribute to two functions, where contributing more to one function detracts from allocation to the second function. We show that whether genes become pleiotropic or specialize on a single function depends on the nature of trade-offs as gene activities contribute to different traits and on how the functionality of these traits affects fitness. In general, when a gene product can perform well at two functions, it evolves to do so, but not when pleiotropy would greatly disrupt each function. Consequently, reduced pleiotropy should often evolve, with genes specializing on the trait that is currently more important to fitness. Even when pleiotropy does evolve, not all genes are expected to become equally pleiotropic; genes with higher levels of expression are more likely to evolve greater pleiotropy. For the case of gene duplicates, we find that perfect subfunctionalization evolves only under stringent conditions. More often, duplicates are expected to maintain a certain degree of functional redundancy, with the gene contributing more to trait functionality evolving the highest degree of pleiotropy. Gene product interactions can facilitate subfunctionalization, but whether they do so depends on the curvature of the fitness surface. Finally, we find that stochastic gene expression favors pleiotropy by selecting for robustness in fitness components. PLEIOTROPY is the property whereby a gene affects more than one function or phenotypic character of an organism. Gene-knockout studies in yeast indicate that deleting genes with higher degrees of pleiotropy has, on average, a more harmful effect on fitness (Salathé et al. 2006; Cooper et al. 2007). This negative relationship with fitness is expected given that most mutational changes are deleterious so that the more characters are affected by a mutation, the more likely the net effect on fitness is harmful, even if the mutation is beneficial for a subset of characters. This claim has been verified in theoretical studies based on Fisher's geometrical model (Chevin et al. 2010;Lourenço et al. 2011). Pleiotropy is consequently seen as a constraint on evolution because it reduces the adaptive capacity of an organism (Orr 2000;Welch and Waxman 2003).Recent observations in a variety of species have found that the extent of pleiotropy varies among genes and is often limited, with a majority of genes influencing a small set of traits while a few genes affect many traits (Dudley et al. 2005; Albert et al. 2008;Wagner et al. 2008;Wang et al. 2010;Wagner and Zhang 2011). This is in direct opposition to the historical assumption of universal pleiotropy underlying most population and quantitative genetics approaches to the joint evolution of multiple characters (...

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Section: Previous Modelsmentioning

confidence: 99%

Gene Functional Trade-Offs and the Evolution of Pleiotropy

Guillaume¹,

Otto

2012

Genetics

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“…Swain has presented an analysis of the chemical Langevin equations for, effectively, a general linear reaction network [28]. He obtains general results for the correlation functions and the variance-covariance matrix in terms of the eigenvalues of K ij .…”

Section: Chemical Langevin Equationsmentioning

confidence: 99%

Exact results for noise power spectra in linear biochemical reaction networks

Warren

Tănase‐Nicola

Wolde

2006

The Journal of Chemical Physics

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“…Approximately 40% of known transcription factors in Escherichia coli are subject to negative transcriptional autoregulation (Thieffry et al, 1998;Rosenfeld et al, 2002). Negative feedback control is typically considered as an important means of repressing the noise level in gene expression by suppressing the variability of the protein level across cells (Savageau, 1974;Becskei and Serrano, 2000;Simpson et al, 2003;Swain, 2004;Boyer et al, 2005;Tao et al, 2007). However, introducing negative feedback without increasing transcription or translation rates can amplify the intrinsic noise level, as negative feedback causes a decrease in the average number of mRNAs and proteins, which results in an increase in the protein variability (Shahrezaei et al, 2008;Singh and Hespanha, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…It is known that both negative feedback and compartmentalisation can reduce gene expression noise (Savageau, 1974;Becskei and Serrano, 2000;Simpson et al, 2003;Swain, 2004;Boyer et al, 2005;Tao et al, 2007;Singh and Bokes, 2012;Bahar Halpern et al, 2015;Battich et al, 2015). The central question we sought to address in this paper was: what effect does the combination of both negative feedback and compartmentalisation have on gene expression noise?…”

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confidence: 99%

The influence of nuclear compartmentalisation on stochastic dynamics of self-repressing gene expression

Sturrock

Shahrezaei

2017

Journal of Theoretical Biology

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Gene expression is an inherently noisy process. This noise is generally thought to be deleterious as precise internal regulation of biochemical reactions is essential for cell growth and survival. Selfrepression of gene expression, which is the simplest form of a negative feedback loop, is commonly believed to be employed by cellular systems to decrease the stochastic fluctuations in gene expression. When there is some delay in autoregulation, it is also believed that this system can generate oscillations. In eukaryotic cells, mRNAs that are synthesised in the nucleus must be exported to the cytoplasm to function in protein synthesis, whereas proteins must be transported into the nucleus from the cytoplasm to regulate the expression levels of genes. Nuclear transport thus plays a critical role in eukaryotic gene expression and regulation. Some recent studies have suggested that nuclear retention of mRNAs can control noise in mRNA expression. However, the effect of nuclear transport on protein noise and its interplay with negative feedback regulation is not completely understood. In this paper, we systematically compare four different simple models of gene expression. By using simulations and applying the linear noise approximation to the corresponding chemical master equations, we investigate the influence of nuclear import and export on noise in gene expression in a negative autoregulatory feedback loop. We first present results consistent with the literature, i.e., that negative feedback can effectively buffer the variability in protein levels, and nuclear retention can decrease mRNA noise levels. Interestingly we find that when negative feedback is combined with nuclear retention, an amplification in gene expression noise can be observed and is dependant on nuclear translocation rates. Finally, we investigate the effect of nuclear compartmentalisation on the ability of self-repressing genes to exhibit stochastic oscillatory dynamics.

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Efficient Attenuation of Stochasticity in Gene Expression Through Post-transcriptional Control

Cited by 206 publications

References 33 publications

Gene Functional Trade-Offs and the Evolution of Pleiotropy

Gene Functional Trade-Offs and the Evolution of Pleiotropy

Exact results for noise power spectra in linear biochemical reaction networks

The influence of nuclear compartmentalisation on stochastic dynamics of self-repressing gene expression

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