Protein concentration fluctuations in the high expression regime: Taylor's law and its mechanistic origin.
Protein concentration fluctuations in the high expression regime: Taylor's law and its mechanistic origin.
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高表达状态下的蛋白质浓度波动：泰勒定律及其机制起源。
DOI：
10.1103/physrevx.12.011051
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Tu,Yuhai
中科院分区：
文献类型：
--
作者：
Sassi,AlbertoStefano;Garcia-Alcala,Mayra;Aldana,Maximino;Tu,Yuhai
由于生长和分裂过程中的噪声，活细胞中的蛋白质浓度随时间波动。在高表达方案中，发现细胞中蛋白质浓度的方差与平均值的平方成比例，这属于称为泰勒定律（TL）的一般现象。为了了解这些波动的起源，我们测量蛋白质浓度动态singleEscherichia colicells从一组菌株与一个变量的荧光蛋白表达。蛋白质表达由一组具有不同强度的组成型启动子控制，这允许人们改变表达水平超过2个数量级，而不会引入来自转录调节因子波动的噪声。我们的数据证实了平方TL，但前因子具有独立于启动子强度的细胞间变异。此外，不同启动子的标准化蛋白质浓度的分布塌陷到相同的曲线上。为了解释这些观察结果，我们使用一个最小的机械模型来描述在一个单细胞的随机生长和分裂过程与调节细胞分裂的反馈机制。在高表达制度，外在噪声占主导地位，该模型再现了我们的实验结果定量。通过在极小模型中使用平均场近似，我们证明了蛋白质浓度的随机动力学是由一个带有乘性噪声的朗之万方程描述的。朗之万方程具有尺度不变性，这是平方TL的原因。通过求解朗之万方程，我们得到了与实验相符的蛋白质浓度分布函数的解析解。该解决方案明确显示了前因子如何依赖于不同噪声源的强度，这解释了其细胞间的变异性。通过使用这种方法来分析我们的单细胞数据，我们发现，生产率的噪声占主导地位的细胞分裂的噪声。在我们的模型中，通过在生产速率中包括固有噪声，也可以捕获低表达方案中与平方TL的偏差。
Protein concentration in a living cell fluctuates over time due to noise in growth and division processes. In the high expression regime, variance of the protein concentration in a cell is found to scale with the square of the mean, which belongs to a general phenomenon called Taylor’s law (TL). To understand the origin for these fluctuations, we measure protein concentration dynamics in singleEscherichia colicells from a set of strains with a variable expression of fluorescent proteins. The protein expression is controlled by a set of constitutive promoters with different strength, which allows one to change the expression level over 2 orders of magnitude without introducing noise from fluctuations in transcription regulators. Our data confirm the square TL, but the prefactor has a cell-to-cell variation independent of the promoter strength. Furthermore, distributions of the normalized protein concentration for different promoters collapse onto the same curve. To explain these observations, we use a minimal mechanistic model to describe the stochastic growth and division processes in a single cell with a feedback mechanism for regulating cell division. In the high expression regime where extrinsic noise dominates, the model reproduces our experimental results quantitatively. By using the mean-field approximation in the minimal model, we show that the stochastic dynamics of protein concentration is described by a Langevin equation with multiplicative noise. The Langevin equation has a scale invariance which is responsible for the square TL. By solving the Langevin equation, we obtain an analytical solution for the protein concentration distribution function that agrees with experiments. The solution shows explicitly how the prefactor depends on strength of different noise sources, which explains its cell-to-cell variability. By using this approach to analyze our single-cell data, we find that the noise in production rate dominates the noise from cell division. The deviation from the square TL in the low expression regime can also be captured in our model by including intrinsic noise in the production rate.
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DOI：
10.1073/pnas.90.3.1053
发表时间：
1993-02-01
影响因子：
11.1
作者：
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DOI：
--
发表时间：
2018
影响因子：
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