Non-genetic heterogeneity from stochastic partitioning at cell division.
Non-genetic heterogeneity from stochastic partitioning at cell division.
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基因表达涉及许多固有的概率步骤，这些步骤会造成细胞之间蛋白质丰度的波动。一系列深入分析和基因组规模的调查表明，这种噪音是如何通过遗传网络产生和传播的，通常正如随机模型所预期的那样，随机模型预测基因激活、转录和翻译速率方面的统计特性。但是，当分子在两个子细胞之间随机分配时，细胞分裂时也会产生噪音。在这里，我们从数学上证明了这种分配错误如何导致群体中的非遗传异质性。我们的结果表明，分区错误很难纠正，并且产生的噪声分布与基因表达噪声的分布非常相似，这使得区分两者非常困难。通过将结果应用到之前的实验研究中，并区分实际产生与仅仅传播噪音，我们令人惊讶地假设，大部分归因于基因表达各个方面的细胞间异质性实际上来自细胞分裂时的随机分离。我们还提出了区分两种类型噪声的实验，并讨论了未来的方向。
Gene expression involves many inherently probabilistic steps that create fluctuations in protein abundances between cells. A collection of in-depth analyses and genome-scale surveys have suggested how such noise arises and spreads through genetic networks, often as expected from stochastic models that predict statistical properties in terms of the rates of gene activation, transcription and translation. But noise also arises at cell division when molecules are partitioned stochastically between the two daughter cells. Here we mathematically demonstrate how such partitioning errors contribute to the non-genetic heterogeneity in a population. Our results show that partitioning errors are hard to correct, and that the resulting noise profiles closely mimic those of gene expression noise, making it remarkably difficult to separate between the two. By applying the results to previous experimental studies and distinguishing between actual creation versus mere transmission of noise we surprisingly hypothesize that much of the cell-to-cell heterogeneity that has been attributed to various aspects of gene expression instead comes from random segregation at cell division. We also propose experiments to separate between the two types of noise, and discuss future directions.