蛋白质表达中的噪音与可塑性之间的一致性

Saburo Tsuru, Chikara Furusawa
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引用次数: 0

摘要

基因表达会对突变、环境变化和随机扰动等各种扰动做出反应。基因表达水平的变异性因基因而异,影响适应性变体或突变体的可用性,从而影响非遗传和遗传适应性。不同类型的变异是相互依存的,这表明针对不同扰动的全球运河化/解运河化以及一种共同的潜在机制。尽管如此,基因表达的可塑性(对环境变化做出反应的变异性)与噪声(相同条件下细胞间的变异性)之间的关系仍然值得商榷。之前的研究报告称,可塑性和噪声之间存在正相关,但这些变异性通常是在不同水平上测量的:可塑性在 mRNA 水平,噪声在蛋白质水平。这种方法上的差异使得对它们之间关系的理解变得复杂。我们通过测量大肠杆菌中必需基因和非必需基因的蛋白质表达水平来研究这一问题。利用流式细胞术,我们对同一数据集的噪声和可塑性进行了量化。与非基本基因相比,基本基因表现出较低的噪声和可塑性。非必要基因的噪声和可塑性呈正相关,而必需基因则不然。这项研究提供了蛋白质表达中噪声和可塑性之间依赖于本质的耦合的经验证据,突出了不同类型变异的组织结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Congruence between noise and plasticity in protein expression
Gene expression responds to various perturbations, such as mutations, environmental changes, and stochastic perturbations. The variability in gene expression levels differs among genes, influencing the availability of adaptive variants or mutants and thereby affecting nongenetic and genetic adaptations. Different types of variability are interdependent, suggesting global canalization/decanalization against different perturbations and a common underlying mechanism. Despite this, the relationship between plasticity (variability in response to environmental changes) and noise (variability among cells under the same conditions) in gene expression remains debatable. Previous studies reported a positive correlation between plasticity and noise, but these variabilities are often measured at different levels: plasticity at the mRNA level and noise at the protein level. This methodological discrepancy complicates the understanding of their relationship. We investigated this by measuring protein expression levels of essential and nonessential genes in Escherichia coli. Using flow cytometry, we quantified noise and plasticity from the same dataset. Essential genes exhibited lower noise and plasticity than nonessential genes. Nonessential genes showed a positive correlation between noise and plasticity, while essential genes did not. This study provides empirical evidence of essentiality-dependent coupling between noise and plasticity in protein expression, highlighting the organization of different types of variabilities.
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