Okamoto Shotaro, Negishi Kohei, Toyama Yuko, Ushijima Takeo, Morohashi Kengo
Department of Applied Biological Science, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba 278-8510, Japan.
Department of Mathematics, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba 278-8510, Japan.
Plants (Basel). 2020 Jul 17;9(7):909. doi: 10.3390/plants9070909.
Gene expression varies stochastically even in both heterogenous and homogeneous cell populations. This variation is not simply useless noise; rather, it is important for many biological processes. Unicellular organisms or cultured cell lines are useful for analyzing the variation in gene expression between cells; however, owing to technical challenges, the biological relevance of this variation in multicellular organisms such as higher plants remain unclear. Here, we addressed the biological relevance of this variation between cells by examining the genetic basis of trichome distribution patterns in . The distribution pattern of a trichome on a leaf is stochastic and can be mathematically represented using Turing's reaction-diffusion (RD) model. We analyzed simulations based on the RD model and found that the variability in the trichome distribution pattern increased with the increase in stochastic variation in a particular gene expression. Moreover, differences in heat-dependent variability of the trichome distribution pattern between the accessions showed a strong correlation with environmental factors to which each accession was adapted. Taken together, we successfully visualized variations in gene expression by quantifying the variability in the trichome distribution pattern. Thus, our data provide evidence for the biological importance of variations in gene expression for environmental adaptation.
即使在异质和同质细胞群体中,基因表达也会随机变化。这种变化并非仅仅是无用的噪音;相反,它对许多生物学过程都很重要。单细胞生物或培养的细胞系对于分析细胞间基因表达的变化很有用;然而,由于技术挑战,这种变化在多细胞生物(如高等植物)中的生物学相关性仍不清楚。在这里,我们通过研究拟南芥叶毛分布模式的遗传基础,探讨了细胞间这种变化的生物学相关性。叶片上叶毛的分布模式是随机的,可以用图灵反应扩散(RD)模型进行数学表示。我们分析了基于RD模型的模拟,发现叶毛分布模式的变异性随着特定基因表达中随机变化的增加而增加。此外,不同生态型之间叶毛分布模式的热依赖性变异性差异与每个生态型所适应的环境因素密切相关。综上所述,我们通过量化叶毛分布模式的变异性,成功地可视化了基因表达中的变化。因此,我们的数据为基因表达变化对环境适应的生物学重要性提供了证据。