Biological Complexity Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa, Japan.
Department of Physics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
PLoS Comput Biol. 2024 Jan 5;20(1):e1011753. doi: 10.1371/journal.pcbi.1011753. eCollection 2024 Jan.
Biological cells replicate their genomes in a well-planned manner. The DNA replication program of an organism determines the timing at which different genomic regions are replicated, with fundamental consequences for cell homeostasis and genome stability. In a growing cell culture, genomic regions that are replicated early should be more abundant than regions that are replicated late. This abundance pattern can be experimentally measured using deep sequencing. However, a general quantitative theory linking this pattern to the replication program is still lacking. In this paper, we predict the abundance of DNA fragments in asynchronously growing cultures from any given stochastic model of the DNA replication program. As key examples, we present stochastic models of the DNA replication programs in budding yeast and Escherichia coli. In both cases, our model results are in excellent agreement with experimental data and permit to infer key information about the replication program. In particular, our method is able to infer the locations of known replication origins in budding yeast with high accuracy. These examples demonstrate that our method can provide insight into a broad range of organisms, from bacteria to eukaryotes.
生物细胞以一种精心规划的方式复制其基因组。生物体的 DNA 复制程序决定了不同基因组区域被复制的时间,这对细胞内稳态和基因组稳定性具有重要影响。在不断生长的细胞培养物中,早期复制的基因组区域应该比晚期复制的区域更为丰富。这种丰度模式可以使用深度测序进行实验测量。然而,将这种模式与复制程序联系起来的一般定量理论仍然缺乏。在本文中,我们从任何给定的 DNA 复制程序的随机模型中预测非同步生长培养物中 DNA 片段的丰度。作为关键示例,我们呈现了出芽酵母和大肠杆菌中 DNA 复制程序的随机模型。在这两种情况下,我们的模型结果与实验数据非常吻合,并允许推断有关复制程序的关键信息。特别是,我们的方法能够以高精度推断出出芽酵母中已知复制起点的位置。这些例子表明,我们的方法可以为从细菌到真核生物的广泛生物体提供深入的了解。