Pountain Andrew W, Jiang Peien, Yao Tianyou, Homaee Ehsan, Guan Yichao, McDonald Kevin J C, Podkowik Magdalena, Shopsin Bo, Torres Victor J, Golding Ido, Yanai Itai
Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA.
Department of Biology, New York University, New York, NY, USA.
Nature. 2024 Feb;626(7999):661-669. doi: 10.1038/s41586-023-06974-w. Epub 2024 Jan 24.
Organisms determine the transcription rates of thousands of genes through a few modes of regulation that recur across the genome. In bacteria, the relationship between the regulatory architecture of a gene and its expression is well understood for individual model gene circuits. However, a broader perspective of these dynamics at the genome scale is lacking, in part because bacterial transcriptomics has hitherto captured only a static snapshot of expression averaged across millions of cells. As a result, the full diversity of gene expression dynamics and their relation to regulatory architecture remains unknown. Here we present a novel genome-wide classification of regulatory modes based on the transcriptional response of each gene to its own replication, which we term the transcription-replication interaction profile (TRIP). Analysing single-bacterium RNA-sequencing data, we found that the response to the universal perturbation of chromosomal replication integrates biological regulatory factors with biophysical molecular events on the chromosome to reveal the local regulatory context of a gene. Whereas the TRIPs of many genes conform to a gene dosage-dependent pattern, others diverge in distinct ways, and this is shaped by factors such as intra-operon position and repression state. By revealing the underlying mechanistic drivers of gene expression heterogeneity, this work provides a quantitative, biophysical framework for modelling replication-dependent expression dynamics.
生物体通过在全基因组中反复出现的几种调控模式来决定数千个基因的转录速率。在细菌中,对于单个模型基因回路,基因的调控结构与其表达之间的关系已得到充分理解。然而,在基因组尺度上对这些动态变化缺乏更广泛的认识,部分原因是细菌转录组学迄今仅捕捉到了数百万个细胞平均表达水平的静态快照。因此,基因表达动态变化的全部多样性及其与调控结构的关系仍然未知。在这里,我们基于每个基因对自身复制的转录反应,提出了一种全新的全基因组调控模式分类方法,我们将其称为转录 - 复制相互作用图谱(TRIP)。通过分析单细菌RNA测序数据,我们发现对染色体复制这一普遍扰动的反应将生物调控因子与染色体上的生物物理分子事件整合在一起,从而揭示了基因的局部调控环境。虽然许多基因的TRIP符合基因剂量依赖性模式,但其他基因则以不同方式偏离,这受到操纵子内位置和抑制状态等因素的影响。通过揭示基因表达异质性的潜在机制驱动因素,这项工作为模拟复制依赖性表达动态提供了一个定量的生物物理框架。