Transcription–replication interactions reveal bacterial genome regulation

生物 复制(统计) 遗传学 基因 基因组 计算生物学 抄写(语言学) DNA复制 细菌基因组大小 细胞生物学 DNA 病毒学 语言学 哲学
作者
Andrew W. Pountain,Peien Jiang,Tianyou Yao,Ehsan Homaee,Yichao Guan,K McDonald,Magdalena Podkowik,Bo Shopsin,Victor J. Torres,Ido Golding,Itai Yanai
出处
期刊:Nature [Nature Portfolio]
卷期号:626 (7999): 661-669 被引量:9
标识
DOI:10.1038/s41586-023-06974-w
摘要

Organisms determine the transcription rates of thousands of genes through a few modes of regulation that recur across the genome1. In bacteria, the relationship between the regulatory architecture of a gene and its expression is well understood for individual model gene circuits2,3. 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 cells4. 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. Single-cell expression data from bacteria are used to classify gene regulatory architectures in relation to gene expression dynamics and the cell cycle, revealing distinct categories of gene regulatory mechanisms.
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