Transcription-replication interactions reveal bacterial genome regulation

• 成果类型: Article
• 署名作者: 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
• 署名单位: New York University; New York University; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; New York University; New York University; St Jude Children's Research Hospital; University of Illinois System; University of Illinois Urbana-Champaign; New York University
• 刊物名称: Nature
• ISSN/ISSBN: 0028-5732
• DOI: 10.1038/s41586-023-06974-w
• 发表日期: 2024-02-15
• 页码: 661-+
• 关键词: escherichia-coli chromosome; cell-cycle; messenger-rna; gene-expression; dna-replication; global analysis; division cycle; copy number; proteins; sporulation

摘要

Organisms determine the transcription rates of thousands of genes through a few modes of regulation that recur across the genome(1). In bacteria, the relationship between the regulatory architecture of a gene and its expression is well understood for individual model gene circuits(2,3). 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 cells(4). 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.