Transcription–replication interactions reveal bacterial genome regulation

Andrew W. Pountain, Peien Jiang, Tianyou Yao, Ehsan Homaee, Yichao Guan, Kevin J. C. McDonald, Magdalena Podkowik, Bo Shopsin, Victor J. Torres, Ido Golding and Itai Yanai ()
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Andrew W. Pountain: NYU Grossman School of Medicine
Peien Jiang: NYU Grossman School of Medicine
Tianyou Yao: University of Illinois at Urbana Champaign
Ehsan Homaee: University of Illinois at Urbana Champaign
Yichao Guan: University of Illinois at Urbana Champaign
Kevin J. C. McDonald: University of Illinois at Urbana Champaign
Magdalena Podkowik: NYU Grossman School of Medicine
Bo Shopsin: NYU Grossman School of Medicine
Victor J. Torres: NYU Grossman School of Medicine
Ido Golding: University of Illinois at Urbana Champaign
Itai Yanai: NYU Grossman School of Medicine

Nature, 2024, vol. 626, issue 7999, 661-669

Abstract: Abstract 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.

Date: 2024
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DOI: 10.1038/s41586-023-06974-w

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