Author
Listed:
- Andrew W. Pountain
(NYU Grossman School of Medicine)
- Peien Jiang
(NYU Grossman School of Medicine
New York University)
- Tianyou Yao
(University of Illinois at Urbana Champaign)
- Ehsan Homaee
(University of Illinois at Urbana Champaign
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
NYU Grossman School of Medicine)
- Victor J. Torres
(NYU Grossman School of Medicine
St. Jude Children’s Research Hospital)
- Ido Golding
(University of Illinois at Urbana Champaign
University of Illinois at Urbana-Champaign)
- Itai Yanai
(NYU Grossman School of Medicine
NYU Grossman School of Medicine)
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.
Suggested Citation
Andrew W. Pountain & Peien Jiang & Tianyou Yao & Ehsan Homaee & Yichao Guan & Kevin J. C. McDonald & Magdalena Podkowik & Bo Shopsin & Victor J. Torres & Ido Golding & Itai Yanai, 2024.
"Transcription–replication interactions reveal bacterial genome regulation,"
Nature, Nature, vol. 626(7999), pages 661-669, February.
Handle:
RePEc:nat:nature:v:626:y:2024:i:7999:d:10.1038_s41586-023-06974-w
DOI: 10.1038/s41586-023-06974-w
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