Dynamics of bacterial operons during genome-wide stresses are influenced by premature terminations and internal promoters
Abstract
Bacterial gene networks have many operons, each coordinating a few genes. In Escherichia coli, half of the operons have internal promoters regulating downstream genes independently from upstream genes. We studied their role during genome-wide stresses targeting key transcription regulators, RNAP, and gyrase. We show that the operons' stress response dynamics are highly influenced by premature terminations of transcription elongation and internal promoters (specifically, their numbers, positioning, and responsiveness). Moreover, premature terminations are influenced by positive supercoiling buildup, collisions between elongating and promoter-bound RNAPs, and sequences commonly found at 3’ ends of RNA. We report the same findings in E. coli cells subject to other stresses and in the evolutionarily distant Bacillus subtilis, Corynebacterium glutamicum, and Helicobacter pylori. Our results suggest that the strength, number, and positioning of internal promoters in operons are subject to evolutionary pressure by a genome-wide need to compensate for premature terminations and provide distal genes in operons similar response strengths as genes proximal to the primary promoter.
Dryad DOI: https://doi.org/10.5061/dryad.zgmsbccks
This READ ME.txt file was generated on 10-03-2024 by Rahul Jagadeesan.
SHARING/ACCESS INFORMATION
This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license. All data in Dryad is released into the public domain under the terms of a Creative Commons Zero (CC0) waiver. CC0 does not exempt those who reuse the data from the following community norms for scholarly communication. It does not exempt researchers from reusing the data in a way that is mindful of its limitations. Nor does it exempt researchers from the obligation of citing the original data authors. CC0 facilitates the discovery, re-use, and citation of that data. For more information, see (https://creativecommons.org/share-your-work/public-domain/cc0).
Recommended citation for this dataset: Jagadeesan, Rahul; Dash, Suchintak (2025). Dynamics of bacterial operons during genome-wide stresses are influenced by premature terminations and internal promoters [Dataset]. Dryad. https://doi.org/10.5061/dryad.zgmsbccks
DATA & FILE OVERVIEW
The folder structure is organized as a function of the figures in the paper. However, all necessary context for interpretation is provided in this README. The folder names may reference manuscript figures, but all experimental and data details are explained here independently.
01_Flow_Cytometry
This folder contains all flow cytometry data used in the project. The folder structure is organized as a function of the figures in the paper.
01 Supercoilingsensitivity: To evaluate the relationship between RNAp premature terminations and the Supercoiling sensitivity of genes. We measured YFP-tagged genes to calculate differences in protein expression levels of genes in the same operon as a function of the distance between them. Only pairs of jeans without promoters in between were considered.
Related to Figure 3D of the main manuscript and Supplementary Figure S15, the subfolders Optimal, YFP genes Novobiocin 50 ng, and YFP genes Novobiocin 500ng correspond to the experimental conditions under which the data were measured. Each condition folder (Optimal, YFP genes Novobiocin 50 ng, etc.) contains subfolders named after individual genes, and each gene folder contains three CSV files corresponding to three biological replicates. ref:Genes_list.xlsx
02 Tandem Attenuation: To evaluate the relationship between premature terminations caused due to collisions between RNAps.
Related to Figure 4B&C and Supplementary Table S9; The subfolders: Control, LB 0.25X, LB 0.5X, LB 0.75X, Novobiocin (50 NG), Novobiocin (100 NG), Novobiocin (500 NG), and Rifampicin (2.5 NG) represent the experimental conditions under which the data was collected (LB refers to Luria-Bertani medium, and the values (e.g., 0.25X, 0.5X, 0.75X) indicate dilutions of the standard LB concentration used in the experiment.). Within each condition folder, flow cytometry data are provided for three different promoter constructs: Lac promoter alone, Tet promoter alone, and Tet-Lac in tandem orientation, each measured in three biological replicates.
Note: All flow cytometry data were acquired using an ACEA NovoCyte Flow Cytometer (ACEA Biosciences Inc., San Diego, USA). All flow cytometry raw data is provided as *.csv files.
Each raw file contains 26 columns, out of which we used the following: ‘FITC-H’, 'PE-Texas-Red-H', and ‘Width’. Specifically, we used the parameters 'FITC-H' and 'PE-Texas-Red-H' with a PMT voltage of 600 and 584, respectively, to inform the expression level of GFP and mCherry proteins. The parameter 'Width' measures the duration of the signal, not impacted by the PMT voltage, which correlates with cell size.
Finally, each file contains 50000 rows (excluding the heading row). Each row corresponds to a single-cell measurement.
02 Spectrophotometry
01 Growth rates: To compare the transcription dynamics of cells under each stress condition (Growth phase transition.xlsx).
Related to Supplementary Figure S2. The Data.xlsx file contains growth rates measured under different conditions, with 3 biological replicates.
02 ATP: To measure ATP levels during media-dilution conditions.
Related to Supplementary Figure S16, the Data & Analysis ATP.xlsx file contains information on ATP concentrations measured using Queen-2m cells.
03 Codes: All the custom-made codes used for analysis in this study.
Code
All the codes used in this study are tailor-made and generated using MATLAB 2019. The subfolders are divided based on individual figures and their corresponding codes.
Each subfolder inside the Codes folder corresponds to the figure it was used to generate. Within each subfolder, there is a .txt file that contains detailed instructions on how to use the code and what it is used to calculate.
| Folder Name | Description |
|---|---|
| 01 Figure 2A-C_5 | Calculates mean absolute log fold change (LFC) between genes in operons as a function of distance. |
| 02 Figure 2D-E | Same as above, comparing operons with and without internal promoters. |
| 03 Figure 3A | Calculates genome-wide RNA polymerase (RNAp) fall-offs in E. coli. |
| 04 Figure 3D | Calculates RNAp fall-offs due to RNAp collisions. |
| 05 Supplementary Figure S7 | Analyzes LFC within operons containing transcription factor binding sites. |
| 06 Supplementary Figure S8 | Analyzes LFC within operons without transcription factor binding sites. |
| 07 Supplementary Figure S9 | Analyzes operons with self-transcription factor binding sites. |
| 08 Figure 3B-C | Compares operons with and without supercoiling-sensitive genes. |
| 09 Supplementary Figure S5 | Analyzes LFC as a function of genes' position within operons. |
| 09 Supplementary Figure S14 | Calculates spatial correlation between genes. |