Elucidation of Regulatory Modes for Five Two-Component Systems in Escherichia coli Reveals Novel Relationships

Abstract

Escherichia coli uses two-component systems (TCSs) to respond to environmental signals. TCSs affect gene expression and are parts of E. coli’s global transcriptional regulatory network (TRN). Here, we identified the regulons of five TCSs in E. coli MG1655: BaeSR and CpxAR, which were stimulated by ethanol stress; KdpDE and PhoRB, induced by limiting potassium and phosphate, respectively; and ZraSR, stimulated by zinc. We analyzed RNA-seq data using independent component analysis (ICA). ChIP-exo data were used to validate condition-specific target gene binding sites. Based on these data, we do the following: (i) identify the target genes for each TCS; (ii) show how the target genes are transcribed in response to stimulus; and (iii) reveal novel relationships between TCSs, which indicate noncognate inducers for various response regulators, such as BaeR to iron starvation, CpxR to phosphate limitation, and PhoB and ZraR to cell envelope stress. Our understanding of the TRN in E. coli is thus notably expanded. IMPORTANCE E. coli is a common commensal microbe found in the human gut microenvironment; however, some strains cause diseases like diarrhea, urinary tract infections, and meningitis. E. coli’s two-component systems (TCSs) modulate target gene expression, especially related to virulence, pathogenesis, and antimicrobial peptides, in response to environmental stimuli. Thus, it is of utmost importance to understand the transcriptional regulation of TCSs to infer bacterial environmental adaptation and disease pathogenicity. Utilizing a combinatorial approach integrating RNA sequencing (RNA-seq), independent component analysis, chromatin immunoprecipitation coupled with exonuclease treatment (ChIP-exo), and data mining, we suggest five different modes of TCS transcriptional regulation. Our data further highlight noncognate inducers of TCSs, which emphasizes the cross-regulatory nature of TCSs in E. coli and suggests that TCSs may have a role beyond their cognate functionalities. In summary, these results can lead to an understanding of the metabolic capabilities of bacteria and correctly predict complex phenotype under diverse conditions, especially when further incorporated with genome-scale metabolic models.

Interaction with mammalian enteric viruses alters outer membrane vesicle production and content by commensal bacteria

“…Our gene expression analysis leads to a hypothesis that the bacterial response to these viral particles occurs through one of the bacterial envelope stress response systems, such as the σ E pathway or the two‐component system CpxRA. Broadly speaking, stresses to the outer membrane and misfolded proteins in the periplasm activates the σ E pathway, while stresses to the inner membrane trigger the CpxRA system (Choudhary et al., 2020 ; Steenhuis et al., 2020 ). Both envelope stress response systems result in various regulatory functions to different stimuli, which may explain the large number of differentially expressed genes that were observed.…”

proChIPdb: a chromatin immunoprecipitation database for prokaryotic organisms

“…Each peak can also be associated with its downstream genes and operons, which generates a set of putatively regulated genes for comparisons against regulons defined from other data sources ( 18 ). These comparisons can help elucidate the ‘core’ regulon by discerning between direct and indirect effects, and reveal condition-specific binding behavior ( 19 , 20 ). Also, sequence motif enrichment can be performed to identify common nucleotide motifs in TFBSs ( 21 ), which may indicate the specific DNA features that influence TF binding.…”

A systems biology approach to disentangle the direct and indirect effects of global transcription factors on gene expression in Escherichia coli

“…Thus, additional work involving the experimental validation of metabolite-TF interactions (Diether et al, 2019; Lempp et al, 2019) as well as characterizing other modes of regulation such as sRNA (Wang et al, 2015) or protein-protein interaction (Arifuzzaman et al, 2006) would further contribute to our comprehensive understanding. Besides, the demarcation of the indirect and direct targets of global TFs are condition-dependent, meaning these indirect gene targets have binding sites for the TF but represent silent targets for that particular environment (Choudhary et al, 2020; Park et al, 2013). These sites could also represent relaxed specificity sites that could enable adaptation to newer environments (Taylor et al, 2022).…”

“…The DEGs enriched for direct targets of the dTFs in the double mutants were further validated by determining the binding motifs using FIMO analysis together with the available in vivo ChIP binding or other in vitro binding data (Federowicz et al, 2014;Grainger et al, 2007;Keseler et al, 2017;Myers et al, 2013;Park et al, 2013;Prieto et al, 2012;Santos-Zavaleta et al, 2019). Further, a few regulation-specific genes (15-20%) in double mutants and (20-32%) in case of single mutants, found in our analysis that were not characterized as direct targets, could potentially represent uncharacterized novel targets (Choudhary et al, 2020) of the dTF/s .…”

“…Exploring the regulatory interactions of dTF/s with the iTFs on the indirect gene target expressions Next, we analysed the indirect gene targets for any TF enrichment other than the dTF, assuming the probability of regulating these genes by the dTF is low or they might represent silent targets (Choudhary et al, 2020;Park et al, 2013) in this particular condition. These TFs are other interacting TFs (referred hereafter to as iTFs) whose gene expression or activity are modulated by the dTF/s.…”