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.

Keywords: ChIP-exo; E. coli; gene targets; independent component analysis; transcriptional regulatory network; transcriptomics; two-component systems.

FIG 1

(A) Workflow used for each gene expression data set. (B) Categories of regulation by two-component systems in E. coli.

FIG 2

Comparison of regulatory network in BaeR and CpxR and functional characterization of direct and indirect targets.

FIG 3

Comparison of regulatory network in metal sensors and functional characterization of direct and indirect targets.

FIG 4

Cross-regulation among TCS and iModulon activities across selected conditions. The iModulon activity comes from the ICA-derived A matrix and represents the relative strength of the iModulon signal across the compendium of experimental conditions as described by Sastry et al. (7). The boxplots reflect a subset of conditions that are particularly relevant to the given iModulon. The y axis represents the iModulon activity level, and the x axis represent different experimental conditions. Graphical representations of the full iModulon activity profiles are available in Fig. S2 in the supplemental material.

FIG 5

(A) Example of ChIP-exo peak identification with PhoB peak_7 (pstSCAB-phoU operon). (B) Comparison of PhoB binding consensus motif to peak_7, which was identified as a match by AME.