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 was used to validate condition-specific target gene binding sites. Based on this data we (1) identify the target genes for each TCS; (2) show how the target genes are transcribed in response to stimulus; and (3) reveal novel relationships between TCSs, which indicate non-cognate 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 human gut microenvironment; however, some strains cause diseases like diarrhea, urinary tract infections and meningitis. E. coli’s two-component system (TCS) modulates target gene expression, specially related to virulence, pathogenesis and anti-microbial peptides, in response to environmental stimuli. Thus, it is of utmost importance to understand the transcriptional regulation of the TCSs to infer its environmental adaptation and disease pathogenicity. Utilizing a combinatorial approach integrating RNAseq, independent component analysis, ChIP-exo and data mining, we show that TCSs have five different modes of transcriptional regulation. Our data further highlights non-cognate inducers of TCSs emphasizing cross-regulatory nature of TCSs in E. coli and suggests that TCSs may have a role beyond their cognate functionalities. In summary, these results when further incorporated with genome scale metabolic models can lead to understanding of metabolic capabilities of bacteria and correctly predict complex phenotype under diverse conditions.
Introduction
Bacterial survival and resilience across diverse conditions relies upon environmental sensing and a corresponding response. One pervasive biological design towards this goal consists of a histidine kinase unit to sense the environment and a related response regulator unit to receive the signal and translate it into gene expression changes. This signaling process is known as a two-component system (TCS) (1). In the case of Escherichia coli (E. coli) strain K12 MG1655, there are 30 histidine kinases and 32 response regulators involved in 29 complete two-component systems that mediate responses to various environmental stimuli such as metal sensing, cell envelope stress, acid stress, and pH stress (2). The cell envelope is an important barrier between bacteria and their surrounding environment and is exposed to a variety of stresses and stimuli. In E. coli MG1655, BaeSR and CpxAR are two TCSs that are each instrumental in regulating the response to envelope stress (3). KdpDE and ZraSR are key regulators in maintaining homeostasis for potassium and zinc, respectively, and PhoRB has a role in bacterial pathogenesis and phosphate homeostasis (4). Understanding these elements is important for developing an overarching knowledge of bacterial regulatory networks. A detailed reconstruction of the transcriptional regulatory network (TRN) contribution of these systems in response to a specific stimulus will help us discover different modes of transcriptional regulation. Furthermore, this can unravel cross-regulation and complex relationships amongst TCS systems.
Multiple tools exist to investigate the regulons and activities of transcription factors and TCSs. In this study, we focused on two data-types: chromatin immunoprecipitation coupled with exonuclease treatment (ChIP-exo) binding peaks, and gene expression profiles from RNA sequencing (RNA-seq). ChIP-exo identifies genome-wide binding locations for response regulators with high precision (5). Although ChIP methods provide direct binding evidence for the interactions between TFs and the genome, they also tend to identify off-target, nonspecific binding events that may not affect downstream gene expression (6). In order to reduce the false positive rate from ChIP-exo, gene expression profiles can identify differentially expressed genes (DEGs) in response to appropriate stimuli or response regulator knock-outs. However, some DEGs may be affected by indirect regulation. To circumvent this issue, we also used independent component analysis (ICA), an unsupervised machine learning algorithm that decomposes gene expression data, to find independently modulated sets of genes, or iModulons (Figure 1A), that closely resemble known regulons (7).
A) Workflow used for each gene expression dataset. B) Categories of regulation by two-component systems in E. coli.
A previous application of ICA to PRECISE (a high-quality RNA-seq gene expression compendium) (7) identified nine iModulons that corresponded to eleven TCS regulons. We noticed that two sets of TCS pairs were grouped together: YpdAB and BtsRS, and HprSR and CusSR (7). Interestingly, YpdAB and BtsRS respond to the same inducer (pyruvate). Conversely, HprSR and CusSR both regulate the same set of genes but have different inducing signals. This second observation is in agreement with previous studies (8), and is indicative of cross-regulation between the two TCSs wherein they have the same regulon. ICA generates iModulon activity levels to determine gene expression changes relative to a specific condition. Indeed, these previous analyses showed that ICA can be aptly used to describe condition-specific activities (YpdAB and BtsRS) and provide a detailed understanding of tightly cross-regulated TRNs (HprSR and CusSR).
In this study, we aimed to characterize global TRN contributions of these five TCSs (BaeSR, CpxAR, KdpDE, PhoRB, and ZraSR) in E. coli MG1655. We used three methods (ICA, ChIP-exo, and Differential Expression of Genes (DEGs)) to reveal which genes were regulated by the TCS and characterized the modes of transcriptional regulation into five categories: 1) Functional direct binding, 2) Indirect targets, 3) Non-functional/spurious binding, 4) Cross-regulated genes, and 5) Hypothetically functional binding. Finally, we used the iModulon activities to characterize TCS activation across hundreds of experimental conditions to identify interconnectedness among TCSs.
Results
We generated knockout mutants for each of the five two-component systems (BaeSR, CpxAR, KdpDE, PhoRB, and ZraSR) in E. coli K-12 strain MG1655 (Table 1), and collected RNA-seq data for both wild-type and knockout mutants under stimulated and unstimulated conditions, using specific stimuli for each TCS. We combined these expression profiles with 278 additional expression profiles previously generated (called PRECISE (7)), and applied the ICA algorithm to the combined dataset (See Methods) to identify 101 iModulons. Of these 101 iModulons, 90 iModulons mapped to the previous set of 92 iModulons from PRECISE (7). Eleven iModulons were specifically activated by the inclusion of the TCS-related expression profiles, four of which represented TCSs (BaeSR, KdpDE, PhoRB, and ZraSR). The CpxR iModulon was among the 90 iModulons previously detected in PRECISE, and was retained upon addition of the new expression profiles. In total, 15 of the 101 iModulons represented TCSs (Supplemental Dataset S1). For further analysis, we focused on the five target TCSs (BaeSR, CpxAR, KdpDE, PhoRB, and ZraSR).
List of strains used in this study
To identify the true regulons for each TCS, we compared four sets of genes: (1) Genes in the iModulon associated with the respective TCS, (2) Genes with ChIP-exo determined upstream binding sites, (3) Genes that are differentially expressed between wild-type and knockout mutants under stimulated conditions, and (4) Genes in previously published regulons in RegulonDB (9) (Figure 1B). Each method alone is prone to false positives and biases, but together provide multidimensional information that can be probed to identify a complete, high-confidence regulon (Figure 1B). For example, differentially expressed genes (DEGs) without direct ChIP-exo evidence are likely targets of downstream, indirect regulation wherein one TF may regulate a set of genes which in turn may lead to regulation of a second set of genes. On the other hand, ChIP-exo binding sites upstream of genes that are absent from both the iModulon and the DEG set are likely non-functional. Such integrated analysis led us to describe the regulatory network of each selected two-component system into five categories (Figure 1B): 1) Functional direct binding: genes which were observed in either iModulons, published regulons, or DEGs, and a ChIP-exo binding peak was observed upstream; 2) Indirect targets: genes that were differentially expressed under each specific condition, but no binding peaks were observed upstream; 3) Non-functional/spurious binding: peaks upstream of genes which were neither differentially expressed nor had iModulons and/or published regulons associated to them. Such peaks may have arisen due to off-target recognition or due to hot spots which tend to be recognized by many TFs; 4) Cross-regulated genes: genes which were observed in iModulons and/or published regulons but were not part of functional direct targets and indirect targets; and 5) Hypothetically functional binding: genes which were found in the published regulon and showed ChIP-exo binding peaks but were not differentially expressed. These genes may be a part of a direct regulatory network, but the data was not supported by transcriptomics; therefore, we categorized them into hypothetical functional binding.
1) Reconstruction of CpxR and BaeR regulatory responses under ethanol stress in E. coli
BaeR and CpxR are activated by a wide range of stressors (10–14) and play a regulatory role in the envelope stress response (ESR) system (10, 15–17). We stimulated the wild-type, ΔbaeR and ΔcpxR strains with 5% ethanol stress, which has been identified as a particularly effective inducer (18) to elucidate their regulons.
BaeR
BaeR is involved in multidrug resistance through regulation of the MdtABC efflux pump (19–21). However, BaeR has also been shown to impact genes related to many other functions, such as signal transduction, chemotactic responses, flagellar biosynthesis, and maltose transport (22). Despite this range of functionality, there are only eight genes in the previously published BaeR regulon. The BaeR iModulon exhibited 37.5% (3 out of 8) overlap with these published regulon genes, consisting of spy, baeR, and mdtA genes (Table 2). Five of the published regulon genes (mdtB, mdtC, mdtD/iceT, baeS, and acrD) were not identified in the BaeR iModulon. This discrepancy was most likely due to differences in BaeR induction conditions across the published results (10–13), or the possibility that the three iModulon genes are much more significantly regulated by BaeR than the other five genes in the published regulon. To test this claim, ChIP-exo results were analyzed (Supplemental Dataset S2) then compared to the iModulon. ChIP-exo results indicated 21 BaeR binding peaks upstream of 17 operons consisting of 32 genes. We identified binding sites upstream of spy and mdtA genes (mdtABCD-baeSR operon). Since a peak was identified upstream of mdtA gene, which forms a part of mdtABCD-baeSR operon (see methods), we considered all the genes in that operon to be under the direct regulatory network of BaeR. In total, we designated six genes as functional direct targets of BaeR. No binding peaks were identified upstream of acrD, indicating that it is indirectly regulated by BaeR.
Genes in bold text in “iModulon Genes” and “Targets from RegulonDB” columns indicate common genes.
Differentially expressed genes (DEGs) were analyzed to further expand the global regulatory network of BaeR. A total of 328 genes were found to be differentially expressed due to the baeR knockout under 5% ethanol stress condition (Supplemental Dataset S3). ChIP-exo peaks were detected upstream of nine of these genes, expanding the number of direct targets from seven to nine. Of these nine targets, two genes, intF and tnaA, have not been previously identified as a part of the BaeR regulon. However, tnaA has previously been shown to contribute to BaeR activity under indole stress (23), as it is involved in metabolizing tryptophan to indole. The remaining 319 differentially expressed genes could either be indirect targets of BaeR (Figure 1, Supplemental Dataset S4) or other transcription factors responsive to ethanol such as CpxR. Functional analysis of indirect targets through COG categories confirmed BaeR’s manifold activity (Figure 2).
Comparison of regulatory network in BaeR and CpxR and functional characterization of direct and indirect targets.
Overall, 2.74% of DEGs were under the direct regulatory network of BaeR. The remaining 23 genes found through ChIP-exo results could be classified as non-functional targets or cases of spurious binding (Figure 1, Supplemental Dataset S5). In most of the cases we did identify genes being regulated by BaeR knockout but they either did not pass the P-value threshold (< 0.05) or log 2 fold change threshold (1.5). Others may be the result of off-target binding.
CpxR
CpxR is among the most extensively studied of the response regulators, which is unsurprising when considering its host of functions. CpxR has been shown to have a regulatory role in the envelope stress response (ESR) system (10, 15–17), protein folding and degradation (24–27), pilus assembly and expression (28–30), secretion (15), motility and chemotaxis (15, 31), biofilm development (14, 32), adherence (33, 34), multidrug resistance and efflux (12, 35), porins (36), and copper response (37, 38), among others. Two iModulons were linked to the CpxR regulon: CpxR and CpxR-KO. The CpxR iModulon consists of genes that were regulated specifically under wild type conditions, and CpxR_KO consists of genes differentially expressed due to CpxR deletion. Collectively, both CpxR iModulons consist of 16 genes that cover diverse functions such as motility, inorganic ion transport and metabolism, and carbohydrate metabolism, among others. Nine of the 16 iModulon genes were previously published regulon genes, which is only a fraction (14%) of CpxR’s previously-identified 66 genes (9) (Table 2). This provides extra incentive to eventually validate the CpxR iModulon with different induction conditions. Interestingly, the CpxR iModulon consisted of seven new genes that were not previously identified to be part of the CpxR regulon (Table 2). These seven new genes may indicate expansion of the CpxR regulon specific to ethanol stress. To validate this hypothesis, ChIP-exo peaks were examined. We were able to identify 44 binding peaks upstream of 16 operons (consisting of 30 genes). Of these genes, five were in the CpxR iModulon (cpxP, cpxR, raiA, yagU, and yccA). Two genes (raiA and yagU) in the CpxR iModulon with associated binding peaks had not been previously identified as part of the CpxR regulon. The five overlapping ChIP-exo/iModulon/regulon genes were classified as direct targets of CpxR under ethanol. For the remaining five iModulon genes that are not previously published regulon genes and do not have binding peaks identified, 60% are part of the uncharacterized “y-ome” (39). These genes may be the result of cross-regulation between different pathways responding to ethanol and put under the category of cross-regulatory network.
We inspected DEGs to further understand the nature of the five new iModulon genes and to expand the global regulatory network of CpxR. Differential gene expression analysis expanded the indirect targets of CpxR to include 368 genes. Comparison of ChIP-exo binding peaks and differentially expressed genes of the cpxR knockout vs. wild type under 5% ethanol stress condition revealed seven additional genes (cpxA, fimA, fimI, lldD, lldR, ppiA, and yceI) that fall under the direct regulatory network of CpxR, expanding this network to 12 genes. The remaining 357 DEGs formed the indirect regulon of CpxR, including eight genes from the previously published regulon (Figure 2, Supplemental Dataset S4). A diverse group of COG categories corroborated CpxR’s regulatory role.
Interestingly, among the 368 differentially expressed genes (DEGs), 194 were differentially expressed in both knockout strains (baeR and cpxR), suggesting non-specific targets of BaeR and CpxR and high cross-regulation between these two pathways. The remaining 17 ChIP-exo binding peaks overlapped with one previously published regulon gene (ompC) which was not part of either the DEGs or iModulons. This suggested that ompC was neither directly nor indirectly affected by cpxR knockout under ethanol stress, and therefore is considered to be part of the “hypothetically functional binding” network (Supplemental Dataset S6). Five iModulon genes (alx, mdtJ, yjfN, yliI, and yobB) did not form part of direct and indirect targets but instead formed part of the network which is involved in cross-regulation. The remaining 52 previously published regulon genes which did not have binding sites upstream and were not differentially expressed also formed part of a cross-regulatory network (Supplemental Dataset S7).
2) Reconstruction of KdpE, PhoB, and ZraR transcriptional regulatory network in E. coli
Decoding the role of metal and nutrient sensors in the transcriptional regulatory network may provide a deeper understanding of how bacteria utilize these systems to sense nutrients and ionic strength of the environment. These mechanisms play a crucial role in the organism’s ability to adapt to various environmental niches. The KdpE, PhoB, and ZraR two-component systems were selected for further analysis in this category.
KdpE
The KdpE response regulator’s sole target is the kdpFABC operon, which has been shown to activate the potassium uptake system during low potassium conditions (40, 41) or salt stress (42, 43). To induce KdpE, a small amount of potassium (0.1 mM KCl) was added to the cells in tris maleic acid (TMA) minimal medium, which aligns with previous experiments where successful induction was achieved (44, 45). The KdpE iModulon included the expected kdpA, kdpB, and kdpC potassium uptake genes; however, the iModulon does not include the fourth gene in the kdpFABC operon, kdpF (Table 2). This result provides validation of previous studies that have instead described the target operon as kdpABC (43, 46). kdpF may function only as a stabilizer of the transporter complex (47), and the gene may also just be a less vital component of the regulon. Apart from the kdpABC operon, the only other KdpE iModulon genes include those that code for the TCS itself, kdpD and kdpE. The small KdpE iModulon confirms the existing knowledge of the response regulator as targeting a single locus in the bacterial genome. Despite the small iModulon size, there were still 31 ChIP-exo binding peaks upstream of 37 genes including kdpABC genes from KdpE iModulon observed. Surprisingly, no binding peaks were observed upstream of two iModulon genes, kdpDE; however, kdpDE is downstream of the kdpFABC operon, which may indicate that kdpDE is in the same transcription unit as kdpABC. Consistent with this, when comparing 197 DEGs in response to kdpE knockout under potassium limited condition (0.1 mM KCl), we did find kdpDE to be differentially expressed. DEGs further added three extra genes (adeP, uxaA, and uxaC) to the direct regulatory network of KdpE, amounting to six genes under direct regulation of KdpE. The remaining 191 DEGs can be interpreted as potassium-dependent genes under indirect KdpE regulation. As expected, the COG category ‘inorganic ion transport’ was overrepresented. In addition, the categories ‘energy production and conversion’ and ‘metabolism and nucleotide transport and metabolism’ were also over represented. A wide array of COG categories suggests that KdpDE may indirectly regulate genes involved in metal efflux pump and electron transport systems to maintain homeostasis (Figure 3).
Comparison of regulatory network in metal sensors and functional characterization of direct and indirect targets.
Notably, there was evidence of kdpF being down-expressed in the KdpE knockout (log fold change of −3.22) but due to an insignificant p-value, this was not regarded as differentially expressed. Therefore, kdpF appears as the sole gene from the KdpE published regulon that was not identified by DEG analysis in this study (placing it in the category of “cross-regulated” genes). Apart from kdpF and genes categorized as direct or indirect targets for KdpDE, there were also 31 genes identified with ChIP-exo that did not overlap with other datasets and were thus categorized as potential spurious binding genes.
PhoB
The PhoB response regulator has been demonstrated to regulate phosphate uptake and metabolism under phosphate-limiting conditions (48–51) and is linked to virulence in pathogenic E. coli (4). To induce PhoB via phosphate deficiency, cells were first grown in M9 minimal medium and then washed with and further grown in M9 minimal medium without phosphate (M9-P). The PhoB iModulon consisted of the expected pstSCAB operon for regulation of the phosphate uptake system (52–54) and the genes coding for PhoRB itself. Contrary to the modest six gene iModulon, the previously published PhoB regulon is considerably larger and is thought to consist of 60 genes (55, 56), including all six of the iModulon genes. For the binding site analysis, we found 12 ChIP-exo binding peaks upstream of 17 genes. Six of these genes were included in the iModulon (the pstSCAB phoBR operons), indicating direct response to phoB knockout. When comparing the 113 DEGs under phosphate limited condition to the ChIP-exo binding peaks, an additional gene, phoU-phosphate specific transporter (which was also part of the previously published regulon), was added to the direct regulatory network of PhoB. The remaining 106 DEGs can be identified as phosphate-dependent genes, including six previously-determined regulon genes (cusB, phnC, phnD, phnN, phoA, and ugpB) or indirect targets of PhoB. Interestingly, the COG category ‘cell motility’ was more pronounced than ‘inorganic ion transport and metabolism’, indicative of the involvement of PhoRB in cell migration in addition to adapting to a phosphate limited environment. Aside from the direct and indirect targets, the remaining 57 genes found to be associated with PhoB included 10 genes that were identified as a potential result of spurious binding, two genes that fell under the hypothetically functional binding category, and 45 genes that were possibly related to the KdpE network via cross-regulation.
ZraR
Finally, we chose to examine ZraR (formerly called HydG), which had been previously characterized as a σ54-dependent response regulator that activates chaperone ZraP to provide tolerance to high zinc concentrations (57–59). More recently, it has been proposed that ZraR is not directly involved in zinc resistance (60) but rather is simply activated by zinc to achieve its broader role in the envelope stress response (ESR), similar to CpxAR (61). Since the commonality of these differing theories centers on zinc, we chose to study ZraR regulatory activity on LB medium supplemented with 1 mM ZnCl2 (57). The ZraR iModulon included only two of the three previously published regulon genes, zraR and zraP. One of the regulon genes, zraS, was not identified by the iModulon. However, ChIP-exo binding peaks were observed upstream of all three regulon genes (zraSR and zraP), confirming these genes to be under the direct control of ZraR. The ChIP-exo results only indicated one additional direct target gene, mgtA, and the remaining 12 binding peaks might be attributed to spurious binding. DEGs were examined to identify indirect targets and expand direct targets (if any) of ZraR. In the presence of 1 mM ZnCl2, 97 genes were found to be differentially expressed between the zraR knockout and wild-type strain. Four of these genes (zraSR, zraP and mgtA) had corresponding upstream binding peaks found by ChIP-exo or were included in the ZraR iModulon. The remaining 93 DEGs can be attributed to indirect ZraR regulation. Therefore, ZraSR seems to be involved in cell migration and carbohydrate metabolism (Figure 3) similar to CpxAR, further strengthening the cross-regulation of these two TCSs.
Overall, integrating iModulons with ChIP-exo and DEGs enabled us to expand the global regulatory network contribution of each selected TCS and categorize genes in different modes (direct or indirect) of transcription regulation.
3) ICA identifies potential novel inducers
To identify potential novel inducers and inter-connection between TCS pathways, we compared the activity levels of each TCS iModulon across all PRECISE experiments. To this end, the iModulon activities of each TCS were analyzed across their expected induction condition(s) in our experiments, as well as the previous conditions of PRECISE experiments. By calculating each TCS iModulon activity for the experiments under control conditions (e.g. LB medium for BaeSR, CpxAR, and ZraSR), a baseline was established for expected TCS behavior in the absence of an induction condition. From there, iModulon activities were calculated for all other experimental conditions across PRECISE and the other TCS experiments of this study, and then these activities were evaluated against the control condition iModulon activity. While many experimental conditions yielded iModulon activities indiscernible from those of the control, several key environmental inducers were shown to have significant differential activity for each TCS (Figure 4). These results are important for understanding which types of environments are most likely to trigger TCS action. The ability to succinctly examine the activity levels of an iModulon over an ever-growing compendium of conditions enables the broad characterization of the iModulon’s response (or lack of response) to each of the conditions. Supplemental Figure S2 shows the activity profiles for each of the five TCS response regulators (RR) across all conditions, including PRECISE conditions and the new conditions tested in this study.
Cross-regulation among TCS and iModulon activities across selected conditions
When observing the activity levels of ethanol sensors (BaeR and CpxR), we noted that a high average activity level occurred when the wild-type strain was cultured on LB + 5% ethanol. Interestingly, both the BaeR and CpxR iModulons exhibited increased activity for a previous set of experiments that studied the effect of osmotic stress on E. coli K-12 MG1655 in relation to the OmpR regulon (62). Although Bury-Moné 2009 (18) had suggested that 0.6 M NaCl did not activate a CpxR and BaeR target gene reporter, our results suggest that osmotic stress (via the addition of 0.3 M NaCl) may induce BaeR and CpxR to some extent. A prior study attempted to identify cross-regulation between CpxAR and OmpR-EnvZ systems (63). In addition to osmotic stress induction, previous PRECISE iron starvation conditions (64) also yielded a significant increase in BaeR iModulon activity, which indicates that BaeR may also be involved in response to iron deficiency (Figure 4). For CpxR, iModulon activity peaks also occurred across experiments testing for phosphate starvation. The array of conditions inducing the BaeR and CpxR iModulons is reasonable considering the response regulators’ extensive functionalities and roles in responding to cell envelope stress (Figure 4).
KdpE was shown to be directly phosphorylated by PhoR under simultaneous potassium and phosphate limitation (45). Consistent with this, we observed high peak activity of the KdpE iModulon for our TCS experiments conducted on phosphate-depleted M9 medium, in addition to the expected induction of the wild-type strain on TMA + 0.1 mM KCl (Figure 4). Another notable iModulon activity increase was observed for a subset of PRECISE experiments that were conducted under osmotic stress (62), which is consistent with KdpE’s documented induction under salt stress (42, 43). Compared to KdpE, the PhoB iModulon was active in several of our other TCS experimental conditions, with an especially notable trend of activity increases for all our samples on LB + 5% ethanol stress. Although PhoB is not generally thought to be linked to the envelope stress response, these iModulon activity increases could potentially be linked to previous research that has found ethanol to induce calcium phosphate crystallization (65, 66) that could limit the amount of freely available phosphate and mirror starvation conditions. For the PRECISE experiments, most iModulon activities were negative or only minimally positive, which is consistent with the absence of phosphate starvation conditions in any of the previous experiments (Figure 4).
Notably, the ZraR iModulon includes only the two expected regulon genes, zraR and zraP, yet this iModulon exhibits significantly heightened activity over a surprisingly wide range of experimental conditions apart from LB + 1 mM ZnCl2 (Supplemental Figure S2). For this study’s experiments, the ZraR iModulon activity was high for all experiments conducted in phosphate-depleted M9 medium and most experiments conducted in LB + 5% ethanol. The high activity of the iModulon across diverse conditions supports the view of ZraR as a broader regulator past simple zinc resistance; specifically, ethanol’s activation of the iModulon could be indicative of ZraR’s relation to ESR, as previously suggested (61) (Figure 4).
Overall, combining our new dataset with PRECISE and applying ICA enabled us to identify potential cross-regulation between TCS pathways, which allowed the identification of novel or non-cognate TCS-inducer pairs. Both the known induction conditions validated by this study and the novel induction conditions illuminated by this study are summarized in Table 3 below.
Response regulator induction across various conditions using ICA iModulon activity levels.
4) Validation of Results
Before using the various gene datasets to scrutinize the regulatory network of each TCS, the quality of the newly generated data was first evaluated. Namely, the peaks found with ChIP-exo were shown to contain the published consensus motif of each TCS, and the RNA-seq data was analyzed to verify TCS regulon induction under their respective stimulation conditions.
Verifying ChIP-exo Binding Peaks
To validate the binding peaks found by the ChIP-exo analysis, the peaks associated with direct targets from this study were compared to known binding peak motifs using AME (Analysis of Motif Enrichment) from the MEME Suite (67). The experimentally identified peaks that were found to match the consensus motif as represented by RegulonDB (68), are summarized below in Table 4.
ChIP-exo peaks for direct target genes that were identified as a match to the consensus motif by AME
All five of the examined TCSs were shown to have at least one direct target ChIP-exo peak that matches the consensus motif using AME. In accordance with expectations, the matching peaks pinpointed by AME included canonical primary targets of each TCS response regulator. A visualization of the peak genomic location and sequence match to the consensus motif is shown below in Figure 5 for the pstSCAB-phoU peak (id: peak_7) that was identified as a match to the PhoB consensus motif.
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.
Examining Induction with RNA-seq Differential Gene Expression
The results of this study rely upon the adequate stimulation of the various TCSs under their various induction conditions. As a verification of these induction conditions, a closer look is given to differential gene expression in both the unstimulated and stimulated conditions. For the histidine kinase and response regulator genes of each TCS, the response regulator knockout versus wild-type RNA-seq gene expression is compared under both the unstimulated and stimulated conditions to confirm successful induction (Table 5). A broader comparison of the response regulator knockout versus wild-type expression for the entire direct regulon of each TCS is available in Supplemental Dataset S8.
Summary of differential expression of RR knockout versus wild-type strains under unstimulated and stimulated conditions for each TCS gene
Both the histidine kinase and response regulator genes were shown to be differentially expressed at their respective stimulation condition with a log fold change greater than 1.5 and a p value less than 0.05 (Table 5). This observation confirms that each of the TCS were more expressed in the wild-type strain versus the RR KO strain under induction. For the respective unstimulated conditions, some of the TCS genes were also differentially expressed, including all the response regulator genes and the cpxA and phoR histidine kinase genes. This result shows that all the response regulator genes and two of the histidine kinase genes were being expressed in the wild type even without induction. However, all of the histidine kinase genes and all of the response regulator genes other than cpxR exhibited a greater magnitude of log fold change in the stimulated condition versus the unstimulated condition. Overall, this seems to indicate successful induction of the histidine kinase elements to the respective stimuli.
Discussion
E. coli uses two-component systems (TCSs) to respond to environmental signals by modulating gene expression. Each TCS employs a signal transduction mechanism to modulate target gene expression when a certain stimulus is received. These signal transduction systems form a part of the global transcriptional regulatory network (TRN). In this study, we identified the TRN contributions of five TCSs in E. coli: BaeSR and CpxAR, which were stimulated by ethanol stress, and KdpDE, PhoRB, and ZraSR, which were stimulated by potassium limitation, phosphate limitation, and added zinc, respectively. The recent identification of iModulons in E. coli’s transcriptome (7) enables new capabilities to further explore its response to environmental stimuli. Here, this ICA-driven iModulon determination process was implemented in combination with ChIP-exo data analysis to study the TRN contribution of five TCSs. This technique delivered three key results: 1) pinpointing and validation of the target genes for each TCS; 2) determination of modes of transcriptional regulation of the target genes for each TCS; and 3) the elucidation of TCS activity across varying conditions and novel relationships among TCSs.
The integrative approach of combining ICA, Chip-exo, and RNA-seq datasets provided us with the strong evidence to expand the global regulatory network of each of the five TCSs (BaeSR, CpxAR, KdpDE, PhoRB, and ZraSR) and decipher different modes of transcriptional regulation. With this study, we extend the scope of TRNs beyond direct regulations by defining indirect targets, cross-regulatory networks, and hypothetically functional binding genes. Apart from the classically studied direct targets, genes in cross-regulatory networks and hypothetically functional binding genes fall into new categories that are of particular interest. For example, ompC was characterized as a hypothetically functional binding gene target of CpxR, since it was previously reported as part of the CpxR regulon in response to high acetyl phosphate (36) and we identified ChIP-exo binding peaks upstream of it. However, we did not observe any differential expression of this gene or iModulon activity. The lack of response in ompC expression despite the potential binding of CpxR suggests that some other regulator may be repressing the action of CpxR on ompC. Since ompC already belongs to the published CpxR regulon, it is possible that the specific conditions of this study (i.e. ethanol induction on LB media) triggered a repression response that is not always present. Therefore, we classified this gene under the “hypothetically functional binding gene” category, suggesting a possible regulation of this gene via CpxR despite the lack of substantiation with RNA-seq expression results. Similarly, two genes (sbcC and sbcD) from the PhoRB network were characterized as hypothetically functional binding genes, which could also indicate that these genes are potential binding targets of PhoB.
The “cross-regulatory network” category included genes which were identified in the iModulon and/or published regulon but could not be confirmed using ChIP-exo methods and/or DEGs. This category of genes are of interest because they show the interwoven and signal-specific nature of the TRN. It could be postulated that cases where the genes were just identified by iModulons were due to a more stringent threshold for calculating DEGs. However, it may be more accurate to consider the unique, albeit complementary, perspectives offered by iModulons and DEGs on the effects of gene knockouts. Specifically, iModulons identify genes that are co-expressed, and likely co-regulated, across over 300 expression conditions, whereas DEGs offer a targeted analysis of the effect of a single regulator knock-out. Due to the larger dataset feeding ICA, patterns can be deciphered that may appear insignificant by DEG analysis alone. For example, we observed five genes in the cross-regulatory network of the CpxR regulon (alx, mdtJ, yjfN, yliI, and yobB) that were identified by iModulons but not by DEGs because of low p-value significance (> 0.05 threshold). This example highlights the capability of ICA in deciphering the global TRN.
In addition to introducing new ways to categorize RNA-seq and ChIP-exo results, we show that applying ICA to transcriptomics data provides a thorough understanding of TCS TRN contributions. Even further, we are able to propose novel relationships between different regulatory elements. For example, we identified that apart from the previously known ethanol stress as an inducer of BaeR, osmotic stress and iron-deficiency may also modulate BaeR target gene binding; however, additional experimentation would be necessary to verify this relationship. Other novel relationships were indicated by the increase in CpxR and ZraR iModulon activities under phosphate starvation conditions, which suggests that phosphate limiting conditions may also act as an inducer for CpxAR and ZraSR two-component systems. Conversely, we also identified a potential involvement of PhoRB and ZraSR in the envelope stress response. One possible explanation of the link between phosphate starvation and envelope stress response could be the role of each of the stimuli in biofilm formation. Specifically, it has been suggested that the phosphate starvation of E. coli results in the modification of lipopolysaccharides to increase biofilm formation (69), and CpxAR has been proposed to be induced by biofilm formation due to its sensing of cell adhesions (34). Therefore, the phosphate starvation in this study may have stimulated biofilm formation, which may have in turn induced CpxAR and broader ESR targets. We were also able to confirm previously documented relationships between KdpDE and PhoRB systems (45) wherein the activity of the KdpE iModulon was found to be high under phosphate limited conditions, in addition to potassium and osmotic stress. Taken together, this study identified several new potential relationships and provided evidence towards suspected relationships by focusing on only five out of the 29 complete TCSs. As the scope increases past these five TCSs, there is potential for identifying additional novel relationships to enhance our knowledge of TRN interconnectedness.
Finally, we validated the genesets falling in each category by comparing the known consensus motifs using AME. We observed that at least one direct target ChIP-exo peak for each TCS matched the consensus motif using AME. Interestingly, two genes (sspA and sspB) that were categorized as potentially spurious binding peaks for BaeR were also found to match the consensus BaeR motif. This result could indicate that the sspAB operon should be reclassified as a novel binding target of BaeR. The gene sspA has been shown to be essential for survival during acid-induced stress (70), so a relationship to the envelope stress-mediating BaeSR TCS does not seem improbable. However, further experimentation would be necessary to validate sspAB as a potential binding target of BaeR, especially with the lack of RNA-seq substantiation in this study.
In summary, we suggest that integrating ChIP-exo and DEG analyses with ICA can provide a deeper understanding of the transcriptional regulatory network of E. coli. In particular, ICA provides a broad view of TCS activity over diverse conditions, enables the detection of non-cognate inducers to response regulators (this study), and can identify redundancy in TF actions (HprSR and CusSR) (our previous study (7)) by showing that different external signals cause expression of the same set of genes. This robust validation of the five TCSs’ contributions to the E. coli TRN expands the current knowledge base by identifying new potential direct and indirect targets of each TCS RR and by confirming previously predicted or inferred TCS target genes that were found in earlier studies. Looking forward, the horizon of bacterial ‘awareness’ can be examined by incorporating these results into a genome-scale reconstruction of two-component systems which can reliably predict metabolic capabilities under different environmental conditions.
Materials and Methods
Bacterial strains and growth conditions
Strains used in this study are E. coli K-12 MG1655 and its derivatives (deletion strains and myc-tagged strains). As previously described (71), strains retaining 8-myc were generated by a λ red-mediated site-specific recombination system targeting the C-terminal region of each selected response regulator. Knock-out mutants (ΔbaeR, ΔcpxR, ΔkdpE, ΔphoB and ΔzraK) were generated according to the procedure from Datsenko and Wanner (72), using pKD13 and pKD46 as suggested by the authors (please refer to Supplemental Table S1 for full oligonucleotide list). All knockouts are complete deletions, except for KdpE; the KdpE locus could potentially produce an 18 bp peptide, since the sequence after the stop codon contains too many A/T-rich repeats for proper oligonucleotide annealing hence the binding site had to be shifted into the coding region (please see supplemental table for oligonucleotide list). If not stated otherwise, cells were grown in pre-cultures prepared from glycerol stocks, overnight and then transferred to fresh medium the next morning. For ethanol sensors (BaeR and CpxR): Cells were grown at 37 °C in liquid LB medium to an OD600=0.5, then ethanol was added to a final concentration of 5% w/v. To ensure proper binding of RRs to their target genes, cells were grown for 30 min in the presence of ethanol before being collected for ChIP-exo and RNA-seq. This growth condition was chosen according to Bury-Moné 2009 (see Table 1 in Bury-Moné (18)), who showed, that 5% ethanol led to the strongest induction of β–galactosidase reporters for CpxR and BaeR among all tested conditions (i.e. 2 mM and 4 mM Indole, 3% and 5% ethanol, 0.5 mM Dibucaine, 5 mM EDTA, and 0.6 M NaCl). For RNA seq controls, cells were grown in liquid LB medium and collected at an OD600=0.5. The KdpDE TCS induces expression of the high affinity K+ transporter KdpFABC under K-limiting conditions (Schramke et al., 2016). According to Schramke et al., KdpD acts as a phosphatase on KdpE-P and prevents production of the high-affinity K+ transporter at high extracellular K+ concentration (>5 mM). When environmental levels of K+ fall below the threshold for autokinase activation, kdpFABC expression is initiated; however, as long as the intracellular K+ concentration remains high, the KdpD phosphatase activity remains stimulated (Schramke et al., 2016). Therefore, to induce KdpDE, cells were grown in K-sufficient conditions, washed and then grown under K-limiting conditions to ensure drops in intracellular K+ levels below the threshold level and thus proper induction of the TCS: Cells were grown at 37 °C overnight in liquid Tris maleic acid minimal medium (TMA) (44, 45) supplemented with 115 mM KCl and 0.4 % w/v Glucose, then washed twice with TMA containing 0.1 mM KCl and 0.4 % w/v Glucose. Cells were inoculated in TMA with 0.1 mM KCl and 0.4 % w/v Glucose and collected at an OD600=0.5 for ChIP-exo and RNA-seq analysis. For RNA seq controls, cells were grown in liquid TMA supplemented with 115 mM KCl and 0.4 % w/v Glucose, and collected at an OD600=0.5. For induction of the PhoRB TCS we chose to use M9 minimal medium without any phosphate, to ensure proper induction of the system. To our knowledge, this condition was not used before. To induce phosphate limiting conditions, cells were grown in liquid M9 minimal medium (containing phosphate) until OD600=0.5. Growing cells until the mid exponential growth phase was necessary, because the lack of phosphate led to a growth arrest. Then cells were washed three times with M9 minimal medium without phosphate (M9-P; without Na2HPO4 and KH2PO4) and incubated in M9-P for 60 min (about the doubling time of MG1655 in M9 minimal medium) at 37 °C. Cells were then collected for ChIP-exo and RNA-seq analysis. For RNA seq controls, ΔphoB cells were grown in liquid M9 minimal medium and collected at an OD600=0.5 (WT samples were taken from (7), see Table 1). For ZraSR: Cells were grown at 37 °C in liquid LB medium containing 1 mM ZnCl2, according to Leonhartsberger et al., 2001 (57) but without the addition of glucose to the LB medium. At an OD600=0.5 cells were collected for ChIP-exo and RNA-seq analysis. For RNA seq controls, cells were grown in liquid LB medium and collected at an OD600=0.5. Samples for ChIP-exo and RNA-seq were taken independently.
RNA-seq
For RNA-seq, 3 mL of culture were mixed with 6 mL of RNAprotect bacteria reagent (Qiagen) and processed according to the manufacturer’s instructions. Cell pellets were frozen and stored at −80 °C until processed. RNA was extracted using the Zymo Research Quick RNA fungal/bacterial microprep kit, according to the manufacturer’s instructions. Ribosomal RNA was removed from total RNA preparations using RNase H. First, traces of genomic DNA were removed with a DNase I treatment. Then, secondary structures in the ribosomal RNA were removed by heating to 90 °C for one second. A set of 32-mer DNA oligonucleotide probes complementary to 5S, 16S, and 23S rRNA subunits and spaced nine bases apart were then annealed at 65 °C followed by digestion with Hybridase (Lucigen), a thermostable RNase H. Hybridase was added at 65 °C, the reaction was incubated for 20 minutes at that temperature, then heated again to 90 °C for one second to remove remaining secondary structures, and finally returned to 65 °C for 10 minutes. The reaction was quickly quenched by the addition of guanidine thiocyanate while still at 65 °C before purifying the mRNA with a Zymo Research RNA Clean and Concentrator kit using their 200 nt cutoff protocol. Carryover oligos were removed with a DNase I digestion which was started at room temperature and gradually increased to 42 °C over a half hour. This was followed up with another column purification as stated above.
Paired-end library preparation was done using the KAPA RNA HyperPrep kit following the manufacturer’s instructions with an average insert size of 300 bp. The libraries were then analyzed on an Agilent Bioanalyzer DNA 1000 chip (Agilent). RNA-seq libraries were sequenced on the NextSeq 500 (Illumina) at the Salk Institute for Biological Studies, La Jolla, CA, USA. RNA-seq experiments were performed in duplicates.
ChIP-exo experiments
To identify binding sites for each response regulator under their respective induction conditions (see above and Table 1), ChIP-exo experiments were performed as described previously (64). Specifically, cells were crosslinked in 1% Formaldehyde and DNA bound to each response regulator was isolated by chromatin immunoprecipitation (ChIP) with the specific antibodies that recognize myc tag (9E10, Santa Cruz Biotechnology), and Dynabeads Pan Mouse IgG magnetic beads (Invitrogen). This was followed by stringent washing as described earlier (73). To perform on-bead enzymatic reactions of the ChIP-exo experiments, ChIP materials (chromatin-beads) were used (5). The method has been described in detail previously (64, 74). ChIP-exo experiments were performed in duplicates. ChIP-exo libraries were sequenced on the NextSeq 500 (Illumina) at the Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA, or on the NextSeq 500 (Illumina) at the Salk Institute for Biological Studies.
ChIP-exo processing and peak calling
The ChIP-exo sequence reads were mapped onto the reference genome (E. coli MG1655 NC_000913.3) using bowtie (75) with default options as also described previously (64, 74). This generated SAM output files. Peak calling from biological duplicates for each experimental condition was done using the MACE program (76). Two level filtering of ChIP-exo data was done: first, we removed any peaks with S/N ratio of less than 1.5 to reduce false positive peaks. Top 5% of the signals at genomic positions was set as noise level as previously described (64, 74). MetaScope (https://sites.google.com/view/systemskimlab/software) was used to visualise the peaks. To further reduce the false positive peaks, a cut-off analysis was done for each regulator on the signal-to-noise (S/N) ratio (Supplemental Figure S1). We removed any peaks that had S/N ratio below the threshold. Thereafter, the peak was assigned to the nearest operon on both strands. Operons located within 500 bp of the peak were considered. Specifically, if a peak was upstream of an operon, we considered all the genes in that operon to be regulated by that response regulator.
Analysis of Motif Enrichment (AME) for Consensus in ChIP-exo Peaks
After the final S/N thresholding and 500 bp cutoffs were applied to filter the ChIP-exo data, the resulting binding peaks were buffered with a margin of 20 base pairs on either end of their sequence then compared to the published consensus motif to identify matching sequences (Table 4). The search was performed by AME from the MEME suite (67) using a one-tailed Fisher’s exact test method to evaluate motif enrichment. The fraction of maximum log-odds for scoring a potential match was specified as 0.25, and the E-value for reporting was arbitrarily set at 10 (no TCS had more than 10 identified sequences). A 0-order Markov sequence model was created by AME to normalize for biased distributions; additionally, AME created a control sequence by shuffling the letters in each of the input sequences while preserving 2-mers.
Differential Gene Expression
RNA-seq sequence reads were mapped onto the reference genome (E. coli MG1655 NC_000913.3) using bowtie 1.1.2 (75) with the following options “-X 1000 -n 2 −3 3”, where X is maximum insert size, n is number of mismatches, and −3 3 denotes trimming of three base pairs at 3’-end. Read count was performed using summarizeOverlaps from the R GenomicAlignments package, using options “mode=“IntersectionStrict”, singleEnd=FALSE, ignore.strand=FALSE, preprocess.reads=invertStrand” (77). Differential gene expression was identified by DESeq2 (78). Genes with log2 fold change > 1.5 and a false discovery rate (FDR) value < 0.05 were considered as differentially expressed genes. Genes with p values assigned ‘NA’ based on extreme count outlier detection were not considered as potential DEGs.
Independent component analysis (ICA)
To identify independent sources of gene expression control, ICA was applied to the combined dataset that consisted of all 278 RNAseq gene expression data from PRECISE (7) and 32 datasets from this study which included gene expression data of knockout mutants and wild types under stimulated and unstimulated conditions. Genes with less than 10 fragments mapped per million reads across the entire dataset were removed from the RNA-seq datasets to reduce noise. FastICA was performed 100 times with random seeds and a convergence tolerance of 10−6. For each iteration, we constrained the number of components to the number that generates 99% variance in principal component analysis (PCA). We further used DBSCAN to cluster the resulting components to identify a set of robust independent components using a minimum cluster seed size of 50. We repeated this process five times and only the consistent components that occurred in each run were taken. iModulons were extracted as previously described (7).
iModulon Characterization
We checked for the significant genes in each iModulon and mapped it to the set of genes regulated by a specific regulator or transcription factor. This comparison was done using one-sided Fisher’s Exact Test (FDR < 10−5). The regulon/regulator with lowest p-value was given the name of the iModulon. The ones which were not characterized by this method were further analyzed through Gene Ontology (GO) enrichment. In this case, significant genes in each iModulon were compared to genes in each GO term and a p-value was assigned using the one-sided Fisher’s Exact Test (FDR < 10−5). The GO term having lowest p-value was assigned as the name of the iModulon.
Data Availability
The ChIP-exo and RNA-seq datasets have been deposited to GEO with the accession number of GSE143856. All other data is available in the supplementary data.
Supplementary materials file list
Figures
Supplemental Figure S1: Signal-to-noise (S/N) ratio for ChIP-exo data of BaeR, CpxR, KdpE, PhoB, and ZraR.
Supplemental Figure S2: ICA Activity profiles for BaeR, CpxR, KdpE, PhoB, and ZraR iModulons.
Tables
Supplemental Table S1: Oligonucleotides used in this study to add a 8-myc tag or to knock out genes.
Datasets
Supplemental Dataset S1: Genes and their weighting for each of the 15 identified TCS RR iModulons.
Supplemental Dataset S2: ChIP-exo binding peaks for BaeR, CpxR, KdpE, PhoB, and ZraR.
Supplemental Dataset S3: Differentially Expressed Genes (DEG) between wild-type and knockout strains for BaeR, CpxR, KdpE, PhoB, and ZraR.
Supplemental Dataset S4: Indirect target genes of BaeR, CpxR, KdpE, PhoB, and ZraR.
Supplemental Dataset S5: Potentially spurious ChIP-exo binding genes of BaeR, CpxR, KdpE, PhoB, and ZraR.
Supplemental Dataset S6: Hypothetically functional binding genes of BaeR, CpxR, KdpE, PhoB, and ZraR.
Supplemental Dataset S7: Cross-regulated genes of BaeR, CpxR, KdpE, PhoB, and ZraR.
Supplemental Dataset S8: Differential expression of TCS RR regulons under unstimulated and stimulated conditions.
Author contribution
KSC and BOP designed the study. JAK designed, generated the strains and performed the experiments. YG, RS assisted in generating RNA-seq and ChIP-exo data. KSC and KD processed the RNA-seq and ChIP-exo data and performed computational analysis. AVS and YS contributed in data analysis. KSC, KD, AVS and BOP wrote the manuscript, with contributions from all other authors.
Competing financial interests
The authors declare no financial competing interests.
Acknowledgements
We thank Irina Rodionova for helpful discussion. This work was funded by the Novo Nordisk Foundation Grant Number NNF10CC1016517. We thank Marc Abrams for reviewing the manuscript and providing constructive suggestions.
Footnotes
Author order was determined based on contributions to experiments and analysis.
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