Gene network analysis identifies a central post-transcriptional regulator of cellular stress survival

Abstract
Introduction
Results
Discussion
Materials and methods
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Abstract

Cells adapt to shifts in their environment by remodeling transcription. Measuring changes in transcription at the genome scale is now routine, but defining the functional significance of individual genes within large gene expression datasets remains a major challenge. We applied a network-based algorithm to interrogate publicly available gene expression data to predict genes that serve major functional roles in Caulobacter crescentus stress survival. This approach identified GsrN, a conserved small RNA that is directly activated by the general stress sigma factor, σ^T, and functions as a potent post-transcriptional regulator of survival across distinct conditions including osmotic and oxidative stress. Under hydrogen peroxide stress, GsrN protects cells by base pairing with the leader of katG mRNA and activating expression of KatG catalase/peroxidase protein. We conclude that GsrN convenes a post-transcriptional layer of gene expression that serves a central functional role in Caulobacter stress physiology.

https://doi.org/10.7554/eLife.33684.001

Introduction

Organisms must control gene expression to maintain homeostasis. A common mode of gene regulation in bacteria involves activation of alternative sigma factors (σ), which redirect RNA polymerase to transcribe genes required for adaptation to particular environmental conditions. Alphaproteobacteria utilize an extracytoplasmic function (ECF) σ factor to initiate a gene expression program known as the general stress response (GSR) (Figure 1A). The GSR activates transcription of dozens of genes, which mitigates the detrimental effects of environmental stressors and influences the infection biology of alphaproteobacterial pathogens (reviewed in [Fiebig et al., 2015; Francez-Charlot et al., 2015]). The molecular mechanisms by which genes in the GSR regulon enable growth and survival across a chemically- and physically distinct spectrum of conditions are largely uncharacterized. Defining the functional role(s) of individual genes contained within complex environmental response regulons, such as the GSR, remains a major challenge in microbial genomics.

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Iterative rank analysis of gene expression data identifies *gsrN*, a small RNA that confers resistance to hydrogen peroxide.

(A) Activation of general stress response (GSR) sigma factor, σ^T, promotes transcription of genes that mitigate the effects of environmental stress and genes that regulate σ^T activity. (B) Normalized transcript levels from (Fang et al., 2013) of known GSR regulated genes are plotted as a function of cell cycle time. The core GSR regulators, *sigT* and *phyR*, are highlighted in red and black, respectively. Data plotted from Figure 1—source data 1. (C) *sigT* and *phyR* transcript levels are correlated as a function of cell cycle progression, Pearson’s correlation coefficient r = 0.92. (D) An initial correlation-weighted network was seeded with experimentally defined GSR regulatory genes (red, value = 1) (left). Final ranks were calculated using the stable solution of the iterative ranking algorithm (right). Red intensity scales with the final rank weights (Figure 1—source data 2). A gene encoding a small RNA, *gsrN,* was a top hit on the ranked list. (E) Colony forming units (CFU) in dilution series (10⁻¹ to 10⁻⁶ dilution factor) of wild-type and mutant *Caulobacter* strains after 0.2 mM hydrogen peroxide treatment for 1 hr. Red denotes core GSR regulatory genes. Black denotes known σ^T–regulated genes. GenBank locus ID is indicated for unnamed genes.

https://doi.org/10.7554/eLife.33684.002

Figure 1—source data 1 Excel file of gene expression data from (Fang et al. , 2013) and estimated by Rockhopper (Tjaden, 2015). Each column represents the estimated transcript from total RNA-extractions of Caulobacter crescentus cultures at five time points post-synchronization. These values were used to construct the network.: https://doi.org/10.7554/eLife.33684.005
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Figure 1—source data 2 Excel file of the results from the iterative rank algorithm. Results can be recapitulated using the scripts in https://github.com/mtien/IterativeRank.: https://doi.org/10.7554/eLife.33684.006
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In the alphaproteobacterium Caulobacter crescentus (hereafter referred to as Caulobacter), strains lacking core regulators of the GSR have survival defects under multiple conditions including hyperosmotic and hydrogen peroxide stresses (Alvarez-Martinez et al., 2007; Foreman et al., 2012; Herrou et al., 2010; Lourenço et al., 2011). However, the majority of genes regulated at the transcriptional level by the Caulobacter GSR sigma factor, σ^T, have no annotated function or no clear role in stress physiology. While studies of transcription can provide understanding of stress responses, this approach may miss functionally important processes that are regulated at the post-transcriptional level, such as those controlled by small RNAs (sRNAs). Roles for sRNAs in bacterial stress response systems are well described (Wagner and Romby, 2015), but remain unexplored in the alphaproteobacterial GSR.

Regulatory roles and mechanisms of action of sRNAs are diverse: sRNAs can control gene expression by protein sequestration or by modulation of mRNA stability, transcription termination, or translation (Wagner and Romby, 2015). The system properties of environmental response networks are often influenced by sRNAs, which can affect the dynamics of gene expression via feedback (Beisel and Storz, 2011; Mank et al., 2013; Nitzan et al., 2015; Shimoni et al., 2007) or buffer response systems against transcriptional noise (Arbel-Goren et al., 2013; Golding et al., 2005; Levine and Hwa, 2008; Mehta et al., 2008). However, the phenotypic consequences of deleting sRNA genes are typically subtle and uncovering phenotypes often requires cultivation under particular conditions. Thus, reverse genetic approaches to define functions of uncharacterized sRNAs have proven challenging.

We applied a rank-based network analysis approach to predict functionally significant genes in the Caulobacter GSR regulon. The hypothesis motivating our analysis was that genes whose expression is most correlated to the core GSR regulators, calculated by iterative rank, would also be among the most important for stress response. This analysis led to the prediction that a sRNA, which we name GsrN, is a major genetic determinant of growth and survival under stress. We validated this prediction, demonstrating that gsrN is under direct control of σ^T and functions as a potent post-transcriptional regulator of survival across distinct conditions including hydrogen peroxide stress and hyperosmotic shock. We developed a novel forward biochemical approach to identify direct molecular targets of GsrN and discovered that peroxide stress survival is mediated through an interaction between GsrN and the 5’ leader sequence of katG, which activates KatG catalase/peroxidase expression. This post-transcriptional connection between σ^T and katG, a major determinant of peroxide stress and stationary phase survival (Italiani et al., 2011; Steinman et al., 1997), explains the peroxide sensitivity phenotype of Caulobacter strains lacking a GSR system.

Finally, we demonstrate that RNA processing and sRNA-mRNA target interactions shape the pool of functional GsrN in the cell, and that changes in GsrN expression enhance expression of some proteins while inhibiting others. The broad regulatory capabilities of GsrN are reflected in the fact that a gsrN deletion strain has survival defects across chemically- and physically distinct stress conditions, and support a model in which the GSR initiates layered transcriptional and post-transcriptional regulatory responses to ensure environmental stress survival.

Results

Iterative rank analysis of gene expression data identifies a small RNA regulator of stress survival

We applied a network-based analytical approach to interrogate published transcriptomic datasets (Fang et al., 2013) and predict new functional genetic components of the Caulobacter GSR system. We organized expression data for over 4000 genes (Figure 1B and Figure 1—source data 1) to create a weighted network. In our basic network construction, each gene in the genome was represented as a node and each node was linked to every other node by a correlation coefficient that quantified the strength of co-expression across all datasets (Figure 1C). Within this undirected graph, we aimed to uncover a GSR clique and thus more explicitly define the core functional components of the GSR regulon.

To identify uncharacterized genes that are strongly associated with the GSR, we utilized an iterative ranking approach related to the well-known PageRank algorithm (Brin and Page, 1998). We defined the ‘input’ set as sigT and the experimentally defined regulators of σ^T, which include the anti-sigma factor, nepR, the positive two-component regulators phyR and phyK, and the negative two-component regulators lovR and lovK, as well as the paralogous sigma factor, sigU (in red Figure 1D) (Alvarez-Martinez et al., 2007; Foreman et al., 2012; Herrou et al., 2010; Lourenço et al., 2011). We then optimized parameters through a systematic self-predictability approach (Figure 1—figure supplement 1A and Materials and methods - Iterative rank parameter tuning) and applied iterative ranking to compute a ranked list of genes with strong associations to the input set (Figure 1—source data 2). We narrowed our ranked list by performing a promoter motif search on all hits to predict direct targets of σ^T. ccna_R0081, a gene encoding an sRNA (Landt et al., 2008) with a consensus σ^T binding site (Figure 1—figure supplement 2A) in its promoter was a top hit in our rank list. We hereafter refer to this gene as gsrN (general stress response non-coding RNA) as expression of this sRNA is regulated by the GSR system (see below).

To test whether gsrN transcription requires the GSR sigma factor, σ^T, we generated a transcriptional reporter by fusing the gsrN promoter to lacZ (P_gsrNlacZ). Transcription from P_gsrN required sigT (Figure 1—figure supplement 2A,C), validating gsrN as a bona fide member of the GSR regulon. To determine whether gsrN is a feedback regulator of GSR transcription, we utilized a well-characterized P_sigUlacZ reporter (Foreman et al., 2012). As expected, transcription from P_sigU required sigT and other GSR regulators (phyR, phyK). However, this reporter was unaffected by deletion or overexpression of gsrN. Furthermore, deletion or overexpression of gsrN did not affect activation of P_sigU transcription upon addition of 150 mM sucrose, a known GSR inducer (Figure 1—figure supplement 2D). We conclude gsrN is activated by σ^T, but does not feedback to control GSR transcription.

We next tested whether gsrN plays a role in stress survival. We subjected strains lacking gsrN or the core GSR regulators, sigT, phyR, or phyK, to hydrogen peroxide, a known stress under which GSR regulatory mutants have a survival defect. ΔsigT, ΔphyR, and ΔphyK strains had a ≈4-log decrease in cell survival relative to wild type after exposure to hydrogen peroxide, as previously reported (Alvarez-Martinez et al., 2007; Foreman et al., 2012; Lourenço et al., 2011). Cells lacking gsrN (ΔgsrN) had a ≈3-log viability defect relative to wild type (Figure 1E and Figure 1—figure supplement 2B). Insertion of gsrN with its native promoter at the ectopic vanA locus fully complemented the peroxide survival defect of ∆gsrN (Figure 2A and Figure 2—figure supplement 1C). These data provide evidence that gsrN is a major genetic contributor to cell survival upon peroxide exposure. To query if other σ^T-regulated genes are important for peroxide survival, we selected 10 additional genes that are strongly regulated by σ^T based on past transcriptome studies (Alvarez-Martinez et al., 2007; Foreman et al., 2012) and generated strains harboring single, in-frame deletions of these genes. The functions of these 10 genes are unknown: six encode conserved hypothetical proteins; two encode predicted outer membrane proteins; one encodes a cold shock protein, and one encodes a ROS/MUCR transcription factor. None of these additional deletion strains were sensitive to hydrogen peroxide (Figure 1E and Figure 1—figure supplement 2B).

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GsrN is necessary for hydrogen peroxide stress survival, and expression of GsrN is sufficient to confer peroxide protection in a *sigT* null background.

(A) *Caulobacter* wild type (WT), *gsrN* deletion (Δ*gsrN*), complementation (Δ*gsrN + gsrN*), and *gsrN* overexpression (*gsrN*⁺⁺) strains were subjected to increasing concentrations of hydrogen peroxide for 1 hr and titered on nutrient agar. Complementation and overexpression strains carry plasmids with one or three copies, respectively, of *gsrN* with its native promoter integrated at the ectopic *vanA* locus (see Figure 2—figure supplement 1A for details). Δ*gsrN* and WT strains carried the empty plasmid (pMT552) integrated at the *vanA* locus. Log₁₀ relative CFU (peroxide treated/untreated) is plotted as a function of peroxide concentration. Mean ±SD, n = 3 independent replicates. (B) Northern blot of total RNA isolated from WT and Δ*sigT* strains expressing *gsrN* from its native promoter (P_sigT) or from two constitutive σ^RpoD promoters (P₁ or P₂); probed with ³²P-labeled oligonucleotides specific for GsrN and 5S rRNA as a loading control. Labels on the left refer to 5S rRNA (5S in black), full-length GsrN (FL in dark blue), and the 5’isoform of GsrN. (5’ in cyan) Quantified values are mean ±SD of normalized signal, n = 3 independent replicates. (C) Relative survival of strains in (B) treated with 0.2 mM hydrogen peroxide for 1 hr normalized as in (A). Mean ±SD from three independent experiments (points) is presented as bars.

https://doi.org/10.7554/eLife.33684.007

Expression of GsrN confers protection from peroxide stress

Results outlined above demonstrate that gsrN is necessary for hydrogen peroxide stress survival. To assess the effects of gsrN overexpression, we inserted constructs containing either one or three copies of gsrN under its native promoter into the vanA locus of wild-type and ∆gsrN strains (Figure 2—figure supplement 1A). We measured GsrN expression directly in these strains by Northern blot (Figure 2—figure supplement 1B) and tested their susceptibility to hydrogen peroxide (Figure 2A). Treatment with increasing concentrations of hydrogen peroxide revealed that strains overexpressing gsrN have a survival advantage compared to wild type. Measured levels of GsrN in the cell directly correlated (r = 0.92) with cell survival providing evidence that gsrN confers dose dependent protection from peroxide stress over the measured range (Figure 2—figure supplement 1C).

Given that σ^T regulates many genes, we sought to test if gsrN expression was sufficient to mediate cell survival under peroxide stress in a strain lacking σ^T (and thus GSR transcription). To decouple gsrN transcription from σ^T, we constitutively expressed gsrN from promoters (P1 and P2) controlled by the primary sigma factor, RpoD, in a strain lacking sigT (Figure 2—figure supplement 1A). gsrN expression from P1 was 15% higher, and expression from P2 50% lower than gsrN expressed from its native σ^T-dependent promoter (Figure 2B). Expression of gsrN from P1, but not P2, rescued the ΔsigT peroxide survival defect (Figure 2C). We conclude that gsrN is a major genetic determinant of hydrogen peroxide survival regulated downstream of σ^T under these conditions. Consistent with the dose dependent protection by GsrN, these data demonstrate that a threshold level of gsrN expression is required to protect the cell from hydrogen peroxide.

GsrN is endonucleolytically processed into a more stable 5’ isoform

A notable feature of GsrN is the presence of two isoforms by Northern blot. Probes complementary to the 5’ portion of GsrN reveal full-length (≈100 nucleotide) and short (51 to 54 nucleotides) isoforms while probes complementary to the 3’ portion reveal mostly full-length GsrN (Figure 3A and Figure 3—figure supplement 1A). Smaller 3’ isoforms are apparent as minor species when high concentrations of total RNA are probed (Figure 3—figure supplement 1A). Two isoforms of GsrN are also evident in RNA-seq data (Figure 3—figure supplement 1B).

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Full-length GsrN is endonucleolytically processed into a stable 5’ isoform.

(A) Northern blots of total RNA from wild-type and Δ*gsrN* cells hybridized with probes complementary to the 5’end (left) or 3’ end (right) of GsrN, and to 5S rRNA as a loading control. (B) Predicted secondary structure of full-length GsrN using RNA-specific folding parameters (Andronescu et al., 2007). Cyan indicates the 5’ end of GsrN determined by primer extension. Pink represents the 3’ end. Nucleotide positions labeled with arrows provides context for the mutants in Figure 4. (C) Primer extension from total RNA extracted from *gsrN⁺⁺* and Δ*gsrN* (negative control) cultures (OD₆₆₀ ≈ 1.0, a condition in which GsrN levels were observed to be the highest). Sequence was generated from a radiolabeled oligo anti-sense to the underlined cyan sequence in (E). Sanger sequencing control lanes A, C, G, and T mark the respective ddNTP added to that reaction to generate nucleotide specific stops. C’ labels on the right of the gel indicate mapped positions from the ‘G’ lane. Arrow indicates lane without ddNTPs. Asterisk indicates positions of 5’ termini. (D) Primer extension from RNA samples as in (C). Sequence was extended from a radiolabeled oligo anti-sense to the underlined pink sequence in (E). (E) GsrN coding sequence. Cyan and pink indicate the predicted 5’ and 3’ isoforms, respectively. Primers binding sites used for primer extension in (C) and (D) are underlined. Highlighted C positions correspond to ddGTP stops in the ‘G’ extensions. Black arrowheads correspond to the termini identified by 5’RACE (Figure 3—figure supplement 1).

https://doi.org/10.7554/eLife.33684.009

The short isoform of gsrN could arise through two biological processes: alternative transcriptional termination or endonucleolytic processing of full-length GsrN. To begin to discriminate between these two possibilities, we inhibited transcription with rifampicin, and monitored levels of both GsrN isoforms over time. Full-length GsrN decayed exponentially with a half-life of ~105 s (Figure 3—figure supplement 1C,D). The 5’ isoform increased in abundance for several minutes after treatment, concomitant with the decay of the full-length product. This observation is consistent with a model in which the 5’ isoform arises from the cleavage of the full-length product.

To identify potential endonucleolytic cleavage sites, we conducted primer extension assays to map the 5’ termini of the isoforms. Primer extension binding sites are shown in (Figure 3E). Extension from an oligo complementary to the 5’ portion of GsrN confirmed the annotated transcriptional start site (Figure 3C). Extension from the 3’ portion identified two internal 5’ ends (Figure 3D). The positions of these internal 5’ ends are consistent with two small bands observed on Northern blots of high concentrations of total RNA hybridized with the 3’ probe (Figure 3—figure supplement 1A). The terminus around C53 corresponds to a potential endonucleolytic cleavage site that would generate the abundant stable 5’ isoform (Figure 3B).

To directly test if the 5’ termini identified by primer extension and supported by Northern blotting reflect termini generated by cleavage or by transcription initiation, we implemented Rapid Amplification of cDNA 5’ends (5’RACE) with differential tobacco acid pyrophosphatase (TAP) treatment (Bensing et al., 1996). In this protocol, cDNAs with 5’ termini formed by transcription initiation are amplified only with TAP treatment, whereas those with ends generated by processing are amplified with or without TAP treatment. We were able to clone cDNAs corresponding to both 3’isoforms in both TAP-treated and untreated total RNA samples of gsrN⁺⁺ cultures (Figure 3—figure supplement 3E). The ends of these clones (T54 and T64) are consistent with the ends mapped by primer extension. Together, these results support a model in which the full-length GsrN transcript is endonucleolytically processed into a stable 5’ isoform and a less stable 3’ isoform.

Hfq stabilizes full-length GsrN

sRNAs are often associated with the bacterial RNA chaperone, Hfq, and in some cases also associated with another RNA chaperone ProQ (Gottesman and Storz, 2011; Smirnov et al., 2016; Vogel and Luisi, 2011). Caulobacter, however, does not have an obvious ProQ homologue. To test the influence of hfq on GsrN, we created deletion (∆hfq) and overexpression (hfq⁺⁺) strains. We observed that hfq affects GsrN processing. In ∆hfq strains, full-length GsrN is undetectable by Northern blot, even when gsrN is overexpressed (Figure 3—figure supplement 2). Conversely, overexpression of hfq⁺⁺ results in increased levels of full-length GsrN that exceed levels of the 5’isoform. We conclude that hfq influences the processing of GsrN in vivo. We note that the growth rate of both these strains is significantly attenuated in defined M2X medium. Moreover, large granules have been observed microscopically in ∆hfq strains (Irnov et al., 2017). Given the pleiotropic consequences of hfq deletion, stress survival phenotypes for these strains are difficult to interpret.

5’ end of GsrN is necessary to mediate peroxide survival

To test the function of the 5’ end of GsrN, we integrated a gsrN allele that contains only the first 58 nucleotides (Δ59–106), and lacks the transcriptional terminator (gsrNΔ3’) into the vanA locus (Figure 4A). This short gsrN allele complemented the ∆gsrN peroxide survival defect (Figure 4B). The gsrN(Δ3’) allele produced a 5’ isoform that was comparable in size and concentration to the wild-type 5’ gsrN isoform. Since the transcriptional terminator of gsrN was removed, we also observed a ~ 200 nt run-on transcript from gsrN(Δ3’) (Figure 4C).

Figure 4

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5’ end of GsrN is necessary and sufficient for peroxide survival.

(A) Schematic diagram of GsrN(Δ3’), which lacks nucleotides 59–106, which includes the intrinsic terminator. Nucleotide positions are highlighted in Figure 3B. (B) Relative survival of strains treated with 0.2 mM hydrogen peroxide for 1 hr. WT and Δ*gsrN* strains carry empty intergrated plasmids (EV) or integrated plasmids harboring full-length *gsrN*, *gsrN(*Δ3’), or multiple copies of *gsrN(*Δ3’) (labeled *gsrN(*Δ3’)⁺⁺). Bars represent mean ±SD from four independent experiments (points). (C) Northern blot of total RNA from strains in panel 3B harvested during exponential growth phase. Blots were hybridized with probes complementary to the 5’ end of GsrN and 5S rRNA. Mean ±SD of total GsrN signal from three independent samples. (D) Schematic diagram of GsrN(Δ5’), which lacks nucleotides 10–50, but contains the intrinsic terminator of GsrN (the terminal 3’ hairpin). Nucleotide positions are highlighted in Figure 3B. (E) Relative survival of strains treated with 0.2 mM hydrogen peroxide for 1 hr. Genetic backgrounds are indicated above the line; the GsrN(Δ5’) strain was complemented with either *gsrN* (dark blue) or GsrN(Δ5’) (cyan). Bars represent mean ±SD from at least two independent experiments (points).

https://doi.org/10.7554/eLife.33684.012

To test whether the 5’ end of GsrN is necessary for peroxide stress survival, we deleted nucleotides 10 to 50 of gsrN at its native locus (Figure 4D). The gsrN(Δ5’) strain had a peroxide viability defect that was equivalent to ΔgsrN. Ectopic expression of either full-length gsrN or gsrN(Δ3’) in the gsrN(Δ5’) strain complemented this peroxide survival defect (Figure 4E).

Several RNAs, including katG mRNA, co-purify with GsrN

We developed a forward biochemical approach to identify molecular partners of GsrN. The Pseudomonas phage7 (PP7) genome contains hairpin (PP7hp) aptamers that bind to PP7 coat protein (PP7cp) with nanomolar affinity (Lim and Peabody, 2002). We inserted the PP7hp aptamer into multiple sites of gsrN with the goal of purifying GsrN with its interacting partners from Caulobacter lysates by affinity chromatography (Figure 5A), similar to an approach used by (Hogg and Collins, 2007; Said et al., 2009). PP7hp insertions at the 5’ end of gsrN and at several internal nucleotide positions (37, 54, 59, 67, and 93nt) were functionally assessed (Figure 5—figure supplement 1A). GsrN-PP7hp alleles tagged at the 5’ end or at nucleotide positions 54 or 59 did not complement the ∆gsrN peroxide survival defect (Figure 5—figure supplement 1B). These alleles yielded lower steady-state levels of 5’ isoform compared to wild type (Figure 5—figure supplement 1C,D). GsrN-PP7hp alleles with insertions at nucleotides 37, 67, and 93 restored peroxide resistance to ΔgsrN and produced more 5’ isoform than non-complementing GsrN-PP7 constructs (Figure 5—figure supplement 1).

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GsrN co-purifies with multiple RNAs, including catalase/peroxidase *katG* mRNA.

(A) GsrN-target co-purification strategy. GsrN(black)-PP7hp(purple) fusions were expressed in a Δ*gsrN* background. PP7 RNA hairpin (PP7hp) inserted at nucleotide 37 (*gsrN*(37)::PP7hp) was used as the bait. PP7hp fused to the 3’ hairpin of *gsrN* (PP7hp::*gsrN*-3’)served as a negative control. Stationary phase cultures expressing these constructs were lysed and immediately flowed over an amylose resin column containing immobilized PP7hp binding protein (MBP-PP7cp-His). (B) GsrN-PP7hp purification from strains bearing *gsrN*(37)::PP7hp (left) and PP7hp::*gsrN*-3’ (right) was monitored by Northern Blot with probes complementary to 5’ end of GsrN and PP7hp, respectively. Lysate, flow through (FT), buffer wash, and elution fractions are blotted. Approximately 1 µg RNA was loaded per lane, except for buffer wash (insufficient amount of total RNA). (C) Annotation-based analysis of transcripts that co-purify with *gsrN*(37)::PP7hp (Figure 5—source data 1). Log₁₀ reads per kilobase per million reads (RPKM) is plotted against the ln(-log₁₀(false discovery rate corrected p-value)). Dashed red lines mark the enrichment co-purification thresholds. Genes enriched in the *gsrN*(37)::PP7hp purification compared to PP7hp::*gsrN*-3’ are blue; labels correspond to gene names or *C. crescentus* strain NA1000 CCNA GenBank locus ID. Data represent triplicate purifications of *gsrN*(37)::PP7hp and duplicate PP7hp::3’GsrN control purifications. Log adjusted p-values of zero are plotted as 10⁻²⁶⁰. (D) Sliding-window analysis of transcripts that co-purify with *gsrN*(37)::PP7hp (Figure 5—source data 2). Points represent 25 bp genome windows. RPKM values for each window were estimated by EDGE-pro; p-values were estimated by DESeq. Windows that map to genes identified in (C) are blue. Orange indicates windows with significant and highly abundant differences in mapped reads between *gsrN*(37)::PP7hp fractions and the PP7hp::*gsrN*-3’ negative control fractions. Dashed red lines denote cut-off value for windows enriched in the *gsrN*(37)::PP7hp fractions. Grey points within the dashed red lines are signal that mapped to rRNA. (E) Predicted loops in GsrN accessible for mRNA target base pairing are emphasized in colored texts. A putative mRNA target site complementary to a cytosine-rich tract in the 5’ GsrN loop is represented as a sequence logo. Similar logo was generated for the target site sequences complementary to the 2^nd exposed region in the 3’ end of GsrN. Logo was generated from IntaRNA 2.0.2 predicted GsrN-binding sites in transcripts enriched in the *gsrN*(37)::PP7hp pull-down. 5’ binding motif is present in 32 of the transcripts identified in (C) and (D) and 3’ binding motif is present in 27 of the transcripts identified in (C) and (D). (F) Density of reads mapping to *katG* that co-purified with *gsrN*(37)::PP7hp (blue) and PP7hp::*gsrN*-3’ (red). Read density in each dataset represents read coverage at each nucleotide divided by the number of million reads mapped in that data set. Data represent mean ±SD of three replicate *gsrN*(37)::PP7hp and two replicate PP7hp::*gsrN*-3’ purifications.

https://doi.org/10.7554/eLife.33684.013

Figure 5—source data 1 Excel file of the output from Rockhopper analysis (Tjaden, 2015) on the RNA-Seq samples from the PP7 affinity purified total RNA samples. Figure 5C can be created using the python and R scripts in https://github.com/mtien/Sliding_window_analysis.: https://doi.org/10.7554/eLife.33684.015
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Figure 5—source data 2 Zipped file contain three files. These files include the sliding window analysis files generated from mapping the reads from the RNA-Seq experiment of the PP7 affinity purified total RNA samples. Figure 5D can be created using the scripts in https://github.com/mtien/Sliding_window_analysis.: https://doi.org/10.7554/eLife.33684.016
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Figure 5—source data 3 FASTA file that contains the windows of enrichment and total gene sequences of genes identified in the PP7 affinity purified total RNA samples.: https://doi.org/10.7554/eLife.33684.017
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The PP7hp aptamer inserted at gsrN nucleotide 37 (GsrN(37)::PP7hp) was selected as the bait to identify molecular partners that co-purify with GsrN, as this was the only functional insertion in the 5’ half of gsrN: as presented above, the 5’ end is necessary for function and the 5’ isoform is more abundant than the full-length transcript. The pull-down fraction was compared to a negative control pull-down from cells expressing PP7hp fused to the last 50 nucleotides of GsrN including its intrinsic terminator (PP7hp::GsrN-3’) (Figure 5A). Northern blots demonstrated GsrN-PP7hp fusion transcripts were enriched in our purification (Figure 5B). Electrophoretic separation of the eluate followed by silver staining revealed no significant protein differences between GsrN(37)::PP7hp and the negative control (data not shown). Lack of differential protein signal may be due to the conditions in which we performed the pull-down. We identified and quantified co-eluting RNAs by RNA-seq.

We employed two approaches to identify RNAs enriched in GsrN(37)::PP7hp fractions relative to the negative control fractions. A conventional RNA-seq pipeline (Tjaden, 2015) quantified mapped reads within annotated gene boundaries as a first pass (Figure 5C and Figure 5—source data 1). To capture reads in non-coding and unannotated regions, and to analyze reads unevenly distributed across genes, we also developed a sliding window analysis approach. Software we developed to implement Sliding Window Analysis is available on GitHub (Tien, 2017b). Specifically, we organized the Caulobacter genome into 25 base-pair windows and quantified mapped reads in each window using the EDGE-pro/DESeq pipeline (Anders and Huber, 2010; Magoc et al., 2013). Together, these two quantification strategies identified several mRNA, sRNAs, and untranslated regions enriched in the GsrN(37)::PP7hp pull-down fraction (Figure 5D and Figure 5—source data 2). We applied IntaRNA 2.0.2 (Mann et al., 2017) to identify potential binding sites between GsrN and the enriched co-purifying RNAs. Of the 67 analyzed enriched genes and regions, 32 of the predicted RNA-RNA interactions involved the cytosine-rich 5’ loop in the predicted secondary structure of GsrN (Figure 5E and Figure 5—source data 3); 31 of targets contained G-rich sequences (Table 1). We note that exposed C-rich motifs in sRNAs and G-rich regulatory sequences in mRNA have been reported in several sRNA systems (Geissmann et al., 2009; Papenfort et al., 2008; Romilly et al., 2014; Sharma et al., 2010). A sequence logo (Crooks et al., 2004) of the predicted target mRNA binding sites is enriched with guanosines (Figure 5E), consistent with a model in which six tandem cytosines in the 5’ loop of GsrN determine target mRNA recognition. Twenty-seven of the predicted RNA-RNA interactions involved the 3’ exposed region of GsrN. The remaining eight enriched genes and regions did not have a significant binding site prediction with GsrN.

Table 1

RNAs that co-elute with GsrN-PP7hp.

https://doi.org/10.7554/eLife.33684.018

Gene locus ID	Gene name	log₂ Fold	Identification method	Region(s)	Description	Interacting nucleotides
CCNA_00167	-	4.56, 6.95	Rockhopper, Sliding window	179311–180120 (+),179500–179550 (+, I, S)	metallophosphatase family protein	-
CCNA_00416	-	7.2	Sliding window	429625–429725 (-, I, S)	conserved hypothetical membrane protein	GGCGGAGGG
CCNA_00587	-	4.87	Sliding window	616250–616300 (+, I, S)	alpha/beta hydrolase family protein	UCGGCGGGGGGC
CCNA_00882	-	4.61	Sliding window	962875–962925 (-, U, S)	hypothetical protein	UCGGGGGGU
CCNA_00894	-	4.29	Sliding window	974800–974850 (+, I, S)	1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate synthase	UCAAGUCGGGGC
CCNA_00897	-	3.2	Rockhopper	976013–976177 (+)	hypothetical protein	-
CCNA_00913	-	7.64, 7.80	Rockhopper, Sliding window	993033–993209 (-), 993175–993225 (-, I, S)	hypothetical protein	UCAAGUU
CCNA_00930	-	3.72, 6.98, 3.81	Rockhopper, Sliding window, Sliding window	1006253–1006870 (+),1006275–1006425 (+, I, S),1006475–1006650 (+, I, S)	riboflavin synthase alpha chain	CGGGUCGGGGGUG
CCNA_01024	-	3.32	Rockhopper	1111617–1112111 (-)	hypothetical protein	CAGGCGGGGGG
CCNA_01058	-	5.81	Sliding window	1159075–1159125 (-, D, S)	helix-turn-helix transcriptional regulator	CGGGGGGU
CCNA_01154	-	3.45, 6.81	Rockhopper, Sliding window	1257902–1258591 (+), 1257975–1258025 (+, I, S)	conserved hypothetical protein	GGGGGCG
CCNA_01303	-	5.87, 5.30, 8.22	Rockhopper, Sliding window, Sliding window	1430061–1430900 (+),1430550–1430625 (+, I, S),1430650–1430725 (+, I, S)	conserved hypothetical protein	GGGUCGGGGG
CCNA_01304	-	2.9	Rockhopper	1431129–1431329 (+)	hypothetical protein	GGUUCGCGGACG
CCNA_01335	-	2.99	Sliding window	1448600–1448650 (-, I, S)	ABC-type multidrug transport system, ATPase component	UCGCGUCGA
CCNA_01344	-	4.62	Sliding window	1458550–1458725 (+, I, S)	conserved hypothetical protein	GUCGGGGGUG
CCNA_01584	-	3.14	Sliding window	1699675–1699725 (+, I, A)	multimodular transpeptidase-transglycosylase PBP 1A	GGGGGGC
CCNA_01660	-	4.41, 6.21	Rockhopper, Sliding window	1781219–1781911 (-), 1781350–1781575 (-, I, S)	conserved hypothetical protein	GGGGGCG
CCNA_01966	-	11.3	Sliding window	2110225–2110275 (-, I, A)	vitamin B12-dependent ribonucleotide reductase	GGUCGGGG
CCNA_01996	-	9.15, 8.86	Rockhopper, Sliding window	2142908–2143687 (-), 2143625–2143700 (-, I, S)	undecaprenyl pyrophosphate synthetase	CGGGGGGC
CCNA_02034	-	7.24	Sliding window	2178500–2178550 (+, I, S)	luciferase-like monooxygenase	UCGAUGGGGGGCG
CCNA_02064	lpxC	3.6	Sliding window	2215450–2215550 (-, I, S)	UDP-3-O-(3-hydroxymyristoyl) N-acetylglucosamine deacetylase	UCGGGGGCG
CCNA_02089	-	8.52	Rockhopper	2237967–2238341 (-)	hypothetical protein	UCAAGUCGGGG
CCNA_02217	-	4.02	Rockhopper	2364081–2364383 (-)	hypothetical protein	GCGCGACGAAGG
CCNA_02286	-	3.26	Sliding window	2435450–2435500 (-, I, S)	hypothetical protein	UCCGGUCGCCCGG
CCNA_02595	-	6.85	Sliding window	2743525–2743625 (-, U, S)	Zn finger TFIIB-family transcription factor	UCGCAUCGA
CCNA_02758	-	2.93	Rockhopper	2921763–2922152 (+)	hypothetical protein	UCGCGUC
CCNA_02761	-	3.65	Rockhopper	2923673–2923918 (+)	hypothetical protein	CGGAGGGG
CCNA_02846	-	5.44, 8.60	Sliding window, Sliding window	3000100–3000175 (-, I, S),2999225–2999275 (-, I, S)	DegP/HtrA-family serine protease	AAGUCGGGGGGCG
CCNA_02860	-	3.70, 4.78	Rockhopper, Sliding window	3012116–3013060 (-),3012500–3012550 (-, I, S)	DnaJ-class molecular chaperone	CGGCAAG
CCNA_02975	-	6.34	Sliding window	3130300–3130375 (-, I, A)	excinuclease ABC subunit C	GCGGGGG
CCNA_02987	-	7.26	Sliding window	3142700–3142800 (-, I, A)	hypothetical protein	GUCGGGGGGCGUC
CCNA_02997	cspA	3.61	Rockhopper	3152607–3152816 (-)	cold shock protein CspA	-
CCNA_03002	-	6.03, 4.48	Rockhopper, Sliding window	3155705–3156322 (-),3155750–3155800 (-, I, S)	CDP-diacylglycerol--glycerol-3-phosphate 3-phosphatidyltransferase	-
CCNA_03105	-	9.15	Sliding window	3255775–3255850 (-, I, S)	DnaJ domain protein	AAGUCGGGGGGUGU
CCNA_03113	-	3.50, 5.40	Rockhopper, Sliding window	3263780–3264499 (-),3264400–3264450 (-, I, S)	membrane-associated phospholipid phosphatase	UUGUAUCG
CCNA_03138	katG	3.35	Sliding window	3286000–3286050 (+, I, S)	peroxidase/catalase katG	GUCGGGG
CCNA_03176	-	2.83	Rockhopper	3335155–3335445 (-)	nucleotidyltransferase	GAGUUCGCG
CCNA_03338	tolB	5.27	Sliding window	3519425–3519475 (-, I, S)	TolB protein	UCGCGAGGG
CCNA_03409	-	4.46, 5.44	Rockhopper, Sliding window	3576740–3577696 (-),3577550–3577600 (-, I, S)	alpha/beta hydrolase family protein	GGUUUGUGAAGGG
CCNA_03506	-	3.27, 4.10	Rockhopper, Sliding window	3664090–3664677 (+),3664100–3664175 (+, I, S)	putative transcriptional regulator	GUUGGGGGG
CCNA_03589	sigT	3.58	Rockhopper	3743953–3744558 (-)	RNA polymerase EcfG family sigma factor sigT	-
CCNA_03590	nepR	3.43, 3.50	Rockhopper, Sliding window	3744561–3744746 (-), 3744675–3744725 (-, I, S)	anti-sigma factor NepR	GGGGGGCG
CCNA_03590,CCNA_03589	nepR-sigT	4.42	Sliding window	3744500–3744575 (-, O, S)	anti-sigma factor NepR, RNA polymerase EcfG family sigma factor sigT	GAGCGUCAACGA
CCNA_03617	-	3.5	Rockhopper	3772262–3772717 (+)	Copper(I)-binding protein	-
CCNA_03618,CCNA_03617	-,-	6.99	Sliding window	3772700–3772750 (+, O, S)	SCO1/SenC family protein, Copper(I)-binding protein	GUCGGGG
CCNA_03681	-	5.11	Sliding window	3843700–3843750 (-, U, S)	ABC transporter ATP-binding protein	UCAGUUGGGG
CCNA_03825	-	3.63	Rockhopper	3991412–3991774 (-)	hypothetical protein	GGGGGCGU
CCNA_03825,CCNA_03826	-,-	8.01	Sliding window	3991750–3991825 (-, O, S)	hypothetical protein, conserved hypothetical protein	GGGGGCGU
CCNA_03826	-	3.71	Rockhopper	3991771–3992325 (-)	conserved hypothetical protein	-
CCNA_03888	-	2.99	Rockhopper	761965–762324 (+)	conserved hypothetical protein	GCGGUCCGG
CCNA_03976	-	3.57	Rockhopper	2923462–2923683 (+)	hypothetical protein	GAGCGCGUCGGCA
CCNA_R0016	-	8.53	Rockhopper	844332–844401 (+)	small non-coding RNA	UCGGGGG
CCNA_R0035	-	6.64	Rockhopper	1549367–1549443 (+)	tRNA-Pro	AAGGGGU
CCNA_R0044	-	4.82	Rockhopper	2059848–2059942 (-)	complex medium expressed sRNA	-
CCNA_R0061	-	4.65	Sliding window	2800475–2800525 (-, I, S)	RNase P RNA	UAGGUCGGGGC
CCNA_R0089	-	3.3	Sliding window	3874375–3874425 (+, U, S)	tRNA-Ala	UCGGGGGGCG
CCNA_R0100	-	4.4	Rockhopper	165492–165575 (+)	small non-coding RNA	CGGAGGG
CCNA_R0108	-	4.27	Rockhopper	472905–472973 (+)	small non-coding RNA	UCGGGGG
CCNA_R0180	-	5.14	Rockhopper	3266851–3266937 (-)	small non-coding RNA	-

Gene Locus ID: GenBank locus ID

Gene Name: if available log₂Fold: calculated fold change of the given region
Identification Method: refers to what strategy identified the enriched gene in the PP7hp affinity purification RNA-Seq

Region(s): the region and strand used to calculate the log₂Fold metric. Additionally for the sliding window analysis additional information is provided. First letter indicates the relative position of the region indicated to the annotated gene coordinates: I-internal, U-upstream, D-downstream. Second letter indicates the direction in which the reads mapped: S-sense, A-anti-sense.
Description: product description of the given gene(s)

Interacting nucleotides: the nucleotides within the proposed chromosomal region(s) that are predicted to interact with GsrN.

Transcripts enriched in the GsrN(37)::PP7hp fraction encode proteins involved in proteolysis during envelope stress, enzymes required for envelope biogenesis, cofactor and nucleotide anabolic enzymes, and transport proteins (Table 1). sigT and its anti-σ factor, nepR, were also enriched in the GsrN(37)::PP7hp fraction, though we found no evidence for regulation of σ^T/NepR by GsrN (Figure 1—figure supplement 2D). We observed significant enrichment of rRNA in the GsrN(37)::PP7hp fractions; the functional significance of this signal is not known (grey points above and to the right of the red cut-off Figure 5D). katG, which encodes the sole catalase-peroxidase in the Caulobacter genome (Marks et al., 2010), was among the highly enriched mRNAs in our pull-down. Specifically, reads mapping to the first 60 nucleotides of katG including the 5’ leader sequence and the first several codons of the open-reading frame were enriched in the GsrN(37)::PP7hp pull-down fraction relative to the negative control (Figure 5F). katG was an attractive GsrN target to interrogate the mechanism by which GsrN determines cell survival under hydrogen peroxide stress.

GsrN base pairing to the 5’ leader of katG activates katG translation, and enhances peroxide stress survival

Most bacterial sRNAs regulate gene expression at the transcript and/or protein levels through Watson-Crick base pairing with the 5’end of their mRNA targets (Wagner and Romby, 2015). We sought to test whether GsrN affected the expression of katG. GsrN did not effect katG transcription in exponential or stationary phases, or in the presence of peroxide as measured by a katG-lacZ transcriptional fusion (Figure 6—figure supplement 1A–C). However, katG is transcriptionally regulated by the activator OxyR, which binds upstream of the predicted −35 site in the katG promoter (Italiani et al., 2011). To decouple the effects of OxyR and GsrN on katG expression, we generated a strict katG translational reporter that contains the mRNA leader of katG fused in-frame to lacZ (katG-lacZ) constitutively expressed from a σ^RpoD-dependent promoter. In both exponential and stationary phases, katG-lacZ activity is reduced in ∆gsrN and enhanced in gsrN⁺⁺ strains compared to wild type (Figure 6—figure supplement 1D,F). Hydrogen peroxide exposure did not affect katG-lacZ activity (Figure 6—figure supplement 1E). We conclude that GsrN enhances KatG protein expression, but not katG transcription.

We then used this translational reporter to investigate a predicted binding interaction between the unpaired 5’ loop of GsrN and a G-rich region at the 5’ end of the katG transcript. Specifically, the first 7 nucleotides of katG mRNA (Zhou et al., 2015) are complementary to seven nucleotides in the single-stranded 5’ loop of GsrN, including 4 of the six cytosines (Figure 6A). We disrupted this predicted base pairing, mutating 5 of the seven nucleotides in the putative katG target site and GsrN interaction loop. These mutations preserved GC-content, but reversed and swapped (RS) the interacting nucleotides (Figure 6A). We predicted that pairs of wild-type and RS mutant transcripts would not interact, while base pairing interactions would be restored between RS mutant pairs.

Figure 6 with 3 supplements see all

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GsrN base pairs with the 5’ leader of *katG* mRNA and enhances KatG expression.

(A) Predicted interaction between GsrN (blue) and *katG* mRNA (green), with base-pairing shown in dashed box. Wild-type (WT) and reverse-swapped (RS) mutation combinations of the underlined bases are outlined below. (B) Translation from *katG* and *katG-RS* reporters in Δ*gsrN* strains expressing 3gsrN (WT) or 3gsrN(RS) (RS). Measurements were taken from exponential phase cultures. Bars represent mean ±SD of at least two independent cultures (points). ** p-value<0.01 estimated by Student’s t-test. (C) Relative hydrogen peroxide survival of RS strains. Δ*gsrN* strains expressing 3gsrN or 3gsrN(RS) and encoding *katG* or *katG(RS)* alleles. Bars represent mean ±SD from three independent experiments (points). (D) Northern blot of total RNA from strains in (C) collected in exponential phase hybridized with probes complementary to 5’ end of GsrN and 5S rRNA. Quantification is mean ±SD normalized signal from three independent experiments. **** p-value<0.0001 estimated by Student’s t-test.

https://doi.org/10.7554/eLife.33684.019

Mutating the predicted target site in the katG 5’ leader ablated GsrN-dependent regulation of the katG-lacZ translational reporter (Figure 6—figure supplement 2A); expression was reduced to a level similar to ∆gsrN. We further tested this interaction by assessing the effect of the reverse-swapped gsrN(RS) allele on the expression of katG-lacZ. However, GsrN(RS) was unstable; total GsrN(RS) levels were ≈10-fold lower than wild-type GsrN (Figure 6—figure supplement 3A,C). To overcome GsrN(RS) instability, we inserted a plasmid with three tandem copies of gsrN(RS), 3gsrN(RS), into the vanA locus in a ∆gsrN background, which increased steady-state levels of GsrN(RS) approximately 4-fold (Figure 6—figure supplement 3A,C). katG target site or GsrN recognition loop mutations significantly reduced katG-lacZ expression (Student’s t-test, p=0.0026 and p=0.0046, respectively). Compensatory RS mutations that restored base pairing between the katG target site and the GsrN loop rescued katG-lacZ expression (Figure 6B).

To assess the physiological consequence of mutating the G-tract in the katG mRNA leader and the GsrN C-rich loop, we replaced wild-type katG on the chromosome with the katG(RS) allele in both the ∆gsrN +3 gsrN and ∆gsrN +3gsrN(RS) backgrounds, and measured survival after hydrogen peroxide exposure. Both katG(RS) and gsrN(RS) mutants had survival defects (Figure 6C and Figure 6—figure supplement 3B). Strains harboring the katG(RS) allele phenocopy ∆gsrN under peroxide stress. While katG(RS) survival is compromised, the defect is not as large as a strain missing katG completely (∆katG) (compare Figure 6C and Figure 6—figure supplement 2C). Expressing gsrN(RS) in one, three, or six tandem copies did not complement the peroxide survival defect of ΔgsrN (Figure 6—figure supplement 3B). The peroxide survival defect of the individual RS alleles is restored in the strain carrying both katG(RS) and gsrN(RS) alleles, which has restored base pairing between the GsrN 5’ loop and the katG 5’ leader (Figure 6C). We conclude that base paring between the katG leader and the GsrN loop is critical for katG expression and peroxide stress survival.

We note that the protective effect of gsrN overexpression is lost when katG is deleted. Moreover, the peroxide survival defect of ∆gsrN cells can be rescued by overexpression of katG (Figure 6—figure supplement 2C). We conclude that katG is necessary and sufficient to protect the cell from hydrogen peroxide and that GsrN modulates expression of katG.

Given differences in steady state levels of GsrN and GsrN(RS), we postulated that the capacity of GsrN to interact with its targets influences its stability in vivo. Indeed, mutation of the katG target site reduced GsrN by more than two-fold (Student’s t-test, p<0.0001). The compensatory katG(RS) allele partially restored stability to GsrN(RS) (Figure 6D). katG(RS) mutation or katG deletion did not influence gsrN transcription (Figure 6—figure supplement 2B). Thus, we attribute the differences in steady-state levels of the GsrN alleles to their ability to interact with mRNA targets via the 5’ C-rich loop.

GsrN enhances KatG expression and stabilizes katG mRNA in vivo in the presence of peroxide

To assess the relative effects of GsrN on katG transcript and protein levels in vivo, we directly measured both by dot blot and Western blot, respectively. In untreated and peroxide treated cultures, katG transcript levels trended lower in ∆gsrN and higher in gsrN⁺⁺ compared to wild type (Figure 7A). In untreated cultures, these transcript differences are not statistically significant (Student’s t-test, p=0.39) yet KatG protein tagged with the M2 epitope was reduced two-fold in ∆gsrN lysate relative to wild-type (Student’s t-test, p<0.0001) (Figure 7). Upon peroxide treatment, steady-state katG transcript levels differ significantly between ∆gsrN and gsrN⁺⁺ cultures (Student’s t-test, p<0.01) (Figure 7A). KatG-M2 protein was reduced 3-fold in ∆gsrN lysate relative to wild-type, and overexpression of gsrN increased KatG-M2 two-fold compared to wild-type (Figure 7B). Since GsrN does not influence katG transcription (Figure 6—figure supplement 1A,C), these data support a model in which GsrN enhances KatG translation in vivo.

Figure 7

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GsrN affects KatG and *katG* mRNA levels in vivo.

(A) Dot blot of total RNA of *gsrN* and *katG* mutants grown to early stationary phase (OD₆₆₀0.85–0.9; this is the growth phase we used to initiate stress assays). Samples on right were treated with 0.2 mM hydrogen peroxide for 15 min before RNA extraction. These conditions were chosen to evaluate the effects of peroxide without ablating the ∆*gsrN* cultures. Blots were hybridized with *katG* mRNA, GsrN or 5S rRNA probes. *katG* mRNA signal normalized to 5S rRNA signal is quantified (mean ±SD, n = 3, p-value estimated with Student’s t-test). (B) Immunoblot of KatG-M2 fusion in wild type, Δ*gsrN*, and *gsrN⁺⁺* strains in the presence and absence of peroxide stress probed with α-FLAG antibody. KatG migrates as two bands as previously reported (Italiani et al., 2011). Normalized KatG-M2 signal (mean ±SD, n = 4, ****p<0.0001 Student’s t-test) is presented below each lane. Arrow indicates position of 100 kDa molecular weight marker.

https://doi.org/10.7554/eLife.33684.023

Peroxide treatment results in approximately five-fold induction of katG mRNA in both wild-type and gsrN mutant strains. We attribute this to OxyR-dependent activation of katG, independent of gsrN. The corresponding induction of KatG protein is modest (1.5 to 2-fold) in wild-type and gsrN⁺⁺ strains after 15 min of peroxide treatment. Given the 15 min treatment period, this discrepancy in fold-change may reflect faster accumulation of transcript than protein and/or inefficient katG translation. In ∆gsrN cells, katG transcript is induced by peroxide yet KatG protein does not change significantly. Thus, despite OxyR-induced transcription, efficient translation of katG mRNA requires GsrN.

GsrN is a general regulator of stress adaptation

In the GsrN::PP7hp pull-down fraction, multiple RNAs in addition to katG were enriched (Figure 5C,D). This suggested that GsrN may have regulatory roles beyond mitigation of peroxide stress. To globally define genes that are directly or indirectly regulated by GsrN, we performed RNA-seq and LC-MS/MS measurements on wild-type, ΔgsrN and gsrN⁺⁺ strains (Figure 8A and Figure 8—source data 1). We identified 40 transcripts, including gsrN, with significant differences in mapped reads between the ΔgsrN and gsrN⁺⁺ samples (Figure 8—figure supplement 1A and Figure 8—source data 2). Eleven proteins had significant label free quantitation (LFQ) differences (FDR < 0.05) between gsrN⁺⁺ and ΔgsrN (Figure 8—figure supplement 1B and Figure 8—source data 3). Most genes identified as significantly regulated by transcriptomic and proteomic approaches did not overlap. This is not surprising, and would be expected for cases like katG where GsrN modulates translation, but not transcription.

Figure 8 with 2 supplements see all

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GsrN is a global regulator of stress physiology.

(A) Transcriptomic and proteomic analysis of Δ*gsrN* and *gsrN⁺⁺* strains in early stationary phase (Figure 8—source data 1). Only genes detected in both analyses are plotted. Red indicates transcripts that co-purify with GsrN-PP7hp (Figure 5C,D). (B) *katG* transcript from *∆gsrN* and *gsrN⁺⁺* cells quantified as reads per kilobase per million mapped (RPKM). Data represent mean ±SD of five independent samples. Significance was evaluated with the Wald test. (C) Label free quantification (LFQ) intensities of KatG peptides from Δ*gsrN* and *gsrN⁺⁺* cells (mean ±SD, n = 3; ****p<0.0001 Student’s t-test). (D) Hyperosmotic stress survival of wild type, Δ*gsrN*, *gsrN*⁺⁺, and Δ*katG* cells relative to untreated cells. Stress was a 5 hr treatment with 300 mM sucrose. These conditions were chosen to highlight the dynamic range between Δ*gsrN* susceptibility and *gsrN⁺⁺* protection. Data represent mean ± SD from two independent experiments (points). (E) Northern blot of total RNA from wild type, Δ*gsrN*, and *gsrN*⁺⁺ cultures with or without 150 mM sucrose stress. Blots were hybridized with GsrN and 5S rRNA probes. Normalized mean ± SD of total GsrN signal from three independent samples is quantified.

https://doi.org/10.7554/eLife.33684.024

Figure 8—source data 1 Excel file that contains the log2Fold calculated values from both LC-MS/MS and RNA-Seq analysis of ΔgsrN versus gsrN⁺⁺. Values used to calculate the fold changes from LC-MS/MS can be accessed from PRIDE: PXD008128, which contains the MaxQuant (Cox et al., 2014) LFQ protein group estimations under the name ‘MQrun_delta.txt’ and ‘MQrun_plus.txt’ representing the values for ΔgsrN versus gsrN⁺⁺, respectively. Calculation of averages is outlined in Materials and methods- LC-MS/MS processing of total soluble protein. Averages were then divided and log-transformed. Values used to estimate the fold changes from RNA-Seq were taken from the CLC workbench analysis of the GEO accession number GSE106168 files, see Materials and methods- RNA-seq processing of total RNA.: https://doi.org/10.7554/eLife.33684.027
Download elife-33684-fig8-data1-v2.xlsx
Figure 8—source data 2 Excel file that contains the compiled information from the CLC workbench analysis.: https://doi.org/10.7554/eLife.33684.028
Download elife-33684-fig8-data2-v2.xlsx
Figure 8—source data 3 Excel file that contains the multiple t-test analysis outlined in Materials and methods- LC-MS/MS processing of total soluble protein.: https://doi.org/10.7554/eLife.33684.029
Download elife-33684-fig8-data3-v2.xlsx

We note there is little overlap between the transcripts enriched in the GsrN(37)-PP7hp pull-down fraction, and proteins identified as significantly regulated in our global proteomic measurements. This may be partially due to limited coverage: our protein mass spectrometry data only captured 30% of the annotated proteome. Nonetheless, these data provide evidence that GsrN can function as both a positive and negative regulator of gene expression, either directly or indirectly.

Importantly, RNA-seq and proteomics experiments validated katG as a regulatory target of GsrN. katG transcript levels measured by RNA-seq were not significantly different between ∆gsrN and gsrN⁺⁺ strains (Figure 8B), consistent with our dot blot measurements of unstressed cultures (Figure 7A). Conversely, steady-state KatG protein levels estimated from our LC-MS/MS experiments were significantly reduced in ΔgsrN, consistent with our western blot analysis of KatG protein (Figures 8C and 7B). katG was the only gene that was significantly enriched in the pull-down and differentially expressed in the proteomic studies (Figure 8A). These results provide additional evidence that katG transcript is a major target of GsrN, and that GsrN functions to enhance KatG expression at the post-transcriptional level.

Given our transcriptomic and proteomic datasets, we reasoned that GsrN may contribute to other phenotypes associated with deletion of the GSR sigma factor, sigT. Indeed, the ∆gsrN mutant exhibits a survival defect after exposure to hyperosmotic stress, similar to ΔsigT, while gsrN overexpression protects cells under this condition (Figure 8D). Hyperosmotic stress survival does not require katG (Figure 8D), providing evidence that a distinct GsrN regulatory target mediates this response. Unlike hydrogen peroxide (Figure 7A), hyperosmotic stress induces GsrN expression (Figure 8E). This is consistent with previous transcriptomic studies in Caulobacter in which hyperosmotic stress, but not peroxide stress, activated GSR transcription (Alvarez-Martinez et al., 2007). GsrN transcription is also significantly enhanced in stationary phase cultures relative to logarithmic phase cultures (Figure 1—figure supplement 2E). Although its functional role under this condition remains undefined, it has been reported that katG is a genetic determinant of stationary phase survival (Steinman et al., 1997).

σ^EcfG-regulated sRNAs are prevalent across the alphaproteobacterial clade

The GSR system is broadly conserved in Alphaproteobacteria. Given the importance of GsrN as a post-transcriptional regulator of the Caulobacter GSR, we reasoned that functionally-related sRNAs might be a conserved feature of the GSR in this clade. To identify potential orthologs of gsrN, we surveyed the genomes of Alphaproteobacteria that encoded regulatory components of the GSR system (phyR, ecfG/sigT and nepR homologs) and for which transcriptomic data were publically available.

We initially searched for GsrN-related sequences using BLASTn (Altschul et al., 1990). Hits to GsrN were limited to the Caulobacteraceae family, including the genera Caulobacter, Brevundimonas, and Phenylobacterium. The 5’ C-rich loop of homologs identified in this family had the highest level of conservation compared to other regions of secondary structure (Figure 9B). Predicted gsrN homologs are typically proximal to the genes encoding the core GSR regulators (ecfG/sigT, nepR and phyR) (Figure 9A). C. crescentus is a notable exception where gsrN is positioned distal to the GSR locus. Therefore, we used genome position as a key parameter to identify additional GsrN or GsrN-like RNAs in Alphaproteobacteria outside of Caulobacteraceae.

Figure 9

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Conserved features of GsrN homologues.

(A) Locus diagrams showing predicted *gsrN* homologs in several Alphaproteobacteria. Tree was constructed from the 16 s rRNA sequences of each strain where *Erythrobacter litoralis* (for which there is no apparent *gsrN*-like gene) was the out-group. Red arrows represent *ecfG*, dark gray arrows represent *nepR*, light gray arrows represent *phyR*, and dark blue arrows represent *gsrN* (or its putative homologs). Red boxes represent the conserved σ^ecfG-binding site. The prediction of GsrN orthologs in the Caulobacteraceae (*Caulobacter*, *Brevundimonas*, and *Phenylobacterium)* was based on a BLASTn search (Altschul et al., 1990). The prediction of GsrN in *Rhizobium etli*, *Sinorhizobium meliloti*, and *Brucella abortus* was based on evidence of GSR-dependent expression in published transcriptome data, proximity to the GSR locus, and identification of a σ^ecfG-binding site upstream of the gene. The prediction of *Agrobacterium radiobacter* was based on a BLASTn search of using the predicted GsrN sequence from *R. etli* as the query (Altschul et al., 1990). The prediction of *Rhodopseudomonas palustris* and *Bradyrhizobium diazoefficiens* is solely based on the proximity to the GSR locus and the presence of an upstream σ^ecfG-binding site. (B) Diagram of predicted secondary structure of GsrN in other Caulobacteraceae is colored by secondary structure element. Colors highlighted in the sequence alignment correspond to the predicted secondary structure regions in the cartoon. Density of shading corresponds to conservation at that position. (C) Diagram of predicted secondary structure of predicted GsrN homologs in select Rhizobiaceae where the 5’ portion contains an unpaired 5’ G-rich loop (cyan) flanked by a small hairpin (green) and a stem loop involving the 5’ terminus (red).

https://doi.org/10.7554/eLife.33684.030

Our search for GsrN focused on three parameters: evidence of intergenic transcription, identification of a near-consensus σ^EcfG-binding site in the promoter region, and proximity to the sigT-phyR chromosomal locus. Based on our criteria, we identified a set of putative GsrN homologs in the Rhizobiaceae family (Jans et al., 2013; Kim et al., 2014; Valverde et al., 2008) (Figure 9A). The predicted secondary structure of these putative GsrN homologues has features similar to GsrN from Caulobacteraceae. Specifically, there is an exposed cytosine-rich loop at the 5’ end (Figure 9C). This analysis predicts that GsrN-related small RNAs have a functional role in regulating the general stress response in related Alphaproteobacteria.

Discussion

We sought to understand how GSR transcription determines cell survival across a spectrum of chemical and physical conditions. To this end, we developed a directed gene network analysis approach to predict genes with significant functional roles in the Caulobacter GSR. Our approach led to the discovery of gsrN, a small RNA of previously unknown function that is a major post-transcriptional regulator of stress physiology.

Role of GsrN in mitigating hydrogen peroxide stress

Hydrogen peroxide can arise naturally from the environment and is also produced as an aerobic metabolic byproduct (Imlay, 2013). Our data provide evidence that σ^T-dependent transcription of GsrN basally protects cells from hydrogen peroxide by enhancing KatG expression. Unlike the transcription factor OxyR, which induces katG expression in response to peroxide (Italiani et al., 2011), GsrN is not induced by peroxide treatment. KatG levels change by only a factor of two when gsrN is deleted or overexpressed, but we observe large peroxide susceptibility and protection phenotypes as a function of gsrN deletion and overexpression, respectively. The survival phenotypes associated with subtle fold changes in KatG expression suggest that the capacity of KatG to detoxify endogenous sources of H₂O₂ is at or near its limit under normal cultivation conditions, similar to what has been postulated for E. coli (Imlay, 2013).

Expression of the ferritin-like protein, Dps, is controlled by σ^T and is reported to aid in the survival of Caulobacter under peroxide stress (de Castro Ferreira et al., 2016). The protective effect of Dps is apparently minimal under our conditions given that a) the peroxide survival defect of ∆sigT is rescued by simply restoring gsrN transcription (Figure 2B,C), and b) survival after peroxide exposure is determined almost entirely by modifying base-pairing interactions between GsrN and katG mRNA. This stated, the difference in hydrogen peroxide susceptibility between ∆sigT and ∆gsrN (Figure 1E) may be explained in part by the fact that dps is still expressed in ∆gsrN cells.

Post-transcriptional gene regulation by GsrN is a central feature of the general stress response

Our data define Caulobacter GsrN as a central regulator of stress physiology that 1) is transcribed by the general stress response (GSR) sigma factor (σ^T) and 2) has a major protective effect across distinct conditions. Multiple sRNA regulators of bacterial GSR systems have been described. In the case of E. coli, rpoS translation is controlled by sRNAs, DsrA, RprA, and ArcZ, which are necessary for survival under acid stress (Bak et al., 2014; Pernitzsch et al., 2014). In the case of GsrN, we find no evidence for feedback on σ^T expression and activity, although nepR-sigT mRNA did co-elute with GsrN in a pull-down (Table 1 and Figure 1—figure supplement 2D). The regulatory effects we observe are, apparently, purely post-transcriptional and downstream of σ^T. In this way, GsrN functions like a number of other sRNA regulated downstream of σ^S and σ^B of Gammaproteobacteria and Firmicutes, respectively (Fröhlich et al., 2016; Fröhlich and Vogel, 2009; Mäder et al., 2016; Mellin and Cossart, 2012; Opdyke et al., 2004; Romilly et al., 2014; Silva et al., 2013). GsrN protects Caulobacter from hyperosmotic and peroxide stress conditions via genetically distinct post-transcriptional mechanisms (Figures 1 and 8). We conclude that transcriptional activation of GsrN by σ^T initiates a downstream post-transcriptional program that directly affects multiple genes required for stress mitigation (Figure 10).

Figure 10

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Regulatory architecture of the *Caulobacter* stress response systems.

Expression of the GSR EcfG-sigma factor, *sigT* (σ^T), and select genes in the GSR regulon is regulated as a function cell cycle phase. σ^T-dependent transcription can be induced by certain signals (e.g. hyperosmotic stress), but is unaffected by hydrogen peroxide. Transcription of the sRNA, GsrN, is activated by σ^T, and the cell cycle expression profile of *gsrN* is highly correlated with *sigT* and its upstream regulators. Transcription of the catalase/peroxidase *katG* is independent of σ^T. GsrN dependent activation of KatG protein expression is sufficient to rescue the peroxide survival defect of a ∆*sigT* null strain. GsrN convenes a post-transcriptional layer of gene regulation that confers resistance to peroxide and hyperosmotic stresses.

https://doi.org/10.7554/eLife.33684.031

Quantitative proteomic studies (Figure 8A) demonstrate that GsrN activates and represses protein expression, either directly or indirectly. In the case of KatG, we have shown that GsrN is among the rare class of sRNAs that directly enhance protein expression (Papenfort and Vanderpool, 2015) (Figures 7B and 8C). Our global and directed measurements of mRNA show that katG mRNA levels do not change significantly between ∆gsrN and gsrN⁺⁺ strains (Figures 7 and 8). However, in the presence of peroxide, we observe significant changes in katG mRNA that correlate with changes in KatG protein levels. Our data suggest a role for GsrN as a regulator of katG translation and, perhaps, katG mRNA stability. In this way, GsrN may be similar to the sRNAs, DsrA and RyhB (Lease and Belfort, 2000; Prévost et al., 2007), which function by uncovering ribosome-binding sites (RBS) in the leaders of their respective mRNA targets. Although this mechanism may occur for GsrN-katG, we are not able to predict a clear RBS in the katG mRNA leader, which is among the 75% of open-reading frames (ORFs) in Caulobacter that do not contain a canonical RBS (Schrader et al., 2014). GsrN binding to the 5’ end of katG mRNA could recruit the ribosome to a potential stand-by binding site. Alternatively, it could induce a structural change that enhances ribosome binding, which is a mechanism proposed for the sRNA, RepG (Pernitzsch et al., 2014). Steady state levels of katG mRNA are influenced by GsrN, which may be a result of sRNA-mRNA binding (Fröhlich et al., 2013; Ramirez-Peña et al., 2010) or GsrN-dependent mRNA processing (Obana et al., 2010).

GsrN is a remarkable sRNA that has at least two distinct physiological roles in the cell, mitigating peroxide stress and hyperosmotic stress. The target of GsrN that confers hyperosmotic stress protection remains undefined, but this phenotype is also likely regulated at the post-transcriptional level (Figure 8D). While the reported physiological effects of sRNAs are often subtle, GsrN provides a notable example of a single post-transcriptional regulator that exerts a strong influence on multiple, distinct pathways affecting cellular stress survival.

On GsrN stability and processing

The roles of sRNAs in stress adaptation have been investigated in many species, and a number of molecular mechanisms underlying sRNA-dependent gene regulation have been described. We have uncovered a connection between mRNA target site recognition and GsrN stability that presents challenges in the characterization of GsrN regulatory mechanisms. Specifically, mutations in the katG mRNA leader affect steady-state levels of GsrN (Figure 6D). Given this result, one could envision scenarios in which changes in transcription of katG or some other direct GsrN target could broadly affect stress susceptibility by altering levels of GsrN and, in turn, the stability of other target mRNAs in the cell. In short, the concentrations of mRNA targets could affect each other via GsrN. Such effects should be considered when assessing mRNA target site mutations in this system and others.

GsrN is among a group of sRNAs that are post-transcriptionally processed (Chao et al., 2017; Chao et al., 2012; Mandin and Gottesman, 2010; Papenfort et al., 2015; Papenfort et al., 2009) (Figure 3). Other examples of processed sRNAs include the rpoS regulator, RprA, for which multiple isoforms have been observed. Unlike GsrN, rprA yields a stable 3’ isoform that arises from endonucleolytic-cleavage (Papenfort et al., 2009). The 3’ isoform of RprA does not apparently influence rpoS expression, but regulates a subset of mRNAs via a second identified base-pairing region (Papenfort et al., 2015). Like RprA, the distinct GsrN isoforms may have different target preferences.

Select PP7hp insertions resulted in reduced 5’ isoform formation. PP7hp mutants with low 5’ isoform levels did not complement the peroxide viability defect of ∆gsrN. Processing to a short 5’ isoform may thus be necessary for GsrN to bind katG mRNA and regulate KatG expression. Alternatively, processing may not be required for function, and lack of complementation by certain hairpin insertion mutants could be due to PP7hp interfering with target recognition or simply reducing total levels of GsrN. Regardless, our data clearly show that GsrN is cleaved to yield a 5’ isoform that is stable in the cell (Figure 3—figure supplement 1) and is sufficient to protect Caulobacter from hydrogen peroxide treatment (Figure 4B). The role of RNA metabolism in sRNA-dependent gene regulation is not well understood. GsrN will likely provide a good model to investigate mechanisms by which mRNA target levels and sRNA/mRNA processing control gene expression.

Caulobacter GSR and the cell cycle

The transcription of sigT, gsrN, and several other genes in the GSR regulon are cell cycle regulated (Fang et al., 2013; Laub et al., 2000; McGrath et al., 2007; Zhou et al., 2015), with highest expression during the swarmer-to-stalked cell transition, when cells initiate DNA replication and growth (Figure 1C). GSR activation during this period potentially protects cells from endogenous stressors that arise from upregulation of anabolic systems required for growth and replication. In the future, it is of interest to explore the hypothesis that the GSR system and GsrN provide both basal protection against endogenous stressors generated as a function of normal metabolism and induced protection against particular stressors (e.g. hyperosmotic stress) encountered in the external environment.

Materials and methods

Key resources table

Reagent type	Designation	Source or reference	Identifier
Antibody	Goat anti-Mouse IgG (H + L) Secondary Antibody, HRP	ThermoFisher	32430
Antibody	DYKDDDDK Tag Monoclonal Antibody (FG4R)	ThermoFisher	MA1-91878-1MG
Strain, strain background	See Supplementary file 1
Chemical compound, drug	Agar	Lab Scientific	A466
Chemical compound, drug	30% Hydrogen Peroxide	ThermoFisher	H325-100
Chemical compound, drug	substrate o-nitrophenyl-β-D-galactopyranoside (ONPG)	GoldBio	N-275–100
Chemical compound, drug	acrylamide:bisacrylamide (29:1)	BioRad	1610156
Chemical compound, drug	Acid-Phenol	Ambion	Am9722
Commercial assay or kit	TRIzol	ThermoFisher	15596026
Recombinant DNA reagent	T4 Polynucleotide Kinase	New England Biolabs	M0201L
Chemical compound, drug	ATP, [γ−32P]- 3000 Ci/mmol 10mCi/ml EasyTide	PerkinElmer	BLU502A500UC
Peptide, recombinant protein	SuperScript IV Reverse Transcriptase	ThermoFisher	18090010
Peptide, recombinant protein	RNase H	New England Biolabs	M0297S
peptide, recombinant protein	TURBO DNase	ThermoFisher	AM2238
Recombinant DNA reagent	KOD Hot Start DNA Polymerase	sigmaaldrich	71086
Commercial assay or kit	Micro Bio-Spin Columns With Bio-Gel P-6 in Tris Buffer	BioRad	7326221
Commercial assay or kit	Amylose Resin	New England Biolabs	E8021L
Commercial assay or kit	RNeasy Mini Kit	Qiagen	74106
Commercial assay or kit	SuperSignal West Femto Maximum Sensitivity Substrate	ThermoFisher	34095
Commercial assay or kit	Zero Blunt TOPO PCR Cloning Kit	invitrogen	K2800-20SC
Commercial assay or kit	FirstChoice RLM-RACE Kit	ThermoFisher	AM1700
Other	Raw and analyzed RNA-seq data	This paper	GEO: GSE106168 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE106168
Other	Raw and analyzed LC-MS/MS data	This paper	PRIDE: PXD008128
Other	Raw and analyzed RNA-seq data for GsrN-PP7hp purification	This paper	GEO: GSE106171 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE106171
Other	Raw and analyzed RNA-seq data for Network construction	(Fang et al., 2013) PMC3829707	GEO: GSE46915
Commercial assay or kit	Zeta-Probe Blotting Membranes	BioRad	162–0165
Commercial assay or kit	Low Molecular Weight Marker, 10–100 nt	Alfa Aesar	J76410
Commercial assay or kit	Mini-PROTEAN TGX Precast Gel, 4–20%	BioRad	456–1094
Commercial assay or kit	Precision Plus Protein Kaleidoscope Prestained Protein Standards	BioRad	1610375
Sequence-based reagent	See Supplementary file 1
Sequence-based reagent	See Supplementary file 1
Software, algorithm	Bowtie2	(Langmead and Salzberg, 2012) PMC3322381	http://bowtie-bio.sourceforge.net/bowtie2/index.shtml
Software, algorithm	SAMTools	(Li et al., 2009) PMC2723002	http://samtools.sourceforge.net/
Software, algorithm	IntaRNA 2.0.2	(Mann et al., 2017) 10.1093/nar/gkx279	http://rna.informatik.uni-freiburg.de/IntaRNA/Input.jsp
Software, algorithm	Prism v6.04	GraphPad Software, Inc.	https://www.graphpad.com/scientific-software/prism/
Software, algorithm	WebLogo	(Crooks et al., 2004), PMC419797	http://weblogo.berkeley.edu/logo.cgi
Software, algorithm	Geneious 11.0.2	(Kearse et al., 2012), PMC3371832	https://www.geneious.com/
Software, algorithm	R v 3.3.3		https://www.r-project.org/
Software, algorithm	Python v2.7		https://www.python.org/download/releases/2.7/
Software, algorithm	Rockhopper 2.0	(Tjaden, 2015), PMC4316799	https://cs.wellesley.edu/~btjaden/Rockhopper/
Software, algorithm	Edge-pro	(Magoc et al., 2013), PMC3603529	http://ccb.jhu.edu/software/EDGE-pro/index.shtml
Software, algorithm	DESeq	(Anders and Huber, 2010), PMC3218662	http://bioconductor.org/packages/release/bioc/html/DESeq.html
Software, algorithm	CLC Genomics Workbench 10	(Qiagen)	https://www.qiagenbioinformatics.com/products/clc-genomics-workbench/
Software, algorithm	MaxQuant	(Cox et al., 2014), PMC4159666	http://www.coxdocs.org/doku.php?id=maxquant:start
Software, algorithm	IterativeRank	This paper	https://github.com/mtien/IterativeRank
Software, algorithm	Sliding_window_analysis	This paper	https://github.com/mtien/Sliding_window_analysis

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Iterative rank analysis of gene expression data identifies gsrN, a small RNA that confers resistance to hydrogen peroxide.

Figure 1—source data 1

Figure 1—source data 2

GsrN is necessary for hydrogen peroxide stress survival, and expression of GsrN is sufficient to confer peroxide protection in a sigT null background.

Full-length GsrN is endonucleolytically processed into a stable 5’ isoform.

5’ end of GsrN is necessary and sufficient for peroxide survival.

GsrN co-purifies with multiple RNAs, including catalase/peroxidase katG mRNA.

Figure 5—source data 1

Figure 5—source data 2

Figure 5—source data 3

RNAs that co-elute with GsrN-PP7hp.

GsrN base pairs with the 5’ leader of katG mRNA and enhances KatG expression.

GsrN affects KatG and katG mRNA levels in vivo.

GsrN is a global regulator of stress physiology.

Figure 8—source data 1

Figure 8—source data 2

Figure 8—source data 3

Conserved features of GsrN homologues.

Regulatory architecture of the Caulobacter stress response systems.

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Matthew Tien

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Aretha Fiebig

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Sean Crosson

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