ABSTRACT
The mechanisms by which small RNA (sRNA) regulators select and prioritize target mRNAs remain poorly understood, but serve to promote efficient responses to environmental cues and stresses. We sought to uncover mechanisms establishing regulatory hierarchy for a model sRNA, SgrS, found in enteric bacteria and produced under conditions of metabolic stress when sugar transport and metabolism are unbalanced. SgrS post-transcriptionally controls a nine-gene regulon to restore growth and homeostasis under stress conditions. An in vivo reporter system was used to quantify SgrS-dependent regulation of target genes and established that SgrS exhibits a clear preference for certain targets, and regulates those targets efficiently even at low SgrS levels. Higher SgrS concentrations are required to regulate other targets. The position of particular targets in the regulatory hierarchy is not well-correlated with the predicted thermodynamic stability of SgrS-mRNA interactions or the SgrS-mRNA binding affinity as measured in vitro. Detailed analyses of SgrS interaction with asd mRNA demonstrate that SgrS binds cooperatively to two sites and remodels asd mRNA secondary structure. SgrS binding at both sites increases the efficiency of asd mRNA regulation compared to mutants that have only a single SgrS binding site. Our results suggest that sRNA selection of target mRNAs and regulatory hierarchy are influenced by several molecular features. The sRNA-mRNA interaction, including the number and position of sRNA binding sites on the mRNA and cofactors like the RNA chaperone Hfq seem to tune the efficiency of regulation of specific mRNA targets.
IMPORTANCE To survive, bacteria must respond rapidly to stress and simultaneously maintain metabolic homeostasis. The small RNA (sRNA) SgrS mediates the response to stress arising from imbalanced sugar transport and metabolism. To coordinate the stress response, SgrS regulates genes involved in sugar uptake and metabolism. Intrinsic properties of sRNAs such as SgrS allow them to regulate extensive networks of genes. To date, sRNA regulation of targets has largely been studied in the context of “one sRNA-one target”, and little is known about coordination of multi-gene regulons and sRNA regulatory network structure. Here, we explore the molecular basis for regulatory hierarchy in sRNA regulons. Our results reveal a complex interplay of factors that influence the outcome of sRNA regulation. The number and location of sRNA binding sites on mRNA targets and the participation of an RNA chaperone dictate prioritized regulation of targets to promote an efficient response to stress.
INTRODUCTION
Bacteria live in diverse niches, often encountering rapidly changing and stressful environments. Bacterial stress responses can mitigate the negative effects of stress on cell structure and function. Usually stress responses are coordinated by molecules— either RNAs or proteins, that alter expression of a regulon comprised of multiple genes. Coordinated control of the regulon prepares the cell to survive or adapt to the stress (1, 2). Proteins control expression of target regulons by binding to DNA sequences and modulating the frequency of transcription initiation, but RNAs often modulate gene expression post-transcriptionally. A prevalent type of RNA regulator in bacteria is referred to simply as small RNA (sRNA). These sRNAs are often produced in response to a particular stress, and regulate target mRNAs through base pairing interactions that modify mRNA translation or stability (3, 4). In diverse bacteria, hundreds of sRNAs have been identified (5–7). While the majority of sRNAs have not been characterized, many studies suggest that sRNA regulatory networks are as extensive and complex as those controlled by proteins (8, 9).
A large body of work has illuminated base pairing-dependent molecular mechanisms of post-transcriptional regulation by sRNAs (10, 11). The sRNA SgrS (sugar-phosphate stress sRNA) has been an important model for discovery of both negative and positive mechanisms of target mRNA regulation. SgrS is induced in response to metabolic stress associated with disruption of glycolytic flux and intracellular accumulation of sugar phosphates (also referred to as glucose-phosphate stress) (12, 13). SgrS regulates at least 9 genes and promotes recovery from glucose-phosphate stress. SgrS-dependent repression of mRNAs encoding sugar transporters (ptsG, manXYZ) reduces uptake of sugars to prevent further sugar-phosphate accumulation (Fig. 1) (12, 14, 15). Activation of a sugar phosphatase (yigL) mRNA promotes dephosphorylation and efflux of accumulated sugars (16), and repression of other mRNAs is hypothesized to reroute metabolism to promote recovery from stress (Fig. 1) (17). Each target of SgrS is regulated by a distinct molecular mechanism. How different mechanisms of regulation yield effects of variable magnitude with respect to mRNA stability and translation is an open question.
Temporally-ordered and hierarchical patterns of gene regulation carried out by protein transcription factors have been characterized in many systems (18–21). These regulatory patterns allow cells to efficiently respond to environmental signals by prioritizing induction or repression of products needed to respond to those signals. Protein regulators establish a hierarchy of regulation based on their affinities for binding sites in the operator regions of different target genes. As the concentration of active regulator increases, genes are sequentially regulated based on binding site affinity (22). There is growing evidence that sRNAs also regulate their target genes hierarchically (23, 24). However, the mechanisms involved in establishing and maintaining prioritized regulation of sRNA targets are not known.
We hypothesize that the temporal progression of target regulation by SgrS is specifically optimized to promote efficient recovery from glucose-phosphate stress (Fig. 1). To test this hypothesis, first defined the efficiency of SgrS regulation of each target and found that SgrS indeed prioritizes regulation of some targets over others. We examined the factors that determine regulatory efficiency, including the the arrangement and strength of SgrS target binding sites and the roles of other factors like RNase E and Hfq. Detailed characterization of a specific SgrS-mRNA target interaction revealed cooperative binding of SgrS to two binding sites and a requirement for both binding sites for maximal SgrS-dependent regulation. Collectively, our results upheld the hypothesis that sRNAs regulate expression of genes in their target regulons hierarchically. Features of each sRNA-mRNA pair and molecular mechanisms of regulation precisely determine the regulatory priority for each target.
RESULTS
SgrS differentially regulates targets at the level of translation
Previous studies suggested the possibility of a hierarchy of regulatory effects carried out by the small RNA SgrS, which controls translation of a diverse set of mRNA targets (11, 12, 15, 16, 25). To study this, we used a two-plasmid system to control expression of SgrS and target translational fusions (Fig. 2A). All target transcript fragments fused to gfp contain experimentally confirmed SgrS binding sites. Regulation of target translation by SgrS was measured as described previously (24).
To quantify translational regulation by SgrS and facilitate comparisons of regulatory efficiency among targets, we analyzed the data as described by Levine, et al. (24). Activity of reporter fusions was measured by monitoring GFP fluorescence over time. By plotting the GFP fluorescence (RFU) as a function of growth (OD600) for target-gfp fusions in the absence of SgrS, we defined “basal activity” at different inducer concentrations (example in Fig. S1A). This method of quantifying translational fusion activity accounts for the fact that fluorescence levels are not directly proportional to inducer concentrations ((24) and Fig. S1A). While the absolute values for basal activity differ among different target fusions, all fusions responded to induction in a dose-dependent manner (Fig. S2A). Similar plots (RFU/OD600) were generated for each fusion induced in the presence of SgrS (examples Fig. S1B-F). We define “regulated activity” as the slope of the curve (RFU/OD600) under conditions where both the fusion and SgrS are induced (example in Fig. S1B). As levels of SgrS increase, clear patterns of repression or induction are seen for all target fusions (Figs S1B-F and S2B-F).
To define the efficiency of regulation of each target we plotted regulated activity as a function of basal activity for ptsG, manX, asdI, purR, and yigL. When there is no SgrS-mediated regulation, a line with a slope of 1 is seen for all targets (Fig. 2B-F). Slopes less than 1 indicate that the fusion is repressed by SgrS. This is true for ptsG, manX, asdI and purR reporter fusions (Figure 2B-E). Slopes greater than 1 are indicative of activation by SgrS, which is true only for yigL (Fig. 2F). Importantly, the magnitude of regulation was responsive to SgrS levels. As concentrations of SgrS inducer (aTc) increased, slopes of lines for repressed targets were correspondingly reduced (Fig. 2B-E). This was not the case for yigL, the only positively regulated target of SgrS (Fig. 2F). The magnitude of activation did not increase beyond a maximal level obtained at 20 ng/mL of inducer. While the basis of this difference is unclear, it likely reflects the inherently different molecular mechanisms of regulation: mRNA stabilization for yigL and translational repression for all other targets.
We then compared regulatory efficiency among different targets at different levels of SgrS induction. At the two lowest levels of SgrS induction (10ng/mL and 20 ng/mL aTc), only ptsG and yigL showed substantial repression and activation, respectively (Fig. 3A, B). In contrast, manX, asdI and purR fusions yielded curves whose slopes remained at ~1, indicating no regulation at these lower levels of SgrS. Our interpretation of these results is that ptsG and yigL are the high-priority or “strongest” targets of SgrS, since they are regulated preferentially when SgrS levels are low. With increasing SgrS levels (20-50 ng/ml aTc), regulation of “weaker” targets manX, asdI and purR became apparent (Fig. 3C, D, E). As SgrS levels increased, ptsG repression became more efficient up to a maximal repression at 40 ng/mL of aTc, and it remained the most strongly repressed target at all levels of SgrS. Collectively, these data suggest that SgrS targets are preferentially regulated in the following order: 1/2) ptsG and yigL, 3) manX, 4) asdI, and 5) purR (Fig. 3A-E). We hypothesize that the position of each target within the regulatory hierarchy is determined by characteristics of SgrS-target mRNA interactions and the mechanism of SgrS-dependent regulation.
Differences in in vitro binding affinity are not correlated with regulation efficiency
One of the initial steps in sRNA-mediated regulation is formation of base-pairing interactions with the target mRNA. Binding of the sRNA with its target mRNA is dependent on sequence complementarity and RNA secondary structure. We examined the characteristics of SgrS-target mRNA binding in vitro to determine whether the strength of binding is correlated with the target hierarchy observed at the level of translation.
Electrophoretic mobility shift assays (EMSAs) were performed to measuring binding of SgrS to its target mRNAs ptsG, manX, purR, yigL and asd. Binding of SgrS to ptsG had a KD of 0.11 ± 0.01 μM (Fig. 4A, B), which was lower than KDs for SgrS binding to most of the other targets (Fig. 4A-E). SgrS-manX mRNA binding had a KD of 19.7 ± 2.78 μM (Fig. 4A, C) which is weaker than the interaction with ptsG (Fig. 4B), but stronger compared to purR and yigL (Fig. 4A). Three different fragments of asd mRNA were tested, because previous work demonstrated that SgrS pairs at two distinct sites on asd mRNA (17). The first site, asdI, is adjacent to the ribosome binding site and is sufficient for modest SgrS-dependent translational repression. The second site, asdII, is in the coding sequence of asd, 60-nt downstream of the start codon. When both sites are present, i.e., on asdI-II, stronger SgrS-dependent translational repression is observed (17). Surprisingly, while asdI (containing only the upstream SgrS binding site) regulation is less efficient compared to manX (Fig. 3A-E), in vitro it binds SgrS more strongly with a KD of 0.15 ± 0.04 pM (Fig. 4A, D), which is comparable to SgrS-ptsG binding (Fig. 4A, B). SgrS interaction with asdII was very weak (Fig. 4A). We could not determine KD values for SgrS interaction with asdII, purR and yigL, due to limitations in obtaining high enough concentrations of RNA, but it is apparent that SgrS binding to these targets is much weaker compared to ptsG, manX and asdI (Fig. 4A).
Results of EMSAs with SgrS and asdI-II (containing both SgrS binding sites) revealed apparent binding cooperativity. SgrS binding to asdI-II has a KD of 0.07 ± 0.01 pM (Fig. 4E, F), even slightly lower than that of SgrS-ptsG mRNA binding. Moreover, we observed two shifted species that correspond to one or two SgrS sRNAs pairing with a single asdI-II transcript (Fig. 4E).
Structural analyses of SgrS-asd mRNA interactions
Our data thus far indicate that SgrS regulates mRNA targets in a hierarchical fashion (Figs. 2, 3). SgrS-mRNA binding affinities alone do not explain the target hierarchy, as SgrS-ptsG mRNA and SgrS-asd mRNA interactions have very similar KDs, but ptsG is much more efficiently regulated than asd at all concentrations of SgrS (Fig. 3). To further understand the features that influence the efficiency of target regulation, we performed more detailed analyses of SgrS-asd mRNA interactions.
Previous work demonstrated that SgrS binding site I encompasses nt +31 to +49 and site II from nt +110 to +127 ((17), Fig. 5A) We used IntaRNA (26, 27) to predict the free energy (ΔG) for SgrS interactions with asd mRNA segments containing both sites, or each site individually (Fig. 5B). IntaRNA accounts for the energy necessary to open double-stranded regions of RNA secondary structure, to make them accessible for pairing. We first analyzed SgrS interactions with asdI-II mRNA (encompassing nt +1 to +240), which we denote as “structured” (Fig. 5B). Interaction of SgrS with asd site I has a predicted ΔG of −10.5 kcal/mol, while SgrS pairing with site II has a ΔG of −1.1 kcal/mol (Fig. 5B, structured). The ΔG for interactions between SgrS and the isolated binding sites, are −18 kcal/mol for site I and −7.4 kcal/mol for site II (Fig. 5B, isolated). These predictions suggest that SgrS interaction with site II is less favorable, particularly in the context of the longer structured asd mRNA.
We investigated the structure of asdI-II with selective 2’-hydroxyl acylation analyzed by primer extension (SHAPE), where flexible nts are modified by N-methylisotoic anhydride (NMIA), while nts constrained in helices are not reactive. In the absence of SgrS, the sequence encompassing the asd ribosome binding site (+44 to +50) is located within a structured loop (+36 to +69) on top of a short stem (+31 to +35 pairing with +70 to +74) (Fig. 5C, Fig. S3). The nts in site I (+31 to +49, Fig. 5A) are located on the 5’ side of the stem-loop structure. Most of the nts in this structure are reactive, which is indicative of a flexible conformation that is accessible for ribosome binding or base pairing with the seed sequence of SgrS (Fig. 5C). The seed interaction of SgrS likely promotes opening of the structure. Downstream of the site I stem-loop structure is a highly structured second stem (+83 to +155) that contains site II in the apical region (+110 to +129) (Fig. 5C, Fig. S3). Site II is sequestered in a helix and would not be accessible to base pair with SgrS (Fig. 5C). In light of binding cooperativity observed in Fig. 4E, we hypothesize that SgrS pairing with site I induces rearrangement of asd mRNA secondary structure to facilitate interaction with site II.
We next used SHAPE to probe changes to the asdI-II structure in the presence of SgrS. The reactivity of site I nt +31 to +49 decreased as the concentration of SgrS increased (Fig. 5D), with the exception of nt +41 which is not predicted to base pair with SgrS (Fig. 5A). The SHAPE reactivity plateaued between 5 and 10-fold excess SgrS (Fig. S3E,F), which suggests that binding to site I was saturated. This is consistent with a strong base-paring interaction between SgrS and site I. In contrast, the reactivity of the site II nts +110 to +129 decreased more slowly and to a lesser extent (Fig. 5D), consistent with a weaker and cooperative interaction. Fewer site II nts showed changes in SHAPE reactivity upon addition of SgrS; this is likely due to the highly structured nature of site II in the absence of SgrS.
The reactivity of nts outside of the SgrS binding sites also changed in the presence of SgrS (Fig. 5E). When a mutant SgrS that is not predicted to bind to asdI-IIwas used, minimal changes in SHAPE reactivity were observed, which suggests that the changes in the presence of wild-type SgrS are due to the interactions between SgrS and asdI-II mRNA and not due to the presence of additional RNA in the system (Fig. 5E). This indicates that SgrS binding changes the overall structure of the asd RNA. A secondary structure predicted using the SHAPE data suggests that these changes are limited to opening the SgrS binding sites and extending the site II stem (Fig. 5C). It is worth noting an important caveat to these analyses. The structure prediction algorithms were not designed to account for intermolecular interactions, so this analysis may not be able to capture the in vivo relevant structure of asd mRNA in complex with SgrS. Nonetheless, SHAPE data are consistent with other analyses in demonstrating binding of SgrS to asd mRNA, prominently at site I and to a lesser extent at site II.
Optimal repression by SgrS involves both pairing sites within asd mRNA
To further investigate the role of the two SgrS pairing sites on asd, we performed stochastic optical reconstruction microscopy (STORM) coupled with single-molecule in situ hybridization (smFISH) to monitor SgrS regulation of asd-lacZ variants asdI, asdII, and asdI-II (Fig. 6A) at single molecule resolution. In these experiments bacteria were grown in the presence of L-arabinose to induce expression of chromosomal asd-lacZ variants. Glucose-phosphate stress was induced for 10 min by the addition of 1% a-methyl D-glucopyranoside (aMG). 3D super-resolution images show asd-lacZ mRNAs (Fig. 6B-D, green) and SgrS (Fig. 6B-D, red), as projected on 2D planes, with cells outlined. Numbers of asd-lacZ mRNAs and SgrS sRNAs were counted and represented as “copy number per cell” in histograms, with average copy number per cell indicated above the histogram (Fig. 6B-D). SgrS induction reduced the copy number of asdI-lacZ mRNA by 3-fold (Fig. 6B, green) when SgrS was induced to high levels after 10 min treatment with aMG (Fig. 6B, red and S4A, B). On the contrary, the copy number of asdII-lacZ mRNAs (Fig. 6C, green) was not strongly affected in the presence of high SgrS levels after aMG treatment (Fig. 6C, red and Fig. S4C, D). Copy numbers of asdI-II-lacZ mRNA (Fig. 6D, green) were reduced by ~8-fold after 10 min of αMG induction (Fig. 6D, red, Fig. S6E,F). These data demonstrate that both binding sites on asd mRNA are important for efficient SgrS-dependent regulation of mRNA stability.
We next examined the roles of the two SgrS binding sites in the efficiency of translational regulation. SgrS regulation of an asdI-II translational fusion was compared to regulation of an asdI fusion (Fig. 7A). By plotting regulated activity as a function of basal activity as described above, we determined that SgrS repression of asdI-II was more efficient than repression of asdI across the entire range of SgrS expression levels (Fig. 7B), a result in line with our previous study (17). Comparison to other targets indicated that asdI-II is regulated more efficiently than manX, asdI and purR, at all concentrations of SgrS (Fig. 7C).
We then compared SgrS regulation of asdI and asdI-II in the rne701 mutant strain deficient in degradosome assembly (28). We determined basal activity (Fig. S5A) and regulated activity (Fig. S5B-F) of asdI and asdI-II translational gfp fusions at different levels of SgrS induction. Reminiscent of our data in the wild-type strain (Fig. 7A), SgrS regulated asdI-II more efficiently compared to asdI in the rne701 mutant (Fig. 7D. Moreover, when compared to SgrS regulation of other targets, asdI-II was repressed most efficiently (Fig. 7E) in the rne701 mutant. Taken together the data indicate that the second binding site on asd mRNA enhances the stringency of SgrS-mediated regulation. Moreover, addition of the second binding site on asd changes its regulatory priority relative to other targets in the SgrS regulon.
Transcriptional regulation of asd by SgrS
We observed that the asdI-II transcript is more abundant than asdI (Fig. 6B, D). Since the constructs used in that experiment were expressed from a heterologous promoter, we postulate that increased levels of asdI-II mRNA compared to asdI mRNA must be due to increased mRNA stability or transcription elongation of the asdI-II construct compared to asdI. We constructed asdIand asdI-II transcriptional fusions to lacZ expressed from an inducible promoter (Fig. 8A) to test whether SgrS can regulate asd transcripts independent of translational regulation. Consistent with observations from smFISH, the asdI-II-lacZ transcriptional fusion had substantially higher activity compared to asdI-lacZ (Fig. 8B). While SgrS regulated both reporter fusions, asdI-II repression was more efficient (3.3-fold repression) than asdI (2.1-fold repression) (Fig. 8B). SgrS still regulated both fusions in the rne701 mutant strain (Fig. 8B). Importantly, SgrS-dependent degradation of other SgrS targets ptsG mRNA (29), and manXYZ mRNA (15, 25) was abolished in degradosome mutants. Together with our previous work, these observations suggest that SgrS regulates asd by two independent mechanisms: translational repression by pairing at site I (directly occluding the ribosome binding site) and reducing mRNA levels by promoting mRNA turnover and/or inhibiting transcription elongation.
DISCUSSION
In this study, we set out to define the hierarchy of regulation by a model bacterial sRNA. SgrS is a good model for this study because it has a modestly-sized regulon, and the mechanisms of regulation of several targets have been characterized in detail (16, 17, 25, 30). Our results demonstrate a clear pattern of prioritized regulation of mRNA targets (Fig. 2B-F, Fig. 3A-E). Two targets in particular, ptsG and yigL, were “high-priority” targets that were efficiently regulated even at low levels of SgrS production. Other targets, manX, purR, and asd, were less impacted by SgrS and were only regulated when SgrS was produced at higher levels.
We investigated features of sRNA-mRNA interactions that could impact the overall efficiency of SgrS-mediated regulation. In vitro SgrS-mRNA interactions as measured by EMSA defined KDs that were not well-correlated with in vivo regulatory efficiency (Fig. 4A-F, Fig. 3A-E). Two targets stood out in the comparison of in vivo regulation and in vitro SgrS-mRNA interactions. The yigL mRNA interaction with SgrS was barely detectable in vitro (Fig. 4A), but in vivo, yigL translation was maximally activated at low SgrS levels (Fig. 2F). Conversely, the translation of asdI was modestly regulated by SgrS in vivo (Fig. 2E), but the in vitro SgrS-asdI interaction was comparable to that of SgrS-ptsG, the strongest in vivo regulatory effect. These apparent contradictions between in vitro interactions and in vivo regulatory efficiency led us to further explore SgrS regulation of asd.
Previous work demonstrated that SgrS has two binding sites on asd mRNA: site I overlaps that asd ribosome binding site and site II is ~60 nt downstream in the asd coding sequence ((17) and Fig. 5A). EMSAs demonstrated SgrS pairing at site I alone, but pairing at site II alone was undetectable. Binding of SgrS to an asd mRNA containing both sites I and II was cooperative (Fig. 4E,F). Structural analyses of asd mRNA in the absence and presence of SgrS demonstrated that SgrS indeed pairs preferentially at site I over site II and induces substantial structural rearrangement in the mRNA (Fig. 5C-E, Fig. S3). Quantification of SgrS-dependent degradation of asd mRNA showed that site I is important, but sites I and II together promote the most efficient regulation (Fig. 6B-D, Fig. S4). Similar to binding and regulation of mRNA degradation, SgrS interactions at both sites I and II on asd mRNA improve the efficiency of translational regulation (Fig. 7B,C, Fig. S2). These results suggest that the number and position of sRNA binding sites on mRNA targets control regulation in vivo in ways that could not be predicted based on in vitro characterization of sRNA-mRNA binding.
In many cases, sRNA-mediated regulation of translation is thought to indirectly affect mRNA stability by making untranslated mRNA more susceptible to degradation by RNase E. There are also examples of sRNA regulation, including SgrS regulation of yigL (16), where modulation of mRNA stability is translation-independent. Truncation of RNase E (encoded by rne), removing the C-terminal scaffold for degradosome assembly, often prevents sRNA-mediated degradation of mRNA targets (15, 31, 32). If translational regulation is the primary function of an sRNA on a given mRNA target, the regulation should be preserved in rne mutant backgrounds. For SgrS targets, the regulatory hierarchy is mostly preserved in an rne701 degradosome mutant background (Fig. 7, compare C and E), suggesting that for most targets, regulation of RNA stability is not the primary mechanism of control by SgrS. Interestingly, the high-priority target ptsG was a notable exception. In the wild-type background, ptsG is the most efficiently-regulated target (Fig. 7C), whereas in the rne701 host, it is weakly regulated. This defect could be overcome by increasing SgrS expression levels (Fig. S6B). This result suggests that RNase E-dependent degradation of ptsG mRNA is more important for its efficient regulation by SgrS compared to other targets, where efficient regulation does not depend on subsequent target degradation. This is consistent with the fact that ptsGmRNA levels decrease at least 10-fold whereas other targets exhibit a modest 2-fold decrease in mRNA levels upon SgrS expression (17). Our recent study quantifying SgrS-dependent mRNA degradation at single molecule resolution indicated that ptsGmRNA exhibits faster degradation kinetics than manXYZ mRNA (31), which could enhance the efficiency of regulation in a wild-type but not rne701 mutant background where translational regulation and mRNA degradation are uncoupled.
One of our ultimate goals is to define at a molecular level the mechanisms by which sRNAs select and prioritize regulation of their targets. The current study implicates features of sRNA-mRNA interactions such as number and strength of sRNA binding sites on each mRNA target and accessory factors such as RNase E in dictating regulatory hierarchy. Another factor that is likely to play an important role in setting regulatory priority is the RNA chaperone Hfq. EMSAs demonstrated Hfq binding to ptsG, manX, purR, yigL, asdI, asdII and asdI-II mRNAs (Fig. S7A) with similar KD values for all targets (Fig. S7B). Previous work has shown that sRNAs compete for binding to Hfq, and this competition affects their regulatory ability (33, 34). Different sRNAs can bind to distinct sites on Hfq and this impacts their regulation of particular targets (34, 35). Additional work will be required to determine what role Hfq plays in establishing the hierarchy of regulatory effects in sRNA regulons.
Most sRNA-mRNA interactions are conceived of as single binding site interactions, but we have already identified two SgrS targets that deviate from this model and have shown that additional binding sites can play important roles in regulation and change regulation efficiency (17, 25). We have not yet discovered the specific mechanism of regulation of asd mRNA by SgrS, but have shown definitively that both binding sites are required for strong regulation. SgrS-dependent control of both transcriptional and translational asd reporter fusions is not impacted in RNase E degradosome deficient strains (Fig. 7B,D, Fig. 8B), suggesting that the regulation is not strictly dependent on translation or mRNA turnover. Future work will test the hypothesis that SgrS acts on asd mRNA at the level of transcription elongation, perhaps by an attenuation mechanism.
In Vibrio, quorum sensing-regulated Qrr sRNAs regulate multiple targets by distinct mechanisms and differences in those mechanisms influence the dynamics and strength of regulation (23). Strong and rapid regulation is achieved by sRNAs acting catalytically where the sRNA pairs with and promotes mRNA degradation but is then recycled for use on another mRNA target. A sequestration mechanism, where formation of the sRNA-mRNA complex is the terminal outcome of regulation, results in slower and weaker sRNA-dependent regulation of the target mRNA. For Qrr sRNAs, these regulatory mechanisms seem to depend on which region of the sRNA is pairing with a given target and whether the sRNA-mRNA interaction is strong or weak (23). While some of the same features of SgrS-mRNA interactions may be relevant in determining regulatory efficiency, we note that the SgrS seed sequence responsible for pairing with all mRNA targets characterized thus far is encompassed by a short (~20 nt) mostly single-stranded region of SgrS (12, 15-17). Moreover, we did not see a good correlation between strong versus weak binding in vitro and in vivo regulatory efficiency. It may be true that the “rules” governing regulatory efficiency and specific outcomes are different for each individual sRNA. Work on more model sRNAs will be needed to illuminate broad general principles.
Beyond defining interesting molecular features of sRNA-mRNA interactions, defining regulatory hierarchy for sRNA regulons is important for understanding bacterial physiology. The vast majority of sRNA regulons remain undefined, and thus sRNA functions unknown. For novel sRNAs, distinguishing high-priority from weaker targets may provide crucial clues to the predominant role of the sRNA in cell physiology. For SgrS, the regulatory hierarchy we have defined here is perfectly consistent with growth studies demonstrating the primary importance of SgrS regulation of sugar transport and efflux under glucose-phosphate stress conditions (36). The hierarchy of regulation by sRNAs likely evolved to promote rapid and efficient responses to environmental signals that would provide cells with a competitive growth advantage in their specific niche. It will be critical to develop tools to more rapidly characterize sRNA regulatory hierarchy to better understand functions of the hundreds of uncharacterized sRNAs in diverse bacteria.
MATERIALS AND METHODS
Strain and plasmid construction
List of strains and plasmids used in this study are listed in Table S1. All strains used in this study are derivatives of E. coli K-12 strain MG1655. Oligonucleotide primers and 5’ biotinylated probes used in this study are listed in Table S2 and were acquired from Integrated DNA Technologies. Chromosomal alleles were moved between strains by P1 vir transduction (37) and inserted using λ Red recombination (38, 39).
Translational reporter fusion alleles PBAD-asdI-II-lacZ (MBP151F/MBP193R primers), PBAD-asdI-lacZ (MBP151F/MBP151R primers) and PBAD-asdII-IacZ (MBP193F/MBP193R primers) were constructed by PCR amplifying desired fragments using primers containing homologies to Pbad and lacZ. Similarly, transcriptional fusions PBAD-asdI-II-lacZ (MBP151F/MBP206R3 primers) and PBAD-asdI-lacZ(MBP151F/MBP206R1 primers) were generated by PCR amplification using forward primer with homology to Pbad and reverse primers containing lacZ RBS and lacZ homology. PCR products were then recombined into PM1205 strain using λ Red homologous recombination.
Plasmid harboring SgrS under the control of Ptet0-1 promoter was constructed by PCR amplifying sgrS from E. coli MG1655 chromosomal DNA using oligos containing NdeI and BamHI restriction sites. Resulting PCR product and vector pZA31R (24) were digested by NdeI and BamHI (New England Biolabs) restriction endonucleases. Digestion products were ligated using DNA Ligase (New England Biolabs) to produce pZAMB1 plasmid containing Ptet0-1-sgrS allele.
Plasmid pZEMB8 containing Plac0-1-ptsG-gfp was constructed by PCR amplifying ptsG from MG1655 chromosomal DNA using oligos containing KpnI and EcoRI restriction sites. Resulting PCR products and vector pZE12S (24) were digested by KpnI and EcoRI restriction endonucleases. Digestion products were ligated using DNA Ligase to produce pZEMB2. Superfolder gfp (from now on just gfp) was amplified from pXG10-SF (40) using oligos containing KpnI and XbaI restriction sites. pZEMB2 and the resulting PCR product were digested with KpnI and XbaI, and ligated with DNA Ligase to produce pZEMB8. Plasmids with translational reporter fusions Plac0-1-manX-gfp(pZEMB10), Plac0-1-yigL-gfp (pZEMB15), Plac0-1-purR-gfp (pZEMB25), Plac0-1-asdI-gfp (pZEMB26) and Plac0-1-asdI-II-gfp (pZEMB27) were constructed by restriction cloning into pZEMB8 using KpnI and EcoRI restriction endonucleases.
Media and reagents
Bacteria were cultured in Luria-Bertani (LB) broth medium or LB agar plates (37) at 37°C, unless stated otherwise. Bacteria were grown in MOPS (morpholine-propanesulfonic acid) rich defined medium (Teknova) with 0.2% fructose for reporter fluorescence assays. Where necessary, media was supplemented with antibiotics at following concentrations: 100 μg/ml ampicillin (Amp), 25 μg/ml chloramphenicol (Cm), 25 μg/ml kanamycin (Kan) and 50 μg/ml spectinomycin (Spec). Isopropyl (β-D-1-thiogalactopyranoside (IPTG) was used at concentrations of 0.1-1.5 mM for induction of Plac0-1 promoter, anhydrotetracycline (aTc) 0-50 ng/ml for induction of Ptet0-1 promoter and 0.000002%-0.2% L-arabinose for induction of PBAD promoter, unless otherwise noted. To induce glucose-phosphate stress, 0.5% a-methylglucoside (aMG) was added to the growth medium.
Reporter fluorescence assay
Bacterial strains were cultured overnight in MOPS rich medium supplemented with 0.2% fructose, Amp, Cm and subcultured 1:100 to fresh medium with appropriate inducers (IPTG, aTc) in 48 well plates. Relative fluorescence units (RFU) and optical density (OD600) were measured over time. “GFP expression” was calculated by plotting RFU over OD600 and determining the slopes of linear regression plots for each IPTG concentration in exponentially growing cells in the presence of aTc to induce SgrS expression. “Promoter activity” was calculated by plotting RFU over OD600 and determining the slopes of linear regression plots for each IPTG concentration in exponentially growing cells in the absence of aTc.
In vitro transcription and radiolabeling
Template DNA for in vitro transcription was generated by PCR using gene-specific oligonucleotides containing the T7 promoter sequence. Following oligonucleotides were used to generate specific template DNA: MBP84F/MBP213R–ptsG (+1 to +240), O-JH218/MBP214R-manX (+1 to +240), MBP56F/MBP215R–asdI-II (+1 to +240), MBP56F/MBP222R–asdI (+1 to +110), MBP226F/MBP226R–asdII (+71 to +310), MBP65F/MBP174R–purR (+1 to +230), MBP216F/MBP216R–yigL (−191 to +50 relative to ATG translation start of yigL) MBP234F/MBP234R–gfp (+1 to +240) and O-JH219/O-JH119 were used to generate full-length sgrS template DNA. In vitro transcription of DNA templates was performed according to specifications of MEGAscript T7 Kit (Ambion). In vitro transcribed RNA was 5’-end labeled with radioisotope 32P using the KinaseMax Kit (Ambion). Samples were cleaned by passing through Illustra ProbeQuant G-50 Micro Columns (GE Healthcare). Than samples were cleaned once more with phenol-chloroform: isoamyl alcohol (Ambion) and labeled RNA precipitated with Ethanol:3M NaAc (30:1).
RNA-RNA gel electrophoretic mobility shift assay
Different concentrations of unlabeled mRNA were mixed with 0.02 pmol of 5’-end labeled SgrS. Samples were denatured at 95°C for 1 min, placed on ice for 5 min, and incubated at 37°C for 30 min in 1x binding buffer (20mM Tris-HCL (pH 8.0), 1mM DTT, 1mM MgCl2, 20 mM KCl, 10mM Na2HPO4 (pH 8.0)) (41). Non-denaturing loading buffer was added and samples resolved for 6 h at 40 V on native 5.6% PAGE.
Protein-RNA gel electrophoretic mobility shift assay
0.02 pmol of 5’-end labeled mRNA was denatured at 95°C for 1 min., placed on ice for 5 min. Different concentrations of purified Hfq protein (His-tagged) were added. Samples were incubated at 37°C for 30 min in 1x binding buffer (20mM Tris-HCL (pH 8.0), 1mM DTT, 1mM MgCl2, 20 mM KCl, 10mM Na2HPO4 (pH 8.0)). Non-denaturing loading buffer was added and samples resolved for 1h 30 min at 20 mA on native 4.0% PAGE (41).
SHAPE
The asdI-II RNA (0.15 μM) and SgrS RNA (0.075 μM, 0.15 μM, 0.30 μM, 0.75 μM, 1.5 μM, or 3.0 μM) were folded separately as in (42) using a modified SHAPE buffer (100 mM HEPES [pH 8.0], 2 mM MgCl2, 40 mM NaCl). For each SgrS concentration, the SgrS RNA or the equivalent volume of 0.5X TE was added to the asdI-II RNA and the samples were incubated at 37°C for 30 min. The RNAs were modified with N-methylisatoic anhydride (NMIA, 6.5 mM; Sigma-Aldrich) and collected by ethanol precipitation as in (42). Parallel primer extension inhibition and sequencing reactions were performed using fluorescently labeled primers complementary to the 3’ end of the asdI-II RNA (5’-AGATCAAAGGCATCCTGAAG, 22.5 nM; Applied Biosystems, ThermoFisher Scientific) as in (43) with minor modifications. Prior to primer binding the RNAs were denatured and snap cooled and the reactions were carried out for 20 min at 52°C, followed by 5 min at 65°C. The cDNAs were analyzed on a 3730 DNA Analyzer (Applied Biosystems, Inc.). The data were processed and SHAPE reactivity (difference between the frequency of primer extension products at each nucleotide in +NMIA vs. −NMIA samples) was derived using the QuShape software (44). Data for each nucleotide were averaged with statistical outliers removed and normalized using the 2-8% rule (45). Relative reactivity was calculated by subtracting normalized SHAPE reactivity in the absence of the SgrS RNA from reactivity in the presence of the WT or MT SgrS RNA.
Single-molecule fluorescence in situ hybridization (smFISH)
The asdI-lacZ(MB170), asdII-IacZ (MB183) and asdI-II-lacZ (MB171) strains were grown overnight at 37 °C, 250 rpm in LB Broth Miller (EMD) with 25 μg/ml kanamycin (Kan) and 50 μg/ml spectinomycin (Spec). The next day, the overnight culture was diluted 100-fold into MOPS EZ rich defined medium (Teknova) with 0.2%(w/w) sodium succinate, 0.02% glycerol and 0.01% L-arabinose, for asdI-lacZ and asdII-IacZ strains, and was allowed to grow at 37 °C till the OD600 reached 0.15-0.25. The concentration of L-arabinose used for asdI-II-lacZ was 0.002%. α-methyl D-glucopyranoside (αMG) (Sigma-Aldrich) was added to the culture to a desired concentration to introduce sugar phosphate stress and induce SgrS sRNA expression. After 10 minutes of induction, the culture was taken out and fixation was performed by mixing with formaldehyde (Fisher Scientific) at a final concentration of 4%.
ΔsgrS and ΔlacZ strains were grown in LB Broth Miller (EMD) at 37 °C, 250 rpm overnight. Then the cultures were diluted 100-fold into MOPS EZ rich defined medium (Teknova) with 0.2% glucose and allowed to grow at 37 °C till the OD600 reached 0.2. The cells were then fixed by mixing with formaldehyde (Fisher Scientific) at a final concentration of 4%. TK310 cells were grown overnight, similar to the knockout strains. The overnight culture was then diluted 100-fold into MOPS EZ rich defined medium (Teknova) with 0.2% glucose and 1 mM isopropyl β-D-1-thiogalactopyranoside (IPTG, Sigma-Aldrich) and allowed to grow at 37 °C for 30 minutes. The cells were then fixed in the same procedure as mentioned before.
The fixation and permeabilization of the cells were done using the methods published previously (46). After fixing with 4% formaldehyde, the cells were incubated at room temperature for 30 minutes. The cells were then centrifuged at 600 g for 7 minutes and the pellets were washed with 1X PBS 3 times. The cells were then permeabilized with 70% ethanol for 1 hour at room temperature and stored at 4 °C before fluorescence in situ hybridization.
The smFISH probes were designed using Stellaris Probe Designer and the orders were placed from Biosearch Technologies (https://www.biosearchtech.com/). The labeling of the probes was performed using equal volumes of each probe. The final volume of sodium bicarbonate was adjusted to 0.1 M by adding 1/9 reaction volume of 1 M sodium bicarbonate (pH = 8.5). The probe solution was mixed with 0.05-0.25 mg of Alexa Fluor 647 or Alexa Fluor 568 succinimidyl ester (Life Technologies) dissolved in 5 ML DMSO. The dye was kept about 20-25 fold in molar excess relative to the probes. After incubation with gentle vortexing in the dark at 37 °C overnight, the reaction was quenched by adding 1/9 reaction volume of 3 M sodium acetate (pH = 5). Unconjugated dyes were removed by ethanol precipitation first and then by P-6 Micro Bio-Spin Column (Bio-Rad).
A previously published protocol (46) was used for the hybridization procedure. 60 Ml of permeabilized cells were washed with FISH wash solution (10% formamide in 2X SSC (Saline Sodium Citrate) buffer) and resuspended in 15 μl hybridization buffer (10% dextran sulfate, 1 mg/ml E. Coli tRNA, 0.2 mg/ml BSA, 2 mM vanadyl ribonucleoside complexes, 10% formamide in 2X SSC) with probes. The number of probes used for sRNA SgrS was 9 and they were labeled with Alexa Fluor 647. The number of probes for mRNA lacZ was 24 and they were labeled with Alexa Fluor 568. The concentration of the labeled probes for SgrS and lacZ mRNA were 50 nM and 15 nM each. The reactions were incubated in the dark at 30 °C overnight. The cells were then resuspended in 20X volume FISH wash solution and centrifuged. They were then resuspended in FISH wash solution, incubated for 30 minutes at 30 °C and centrifuged and this was repeated 3 times. The cells were pelleted after the final washing step and resuspended in 20 μl 4X SSC and stored at 4 °C for imaging. The labeled cells were immobilized in poly-L-lysine (Sigma-Aldrich) treated 1.0 borosilicate chambered coverglass (Thermo Scientific™ NuncTM Lab-TekTM). They were then imaged with imaging buffer (50 mM Tris-HCl (pH = 8.0), 10% glucose, 1% β-mercaptoethanol (Sigma-Aldrich), 0.5 mg/ml glucose oxidase (Sigma-Aldrich) and 0.2% catalase (Calbiochem) in 2X SSC).
Single-molecule localization-based super-resolution imaging
An Olympus IX-71 inverted microscope with a 100X NA 1.4 SaPo oil immersion objective was used for the 3D super-resolution imaging. The lasers used for two-color imaging were Sapphire 568–100 CW CDRH, Coherent (568nm) and DL640-100-AL-O, Crystalaser (647nm) and DL405-025, Crystalaser (405nm) was used for the reactivation of Alexa 647 and Alexa 568 fluorophores. The laser excitation was controlled using mechanical shutters (LS6T2, Uniblitz). A dichroic mirror (Di01-R405/488/561/635, Semrock) was used to reflect the laser lines to the objective. The objective collected the emission signals and then they made their way through an emission filter (FF01-594/730-25, Semrock for Alexa 647 or HQ585/70M 63061, Chroma for Alexa 568) and excitation laser was cleaned up using notch filters (ZET647NF, Chroma, NF01-568/647-25x5.0 and NF01-568U-25, Semrock). They were then imaged on a 512×512 Andor EMCCD camera (DV887ECS-BV, Andor Tech). Astigmatism was introduced by placing a cylindrical lens with a focal length of 2 m (SCX-50.8-1000.0-UV-SLMF-520-820, CVI Melles Griot) in the emission path between two relay lenses with focal lengths of 100 mm and 150 mm each and this helped us to do 3D imaging. In this setup, each pixel corresponded to 100 nm. We used the CRISP (Continuous Reflective Interface Sample Placement) system (ASI) to keep the z-drift of the setup to a minimum. The image acquisition was controlled using the storm-control software written in Python by Zhuang’s group and available at GitHub.
The imaging of the sample began with a DIC image of the sample area. Subsequently two-color super-resolution imaging was performed. 647nm excitation was used first and after image acquisition was completed for Alexa Fluor 647, 568nm excitation was used to image Alexa Fluor 568. 405nm laser power was increased slowly to compensate for fluorophore bleaching and also to maintain moderate signal density. We stopped imaging when most of the fluorophores had photobleached and the highest reactivation laser power was reached.
The raw data acquired using the acquisition software was analyzed using the same method as described in previously published work (31), which was a modification of the algorithm published by Zhuang’s group (47, 48). The clustering analysis on the localization data was performed using MATLAB codes in the same method as described previously (31). Background signal was estimated using ΔsgrS and ΔlacZ strains and they were prepared, imaged and analyzed as described before. TK310 cells were prepared, imaged and analyzed in the same way as a low copy lacZ mRNA sample for copy number calculation. The copy number calculation was also performed using MATLAB codes as described previously (31).
FUNDING
National Institutes of Health R01 GM092830 (M.B. and C.K.V.), R01 GM112659 (M.B., M.S.A., T.H., J.Z., and A.P.), R35 GM122569 (T.H., J.Z., and A.P.), R01 GM047823 (T.H.), T32 GM086252 (J.F.); National Science Foundation PHY 1430124 (T.H., J.Z., and A.P); University of Illinois Department of Microbiology James R. Beck Fellowship (M. B.).
ACKNOWLEDGEMENTS
We would like to extend a special thank you to Erel Levine for providing plasmids. We are grateful to Jennifer Rice, Rich Yemm, Divya Balasubramanian, Chelsea Lloyd, Alisa King, Jessica Kelliher and other current and past members of the Vanderpool lab for strains, plasmids and valuable advice. We appreciate and thank Prof. James Slauch and members of his lab for fruitful discussions.