Molecular Microbiology (2014) 92(2), 369–382 ■

doi:10.1111/mmi.12564 First published online 19 March 2014

An evolutionarily conserved RNase-based mechanism for repression of transcriptional positive autoregulation Elisabeth J. Wurtmann,1 Alexander V. Ratushny,1,2 Min Pan,1 Karlyn D. Beer,1 John D. Aitchison1,2 and Nitin S. Baliga1* 1 Institute for Systems Biology, Seattle, WA 98109, USA. 2 Seattle Biomedical Research Institute, Seattle, WA 98109, USA.

Summary It is known that environmental context influences the degree of regulation at the transcriptional and posttranscriptional levels. However, the principles governing the differential usage and interplay of regulation at these two levels are not clear. Here, we show that the integration of transcriptional and post-transcriptional regulatory mechanisms in a characteristic network motif drives efficient environment-dependent state transitions. Through phenotypic screening, systems analysis, and rigorous experimental validation, we discovered an RNase (VNG2099C) in Halobacterium salinarum that is transcriptionally co-regulated with genes of the aerobic physiologic state but acts on transcripts of the anaerobic state. Through modelling and experimentation we show that this arrangement generates an efficient state-transition switch, within which RNase-repression of a transcriptional positive autoregulation (RPAR) loop is critical for shutting down ATP-consuming active potassium uptake to conserve energy required for salinity adaptation under aerobic, high potassium, or dark conditions. Subsequently, we discovered that many Escherichia coli operons with energy-associated functions are also putatively controlled by RPAR indicating that this network motif may have evolved independently in phylogenetically distant organisms. Thus, our data suggest that interplay of transcriptional and posttranscriptional regulation in the RPAR motif is a generalized principle for efficient environment-dependent state transitions across prokaryotes.

Accepted 19 February, 2014. *For correspondence. E-mail nitin [email protected]; Tel. (+1) 206 732 1266; Fax (+1) 206 732 1299.

© 2014 John Wiley & Sons Ltd

Introduction There is growing evidence that the relative usage of transcriptional and post-transcriptional regulation is heavily influenced by environmental context. Post-transcriptional regulation in bacteria, archaea, yeast and humans appears to be prominent during stress, such as during stationary phase in batch culture and in response to extreme changes in osmotic conditions (Bernstein et al., 2002; Fan et al., 2002; Redon et al., 2005; Guillier and Gottesman, 2006; Lange et al., 2007; Ingolia et al., 2009; Romero-Santacreu et al., 2009; Warringer et al., 2010; Evguenieva-Hackenberg and Klug, 2011; Maier et al., 2011; Miller et al., 2011; Munchel et al., 2011; Storz et al., 2011; Gierga et al., 2012). For example, numerous prokaryotic non-coding RNAs (ncRNAs) are known to carry out conditional post-transcriptional regulation of mRNAs, particularly in stress responses such as osmotic stress and iron limitation (Guillier and Gottesman, 2006; Richards and Vanderpool, 2011; Storz et al., 2011; Gierga et al., 2012). However, increased regulation at the posttranscriptional level is not a uniform response to all stress conditions. For example, yeast respond to glucose limitation primarily through transcriptional control but respond to nitrogen limitation primarily via post-transcriptional regulation (Kolkman et al., 2006). Similarly, Bacillus subtilis adapts to using glucose as a carbon source through a slow, primarily transcriptional response, while the response to malate occurs more rapidly and mostly at the post-transcriptional level (Buescher et al., 2012). Transcriptional and post-transcriptional regulation are associated with characteristic dynamic properties (Alon, 2007a). Mathematical modelling demonstrates that the most rapid regulation of a target protein level is accomplished post-transcriptionally by ncRNAs; proteinmediated post-translational regulation accomplishes the same at an intermediate rate; and the slowest approach is via protein-mediated transcriptional regulation (Levine et al., 2007; Shimoni et al., 2007; Mehta et al., 2008). Thus, one explanation for the non-uniform usage of transcriptional and post-transcriptional regulation across genes and environments is that selective interplay of these mechanisms is an evolved strategy to generate response dynamics well-suited to particular environmental perturbations (Gottesman, 2003; Wolf and Arkin, 2003; Levine

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et al., 2007; Shimoni et al., 2007; Mehta et al., 2008; Beisel and Storz, 2010; 2011a,b). Yet, there is little known about the architecture of interactions across transcriptional and post-transcriptional regulatory mechanisms or the selective pressures that might influence use of particular regulatory mechanisms. One class of post-transcriptional regulators is the ribonucleases (RNases), some of which control ‘bulk’ mRNA turnover while others regulate smaller sets of target mRNAs (Arraiano et al., 2010; Evguenieva-Hackenberg and Klug, 2011). Recognition of target mRNAs by specific RNases can be determined by a variety of factors, including sequence, secondary structure, ribosome occupancy, and binding by regulatory non-coding RNAs (Arraiano et al., 2010). Environment-specific roles for some RNases are suggested by observed patterns in the functional classes of genes regulated by these factors. For example, RNase III targets in E. coli are enriched in heat shock and iron transport functions (Evguenieva-Hackenberg and Klug, 2011), yeast RNase DIS3 controls cell-cyclerelated mRNAs (Lee et al., 2002), and the yeast RNase Rnt1p regulates many iron uptake mRNAs (Smith et al., 2011). Many RNases have also been shown to have environment-specific phenotypes and expression patterns. For example, DNA damage and nutrient deprivation responses are impaired in E. coli RNase E mutants (Lee et al., 2005; Tamura et al., 2012), iron shock and cell wall stress responses in yeast are dependent on the Rnt1p RNase (Manasherob et al., 2012), mice lacking the ZC3H12A RNase display uncontrolled inflammatory response (Matsushita et al., 2009), RNase R levels increase in multiple stresses including cold shock in E. coli (Chen and Deutscher, 2005), and E. coli RNase II levels are sensitive to nutrient conditions (Cairrão et al., 2001). Environment-specific RNase activity can also result from changes in protein localization and in the association with cofactors (Evguenieva-Hackenberg et al., 2011). For example, the localization of components of the eukaryotic RNA decay machinery into cytoplasmic foci such as P-bodies is dependent on environmental conditions (Stoecklin and Kedersha, 2013), conditional induction of the yeast protein Dcs1 activates the RNase Xrn1 and is important to growth on glycerol (Sinturel et al., 2012), increased expression of the Rsr protein in starvation in Deinococcus radiodurans recruits the RNase polynucleotide phosphorylase (PNPase) to certain RNA substrates (Wurtmann and Wolin, 2010), and the localization and activity of the RNase angiogenin toward certain RNA substrates is controlled by growth-state-dependent association with an inhibitor protein RNH1 in mammalian cells (Pizzo et al., 2013). Systematic analysis of the expression patterns, phenotypes and functions of RNases in environmental responses is an unmet need within the field. Nonetheless, it is clear from these widespread

observations that RNases play critical and specialized roles in environment-responsive gene regulation in organisms across all domains of life. Here, we have further investigated the selective fitness advantages of RNase-mediated post-transcriptional regulation of environmental response. Investigating the phenotypic and regulatory roles of the H. salinarum RNase VNG2099C, we discovered that the RNase plays a central role in salinity adaptation and in mediating transitions across environment-dependent states, such as those associated with aerobic and anaerobic physiologies. Moreover, the RNase contributes critically to the favourable bioenergetics of the H. salinarum strategy for halophilic physiology by regulating a postively autoregulated potassium transport operon. We observed that this network motif of RNase-repression of positive autoregulation (RPAR) is also present in E. coli, where it frequently regulates energy-consuming functions. Therefore, we conclude that interplay of transcriptional and posttranscriptional regulation in the RPAR motif is an evolutionarily conserved principle of gene regulation that may be well-suited for energy-efficient transitions across major environment-associated physiologic states.

Results The archaeal RNase VNG2099C contributes to growth and to regulation of ion transport To test for a role for RNases in environmental response in the model archaeon Halobacterium salinarum NRC-1, we first looked for fitness contributions of the RNases. Based on sequence homology we discovered that among the ∼ 2400 genes in the H. salinarum genome, there is at least one orthologue for each of 13 different RNases from both prokaryotic and eukaryotic lineages (Table S1). Upon screening for phenotypic consequences of deleting these RNase orthologues, we discovered a significant growth defect in the Δ2099 strain (Fig. 1A). The VNG2099C protein is significantly sequence-similar (e = 2 × 10−34) to the rat liver perchloric acid-soluble protein (L-PSP), a well-characterized endoribonuclease (Morishita et al., 1999) that is a member of the large YjgF/YER057c/UK114 protein family (Manjasetty et al., 2004). Key amino acid residues within the active site of L-PSP are also conserved in VNG2099C (Fig. S1). Interestingly, even the overexpression of VNG2099C in the parental strain background (Δura3/Pfdx-2099) resulted in poor growth, indicating the importance of regulation of its abundance (Fig. 1A). Deletion strains were also successfully constructed for three other RNase orthologues (four others failed multiple attempts and may be essential genes). None of these strains showed a significant phenotypic defect under standard growth conditions (Fig. S2); however, we note © 2014 John Wiley & Sons Ltd, Molecular Microbiology, 92, 369–382

RNase-mediated environmental response 371

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Fig. 1. Deletion of VNG2099C causes a growth defect. A. Perturbations in VNG2099C lead to a growth defect. Growth assays were performed in high throughput by tracking cell density at OD600 using the Bioscreen C instrument as described in Experimental procedures. Normalized growth was calculated by normalizing optical density measurements to the time zero measurement for each strain. Error bars, SEM (n = 4–12 biological replicates per strain). Average maximum optical density for the Δura3 strain was 0.6; similar phenotypes were observed in flask culture (Fig. S3). B. Genome-wide mRNA changes were measured at four points spanning the phases of batch culture growth (Fig. S3) of the Δura3 and Δ2099 strains. Log10 expression changes relative to a mid-log reference RNA are shown for genes with significantly increased expression in the Δ2099 strain as calculated by SAM analysis (Tusher et al., 2001; Saeed et al., 2006). ORFs are ordered vertically by descending values of d, a statistic computed in the SAM analysis measuring the strength of the relationship between gene expression and the experiment grouping (Δura3 versus Δ2099). Squares mark genes associated with K+ transport (red) or other transport functions (blue). C. The kdp operon structure. The bent arrow denotes the transcription start site. The kdp operon includes the positive autoregulator kdpQ in H. salinarum; in E. coli, the operon is controlled by a sensor kinase/response regulator system (Kixmüller et al., 2011). D. Gene expression levels for kdpA and kpdQ mRNAs measured by qRT-PCR, normalized to the Δura3 strain (error bars, SEM; n = 3 biological replicates).

that it is possible that these RNases may have conditionspecific growth defects. We proceeded to identify genes that were dysregulated in the Δ2099 strain. At four points spanning log and stationary phases of batch culture growth, we harvested total RNA from the Δura3 parental strain and the Δ2099 strain for genome-wide transcriptome analysis (Fig. S3). Based on the known repressive function of RNases, we predicted that deletion of VNG2099C would predominantly result in the upregulation of target genes. Indeed, significance analysis for microarrays (SAM) (Tusher et al., 2001; Saeed et al., 2006) with an FDR cut-off of 10% revealed that among 24 genes that were differentially regulated across the growth curve samples upon deletion of VNG2099C, the expression of 23 genes was significantly increased (Fig. 1B and Table S3). Of the © 2014 John Wiley & Sons Ltd, Molecular Microbiology, 92, 369–382

23 upregulated genes, 5 are associated with monovalent inorganic cation transport (GO:0015672; P = 1 × 10−3, Benjamini-corrected modified Fisher exact test). Notably, included in this set are genes from the polycistronic transcript that encodes the positive autoregulator KdpQ and the multisubunit Kdp potassium (K+) transport channel (Fig. 1C; Kixmüller et al., 2011). Using quantitative reverse transcriptase RT-PCR with primers targeting two key genes of this operon, we confirmed that the kdp transcript was upregulated fivefold upon deletion of VNG2099C (Fig. 1D). We also confirmed that the kdp transcript level was restored by genomic replacement of the deleted locus with a functional copy of VNG2099C (Δ2099::VNG2099). Further, we demonstrated that VNG2099C binds the kdpQ mRNA in vivo (Fig. S4). Formaldehyde cross-linking of live cells can stabilize tran-

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Fig. 2. Deletion of VNG2099C causes sensitivity to media ion composition and transport gene dysregulation. A. The growth defect of the Δ2099 strain is sensitive to growth media ion composition. Media are described in Table S2. Maximum growth rate of each strain was normalized to the maximum growth rate of the Δura3 parental strain in the standard culture media ([K] : [Mg] = 26:74). That value of mean maximum growth rate of 0.068 h−1 corresponds to a doubling time of approximately 10 h (log2/0.068), consistent with typical doubling times reported for this organism. Error bars, SEM; significant P-values are listed in red (t-test, n = 4–12 biological replicates per strain). B. Fold induction of kdpQ transcript levels before and after an increase in extracellular KCl concentration, calculated relative to replicate cultures without KCl addition as measured by qRT-PCR. kdpQ induction is statistically significant in the Δura3 strain at 12 h (P = 2 × 10−5, t-test, n = 3 biological replicates; error bars, SEM). C. Gene expression levels for VNG2099C mRNA measured by qRT-PCR at time points before and after an increase in extracellular KCl level (n = 3 biological replicates). This figure is available in colour online at wileyonlinelibrary.com.

sient RNA-protein associations, even for proteins involved in degradation of RNAs (Niranjanakumari et al., 2002; Gong and Maquat, 2011; Schmidt et al., 2011). Here, we used cross-linked immunoprecipitation of epitope-tagged RNase complexes and RT-PCR to show that kdpQ mRNA associates specifically with VNG2099C. We show specificity in two ways. First, we show that the kdpQ transcript is not non-specifically associated with any nucleic acid binding protein, by demonstrating lack of binding to TATAbinding protein B (TbpB). Further, we also show that the RNase is also not non-specifically associated with all transcripts, by quantifying the highly expressed glucose kinase mRNA in the VNG2099C pulldown. Taken together, the effect of VNG2099C deletion on kdp transcript levels (Fig. 1B and D) and the physical association of VNG2099C and kdpQ mRNA in vivo (Fig. S4) strongly support a role for VNG2099C in mediating degradation of the kdp K+ transport operon transcript. RNase VNG2099C is necessary for salinity adaptation of H. salinarum Life in a hypersaline environment is very expensive – it requires ATP-consuming production of osmolytes such as proline or the H. salinarum strategy of maintaining a high K+ cytoplasmic content (∼ 3 M) to counterbalance the high environmental concentration of Na+. The salt-in strategy is bioenergetically advantageous if it is managed efficiently

(Oren, 1999). When the K+ level in the environment is in low mM quantities, H. salinarum expends energy to pump K+ into the cytoplasm using the high-affinity ATP-driven Kdp K+ transport system. When external K+ concentration is higher, the cell can use a low-affinity transport system that does not consume as much energy and Kdp is downregulated (Kixmüller et al., 2011). External salinity can change abruptly in the natural habitats of H. salinarum (hypersaline lakes, oceans and brines) due to weather events in winter or evaporation in shallow water during summer. Switching on and off the high-affinity transport system therefore may be essential for favourable bioenergetics of the salt-in strategy. Thus, we investigated whether VNG2099C might aid in adaptation to changes in extracellular K+ levels by modulating K+ transport. Growth of H. salinarum is responsive to total salinity and to ion composition (Beer et al., 2014). Here, the effect of extracellular K+ levels on the growth of the Δ2099 strain was tested by altering the [KCl] : [MgSO4] ratio to hold total salinity constant. Indeed, an increased ratio of [KCl] : [MgSO4] exacerbated the growth defect of the Δ2099 strain (Fig. 2A). Complementation with functional VNG2099C (i.e. in the Δ2099::VNG2099 strain) rescued this phenotypic defect in all media conditions and confirmed that VNG2099C is responsible for the salinityresponsive growth defect. Further, VNG2099C was required for environment-responsive repression of the kdp transcript pursuant to an increase in extracellular KCl © 2014 John Wiley & Sons Ltd, Molecular Microbiology, 92, 369–382

RNase-mediated environmental response 373 wild-type

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Fig. 3. RNase-mediated phenotypic switching. H. salinarum cells use aerobic metabolism in log phase, dark and high O2 conditions (Schmid et al., 2007; Whitehead et al., 2009). In anaerobic conditions such as stationary phase, light and low O2, cells switch to alternate metabolic strategies including phototrophy and arginine fermentation. VNG2099C co-ordinates phenotypic switching in these oxic/anoxic transitions by regulating key genes in K+ transport (kdp), phototrophy (bop), and arginine fermentation (yhdG) (Fig. 1C, Table S3 and Fig. S1) and is itself co-regulated with aerobic metabolism genes (Fig. S7). In the aerobic state, membrane potential from the respiratory chain may be sufficiently low for passive K+ transport (Oren, 1999), phototrophy is not used (Baliga et al., 2002), and some arginine fermentation genes are downregulated (Ruepp and Soppa, 1996) (making kdp, bop and yhdG expression unnecessary). Another VNG2099C target, trkA2, is one of five TrkA homologues in the H. salinarum genome. TrkA is the NAD+/NADH-binding regulatory subunit of the H+-K+ symporter Trk and may couple K+ transport to cellular redox state (Corratgé-Faillie et al., 2010). In conditions where kdp expression is unnecessary, cells may gain a bioenergetic advantage through repression of kdp by the RNase VNG2099C. In the Δ2099 strain, positive autoregulation of the kdp operon leads to unchecked Kdp channel expression and ATP consumption, in turn leading to a growth defect (Fig. 4).

(Fig. 2B). Interestingly, the transcript level of VNG2099C increased transiently in response to the external upshift in KCl (Fig. 2C).

VNG2099C mediates physiologic state transition in response to oxygen shifts in diverse environments The anti-correlated transcript level changes in VNG2099C and its targets (Fig. 2B and C; Fig. S5) suggested that we could use this as a strategy for discovering additional environmental contexts in which VNG2099C represses target genes. Upon performing this analysis on an existing genome-wide gene expression dataset from 1495 environmental and genetic conditions (D.J. Reiss, A.N. Brooks and N.S. Baliga, unpublished; Bonneau et al., 2007), we discovered that VNG2099C and kdp gene expression are significantly anti-correlated across several environmental transitions associated with a switch from aerobic to anaerobic physiology (e.g. dark > light and log > stationary phase; Fig. S6). The environmental context for VNG2099C activity inferred from this analysis is consistent with the observation that VNG2099C deletion resulted in increased mRNA levels of genes associated with two anaerobic © 2014 John Wiley & Sons Ltd, Molecular Microbiology, 92, 369–382

energy transduction modules: the ATP-producing lightdriven proton-pump bacteriorhodopsin (bop) and an ornithine-arginine antiporter involved in anaerobic arginine fermentation (yhdG; Fig. 1B and Fig. S6). Moreover, analysis of the global gene regulatory network of H. salinarum (Bonneau et al., 2007) revealed that VNG2099C is co-regulated with aerobic TCA cycle and oxidative phosphorylation genes (Fig. S7). This observation suggested that VNG2099C degrades kdp, bop and yhdG mRNAs in oxic conditions, when membrane potential generated by the respiration-driven electron transport chain is sufficient for K+/H+ symport, rendering anaerobic energy production and ATP-consuming K+ uptake unnecessary (Fig. 3) (Oren, 1999; Baliga et al., 2002).

VNG2099C-mediated repression of the kdp operon conserves cellular energy Thus, we hypothesized that the slow growth phenotype of the Δ2099 strain results from a failure to repress the ATP-dependent Kdp transporter in high oxygen, leading to wasteful hydrolysis of ATP (Fig. 3). Indeed, intracellular ATP levels were 35% lower in the Δ2099 strain than in the

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Fig. 4. VNG2099C-mediated repression of the kdp operon conserves cellular ATP level and directly impacts growth rate. A. Intracellular ATP levels are statistically significant between the Δura3 and Δ2099 strains (P = 4 × 10−3, t-test, n = 6 biological replicates; error bars, SEM), the Δ2099 and the Δ2099::2099 strains (P = 4 × 10−3 n = 6 biological replicates; error bars, SEM), the Δ2099 and the Δ2099Δkdp strains (P = 1 × 10−3, n = 3 biological replicates; error bars, SEM), and the Δura3 and the Δ2099Δkdp strains (P = 1 × 10−2, n = 3 biological replicates; error bars, SEM). The intracellular ATP level of each strain was normalized to the level of the Δura3 parental strain (mean = 1.7 μM). B. Instantaneous maximum growth rate in media with varying [KCl] : [MgSO4] ratios (n = 4 biological replicates; error bars, SEM; the data for the Δura3 and Δ2099 strains are repeated from Fig. 4B). Maximum growth rate of each strain was normalized to the maximum growth rate of the Δura3 parental strain in the standard culture media ([K] : [Mg] = 26:74).

Δura3 parental strain (Fig. 4A; P = 4 × 10−3). This effect could be entirely attributed to VNG2099C-repression of the kdp operon, as ATP levels were fully restored upon reintroducing VNG2099C into the Δ2099 background (P = 4 × 10−3 versus the Δ2099 strain) or upon deletion of the kdp operon from the Δ2099 background (Fig. 4A; P = 1 × 10−3 versus Δ2099), both of which also rescued the slow growth phenotype (Fig. 4B). The Δ2099Δkdp and Δkdp strains both have elevated ATP levels relative to the Δura3 parental strain (Fig. 4A and Fig. S8), suggesting that the kdp operon affects the cellular energy state. Thus, these results together demonstrated that VNG2099Cmediated co-ordination of energy metabolism (oxidative phosphorylation, phototrophy and arginine fermentation) with on/off switching of energy-dependent K+ transport is central to maintaining favourable bioenergetics for salinity adaptation using the salt-in strategy. Gene regulatory network analysis reveals that RNases act in a characteristic motif To determine whether RNases also regulate positively autoregulated operons in other species, we performed a systematic analysis of the gene regulatory network governed by RNases in E. coli. We first compiled a comprehensive list of putative targets for the major RNases that act on mRNAs in E. coli (E, G, R, II, III and PNPase) by analysing publically available genome-wide transcriptional measurements of their corresponding mutant strains (Lee et al., 2002; Mohanty and Kushner, 2003;

Stead et al., 2011; Phadtare, 2012; Supplementary File S1). Upon analysing the list of putative RNase targets in the context of the E. coli transcriptional regulatory network (Salgado et al., 2012), we made the intriguing observation that the putative RNase targets indeed included several transcription factors (TFs) engaged in autoregulation loops. In E. coli, 105 out of 310 TF-containing operons are known to be transcriptionally autoregulated (Salgado et al., 2012). Of these, 25 are positively autoregulated, 69 are negatively autoregulated, and 11 display both positive and negative autoregulation. Over/under-enrichment analysis revealed that operons putatively targeted by RNases are enriched within the operons engaged in positive autoregulation (PAR) (9/25, P = 8 × 10−3, hypergeometric test with Bonferroni correction; Fig. 5A) versus negative autoregulation (6/69, P = 1.00) or positive and negative autoregulation (1/11, P = 1.00). In order to understand why RNases might preferentially target positively autoregulated operons, we investigated whether RNase-repression of PAR (RPAR) could create dynamics of gene expression that would be beneficial for certain environmental responses. Upon activation by a stimulus, PAR by a TF slows ‘switch on’ times and can generate a bimodal distribution of gene expression states across a cell population (Isaacs et al., 2003; Maeda and Sano, 2006; Alon, 2007b). When a positively autoregulated gene must be turned off, the transcriptional effect of PAR is to slow down ‘switch off’ times (Maeda and Sano, 2006; Siciliano et al., 2011). To examine the post© 2014 John Wiley & Sons Ltd, Molecular Microbiology, 92, 369–382

RNase-mediated environmental response 375 B

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transcriptional dynamics of autoregulated versus nonautoregulated operons, we first looked for differences in steady-state mRNA half-lives using published genomewide datasets (Bernstein et al., 2002; 2004). Interestingly, mRNAs from positively autoregulated operons are significantly more stable at the post-transcriptional level (average half-life of 4.5 versus 3.3 min for all other mRNAs, P = 1 × 10−4, Mann-Whitney U test with Bonferroni correction; Fig. 5B). Further, even within the total set of mRNAs from positively autoregulated operons, those putatively targeted by RNases have longer half-lives than those that are not (5.2 versus 3.7 min, P = 0.01, Mann– Whitney U-test). Therefore, RNase-based regulation may be essential to conditionally repress these normally slowresponding genes. We next asked why it might be favourable to perform environment-responsive repression of the mRNA (zr) of a positive autoregulator z using a RNase (RPAR) as opposed to a transcription factor (transcription factorrepression of PAR; TPAR). Because rigorously testing this question experimentally would require eliminating RNase recognition sites within a mRNA, potentially resulting in altered protein function due to the mRNA sequence changes, we instead used mathematical modelling to compare the dynamics of the two motifs (Fig. 6A). Using a model for each motif we simulated time (τ0.5) required for the repression of zr in response to an increase in stimulus x (Fig. 6B) and discovered that repression occurs more rapidly if the negative regulator y is a RNase rather than a TF (Fig. 6C–E). The difference in time (Δτ0.5) required for repression by each motif was calculated over a large parameter space for varying strengths of (a) influence of © 2014 John Wiley & Sons Ltd, Molecular Microbiology, 92, 369–382

the environmental stimulus x on synthesis of the repressor y (kxy), (b) the negative regulatory influence of y on degradation or synthesis of mRNA zr ( k yzr ) in RPAR or TPAR systems respectively, and (c) the positive autoregulation of mRNA zr synthesis by z ( k zzr ). This analysis revealed that the time required for repression by a RNase is significantly shorter relative to repression by a TF. Δτ0.5 increased when the stimulus was stronger, i.e. with greater influence on the activity of the negative regulator (positive values of log10(kxy0/kxy), Fig. 6C and D) and when y had a stronger negative regulatory influence on the target zr (positive values of log10 (k yzr 0 k yzr ) , Fig. 6C and E). Remarkably, Δτ0.5 also increased when the strength of PAR was greater (positive values of log10 (k zzr 0 k zzr ) , Fig. 6D and E). In other words, the modelling demonstrated that an RNase is significantly quicker, relative to a TF, at environment-responsive repression of a positively autoregulated transcript. Interestingly, many of the positively autoregulated operons that are RNase-targeted in E. coli contain genes with ATP-hydrolysis activity and/or membrane transportrelated functions (Table S4). This suggested that there may be adaptive benefit to regulating these biological functions with the dynamics associated with a RPARgenerated response.

Discussion RNase VNG2099C acts in salinity adaptation and environment-dependent physiologic transitions We observed that in the obligate halophile H. salinarum, a key potassium transport operon used to maintain osmotic balance is regulated by the RNase VNG2099C, both in normal growth conditions and in response to salinity perturbations. The RNase VNG2099C shows significant homology to proteins with demonstrated RNase activity in the YjgF/YER057c/UK114 protein family. Here, we present experimental evidence strongly indicative of a role for VNG2099C in mediating degradation of the kdp potassium transport operon mRNA: levels of kdp mRNA increase upon VNG2099C deletion, VNG2099C is required for repression of the kdp operon in response to shifts in extracellular potassium levels, and VNG2099C binds to the kdpQ mRNA in vivo. We note that the current data do not distinguish between the alternate hypotheses that VNG2099C acts directly on kdp mRNA as a RNase or that VNG2099C promotes the turnover of the RNA through some other activity on the RNA (e.g. chemical modification, structural remodelling of the RNA, or an effect on the interaction of the RNA with other protein or RNA factors). Nonetheless, a role for VNG2099C in regulating the stability of the potassium transport operon is consistent with the salinity-sensitive growth defect of the

376 E. J. Wurtmann et al. ■ B RPAR model ribonuclease repression of PAR

TPAR model transcriptional repression of PAR

signal kxy kyz r TF

target protein

r

target protein

target mRNA

target mRNA

target mRNA

kzz

kzz

kzz

r

OFF (TPAR - RPAR) 0.5

D

-log10 (P) values 4.5 3.5 2.5 1.5 0.5 1

4 3 2 0

-1 -1 log10(kxy0/kxy)

1 0

1

log10(kyz 0/kyz ) r

r

A OFF 0.5

A OFF

r

Δτ OFF(TPAR - RPAR) 0.5

r

C

Δτ

RNase kyz

Δτ OFF 0.5

τOFF 0.5

τOFF 0.5 Time

E -log10 (P) values 4.5 3.5 2.5 1.5 0.5 1

9 7 5 3

1 1

0 -1 -1 log10(kxy0/kxy)

0 log10(kzz 0/kzz ) r

r

Δτ OFF(TPAR - RPAR) 0.5

target protein

signal kxy

Response

PAR model positive autoregulation only

Signal

A

-log10 (P) values 4.5 3.5 2.5 1.5 0.5 1

9 7 5 3

1 1

0 -1 -1 log10(kyz 0/kyz ) r

r

0 log10(kzz 0/kzz ) r

r

Fig. 6. RNase disruption accelerates repression of postive autoregulation. A. The PAR, RPAR and TPAR motifs. B. Mathematical modelling of the motif models in (A) was performed by varying only motif topology, holding all other parameters equal between the models. The response time (τ0.5) after a stimulus is calculated for the half-maximal response (A0.5OFF) of target mRNA level. C. The difference in response time (Δτ0.5) for TPAR and RPAR. Larger values of Δτ0.5 indicate a faster response by RPAR versus TPAR. The Δτ0.5 values are calculated over varying strengths of the influences kxy and k yzr , where x is the stimulus, y is the negative regulator, and zr is the target mRNA. Positive values of log10(kxy0/kxy) and log10 (k yzr 0 k yzr ) indicate a strong effect of x on y and of y on zr respectively (on the target gene transcription level in the case of TPAR or on the target mRNA degradation level in the case of RPAR). P-values for the Δτ0.5 values (shown with heatmap colours) were calculated by two-sample Kolmogorov–Smirnov test. D. The difference in response time (Δτ0.5) is shown for TPAR and RPAR over varying strengths of the influences kxy and k zzr , where z is the target protein. Positive values of log10(kxy0/kxy) and log10 (k zzr 0 k zzr ) indicate a strong effect of x on y and of z on zr (autoregulation) respectively. E. The difference in response time (Δτ0.5) is shown for TPAR and RPAR over varying strengths of the influences k yzr and k zzr. Positive values of log10 (k yzr 0 k yzr ) and log10 (k zzr 0 k zzr ) indicate a strong effect of y on z and of z on zr (autoregulation), respectively. The reference parameter values for results presented in C-E are as follows: k xy 0 = k yzr 0 = k zzr 0 = 10 a.c.u. (see Experimental procedures).

Δ2099 strain. These observations indicate complex transcriptional and post-transcriptional regulation of salinity adaptation in H. salinarum, as the transcriptional regulators TfbB, TfbD, TfbE, SirR and TbpC have already been implicated in salinity response (Coker et al., 2007; Leuko et al., 2009; Turkarslan et al., 2011), and a decrease in external salt concentration has been observed to result in increased proteasome expression accompanied by increased proteasome activity, as measured by hydrolysis of a synthetic peptide (Chamieh et al., 2012). The transcriptional co-regulation of VNG2099C with oxidative phosphorylation genes, and its targeting of transcripts of the kdp operon and anaerobic energytransduction pathways, accomplishes oxygen-responsive, energy-efficient anaerobic-to-aerobic transitions. Regulation of a RNase with genes of one environmental state while it targets transcripts of an opposing state is a gener-

alized principle uncovered in this work that explains how biological systems can achieve efficient environmentdependent transitions. Furthermore, even though the H. salinarum pathways for potassium transport, phototrophy and arginine fermentation are under the control of independent transcriptional regulatory programs, the action of VNG2099C accomplishes their co-ordinated post-transcriptional repression when oxygen tension rises and their functions become simultaneously unnecessary. Interestingly, this scheme also allows other transcriptionally co-regulated genes to be unaffected across environmental conditions; e.g. carotenoid biosynthesis genes are transcriptionally co-regulated with bop but are not targeted by the RNase, allowing the pathway to continue to feed intermediates and cofactors to a wide array of cellular processes that operate across both high and low oxygen conditions. © 2014 John Wiley & Sons Ltd, Molecular Microbiology, 92, 369–382

RNase-mediated environmental response 377

VNG2099C contributes to the favourable bioenergetics of H. salinarum osmotolerance While the H. salinarum ‘salt-in’ osmotolerance strategy is in general less energetically costly than the ‘compatiblesolute’ strategy used by other halophilic species (Oren, 1999; 2010), active transport of K+ can still require large amounts of ATP and thus it is unsurprising that use of this mechanism is limited to conditions of low extracellular K+ (Kixmüller et al., 2011). Therefore, our finding that active K+ transport is regulated co-ordinately with phototrophy and arginine fermentation suggests that cells may gain an advantage from co-ordinating energyconsuming ion transport with the use of particular energy sources. We hypothesize that the slow growth phenotype, reduced intracellular ATP level and dysregulated Kdp expression observed in the Δ2099 strain may all be connected, in that overexpression of the ATP-dependent Kdp transporter could lead to wasteful hydrolysis of ATP during aerobic growth that results in a growth defect (Fig. 3). Alternatively, dysregulated Kdp expression in the Δ2099 strain could lead to independent effects on ATP level and growth rate if excessive levels of Kdp in the membrane lead to impairment of the respiratory chain, a leaky membrane, or some other perturbation of cellular function that limited growth.

A mixed transcriptional and post-transcriptional regulatory network motif with response dynamics suited for fast phenotypic switching On the transcriptional level, the arrangement of transcription factors and their target genes into recurring network motifs generates response dynamics that are well matched to particular information-processing functions and to certain types of environmental change (Alon, 2007b; Beisel and Storz, 2010). Characteristic network topologies have also been observed at the posttranscriptional level with ncRNAs often acting in singleinput modules, dense overlapping regulons and feedforward loops (Shimoni et al., 2007; Beisel and Storz, 2010; 2011a). Here, we have uncovered a generalized principle for the action of some RNases in a characteristic RPAR network motif. Increase in the strength of PAR generates a stable, bimodal distribution in the expression of the target gene(s) across a population of cells, enabling bet-hedging strategies for dealing with an unpredictable environment (Alon, 2007b). Biological systems have also coopted the bistability of PAR for generating cellular memory of a stimulus (Wolf and Arkin, 2003). However, repression of positively autoregulated transcripts occurs over a much longer time frame relative to transcripts that are not autoregulated © 2014 John Wiley & Sons Ltd, Molecular Microbiology, 92, 369–382

(Siciliano et al., 2011). This slow responsiveness of positively autoregulated genes to transcriptional repression may be exacerbated by their increased mRNA stability relative to non-autoregulated genes in E. coli. In general, post-transcriptional mechanisms can be better suited for rapid conditional regulation of mRNAs (Alon, 2007a; Levine et al., 2007; Shimoni et al., 2007; Mehta et al., 2008), which may explain why RNases are used frequently for the regulation of positively autoregulated transcripts. Indeed, our mathematical modelling shows that the action of an RNase can accomplish much quicker stimulus-responsive repression of positively autoregulated transcripts than would a TF. Notably, the acceleration of repression is greater for stronger PAR. Post-transcriptional regulation of negatively autoregulated genes might also generate regulatory responses with distinct dynamics that are appropriate for certain environmental responses. For example, small RNA-mediated regulation of the negatively autoregulated quorum-sensing regulator LuxO in Vibrio harveyi is thought to fine-tune the threshold of ligand concentration at which there is a switching of cell states (Tu et al., 2010). Physiological role of the RPAR motif Topology of a regulatory network is known to have significant consequences on cellular phenotype and fitness (Alon, 2007b). For example, altering regulatory network topology has been shown to change differentiation behaviour of B. subtilis (Süel et al., 2007) and also significantly decrease fitness of yeast by perturbing the fatty-acid response (Ramsey et al., 2008; Ratushny et al., 2008; 2011; 2012). Additionally, the dynamics of expression of carbon metabolism genes is greatly altered upon disruption of a feed-forward loop containing the E. coli global transcription factor CRP through deletion or overexpression of a regulatory ncRNA (Beisel and Storz, 2011a). More generally, the use of post-transcriptional regulation to control cellular metabolic state has also been observed to occur in E. coli through regulation of a number of metabolic pathways by PNPase, which is itself regulated by binding of the citric acid cycle metabolite citrate (Nurmohamed et al., 2011). In the case of the RPAR motif, the efficacy with which an RNase is able to turn ‘off’ positively autoregulated genes renders it especially advantageous for the regulation of genes whose unnecessary expression is bioenergetically expensive. Indeed, deleting VNG2099C results in untimely expression of the positively autoregulated ATP-consuming kdp operon, unnecessarily draining ATP and ultimately decelerating growth. The usage of the RPAR motif in this scheme might be widespread, as phylogenetically unrelated RNases across a bacterium (E. coli) and an archaeon (H. salinarum) have converged

378 E. J. Wurtmann et al. ■

upon RNase-mediated regulation of positively autoregulated transcripts associated with energy-consuming processes.

used to calculate the instantaneous maximum growth rate in log phase.

Transcriptome analysis

Experimental procedures Strains and culture conditions The Δ2099 strain was created from the H. salinarum NRC-1 Δura3 parental strain using unmarked, in-frame deletion as previously described (Turkarslan et al., 2011). The deletion strain retains the first 21 nucleotides and the final 3 nucleotides (stop codon) of the VNG2099C gene sequence. The Δ2099ΔkdpFABCQ strain was created from the Δ2099 strain with the same technique. The Δ2099::2099 genomic replacement strain was created in the Δ2099 strain background using a two-step in-frame gene replacement strategy to reintroduce the wild-type VNG2099C gene sequence into the same genomic locus. This strategy rules out any distal effect and directly attributes the observed phenotypic consequence to deletion of VNG2099C. The Pfdx-2099-CHA plasmid was used to express VNG2099C from the ferredoxin gene promoter in the Δura3 strain. This plasmid was constructed by insertion of the VNG2099C gene sequence amplified by forward (5′-GGCCGGCATATGAAGCGCCCGATTGAAACC-3′) and reverse (5′-GGCCGGAAGCTTGTCGCGCTGTGCGGCGAT GG-3′) primers into the pMTF_Pfdx_CHA plasmid digested with NdeI and HindIII. Following transformation, the strain carrying the Pfdx-2099-CHA plasmid was cultured with 0.02 mg ml−1 Mevinolin to select for maintenance of the plasmid. Strains were cultured at 37°C in a complex media (CM: 250 g l−1 NaCl, 20 g l−1 MgSO4, 2 g l−1 KCl, 3 g l−1 sodium citrate, 10 g l−1 Oxoid brand bacteriological peptone). Flask culture experiments were performed with continuous shaking (∼ 220 rpm) with 50 ml culture volume in 125 ml unbaffled flasks (Corning, New York, USA), conditions known to be sufficient for aerobic growth in log phase (Yao and Facciotti, 2011). The dissolved oxygen level in CM at 200 rpm shaking at 37°C was measured to be ∼ 60% saturation (∼ 1 mg l−1) using a Vernier dissolved oxygen probe (Beaverton, OR, USA). The standard CM has a [KCl] : [MgSO4] ratio of 26:74 and was used as the basis for the series of media of differing ion composition as listed in Table S2. Media were supplemented with 50 mg l−1 uracil to compensate for the uracil deficiency caused by the Δura3 marker. For KCl-shift experiments, cells were grown to stationary phase in media containing 2 mM KCl before addition of KCl to a final concentration of 100 mM.

Genome-wide RNA levels were determined for the Δura3 and Δ2099 strains at four points in the growth curve (Fig. S3), with strains grown in CM media in flasks using standard culturing conditions. Cells from 2 ml of culture were pelleted (16 000 g, 60 s) and flash frozen. Total RNA was isolated using the mirVana RNA kit (Life Technologies) following the manufacturer’s instructions and then treated with RNase-free DNase (Promega). Whole-genome tiling arrays were designed using e-Array (Agilent Technologies) for the H. salinarum NRC-1 main chromosome (NC_002607) and the pNRC100 (NC_001869) and pNRC200 (NC_002608) plasmids using a 60 000 feature design of 60-mer strand-specific probes spaced at 24 bp. Arrays included manufacturer’s control probes and were printed by Agilent Technologies. RNAs were direct labelled with Alexa547 and Alexa647 dyes (Kreatech), followed by hybridization, washing, slide scanning, spot finding and normalization as described (Turkarslan et al., 2011), with dye flip experiments performed for each sample. Data have been deposited in the GEO (GSE45988). Genes with significant differences in expression between the Δura3 and Δ2099 strains were identified by significance analysis for microarrays (SAM) (Tusher et al., 2001) with an FDR cut-off of 10% using two-class comparison of samples from the Δura3 andΔ2099 strains using MultiExperiment Viewer (MeV) (Saeed et al., 2006).

Reverse transcription RT-PCR Total RNA was isolated from 0.5 OD600 units of cells grown to mid-log phase using the Trizol reagent (Life Technologies) according to manufacturer’s instructions. RNA pellets were resuspended in H2O, treated with RNase-free DNase (Promega), and RNA quality and quantity was determined using a Nanodrop spectrophotometer (Thermo Fisher Scientific). Reverse transcription qRT-PCR analyses were performed in 96-well plates with the Power SYBR Green RNA-to-Ct 1-step kit and the Power SYBR Green RT-PCR Mix (Applied Biosystems) in a 7900HT Fast Real-Time PCR instrument (Applied Biosystems) using gene-specific primers for kdpA, kdpQ and VNG2629G. Fold differences in RNA levels were calculated by the ΔΔCt method using the glucose kinase gene (glcK; VNG2629G) as a reference gene. Triplicate biological replicates were used, with each qRT-PCR reaction performed in technical triplicates. Data analysis was performed using the SDS 1.2 software (Applied Biosystems).

Growth assays ATP measurement Growth assays were carried out in Bioscreen C instruments (Growth Curves USA, Piscataway, NJ). Starter cultures were grown to OD600 ∼ 0.5 and used to inoculate cultures at OD600 = 0.05 for growth at 37°C with continuous shaking (∼ 200 rpm) with OD600 measurement every 15 min for 100 h. Strains to be compared were assayed in the same run using multiple biological replicates of each strain; technical replicates were run in separate instruments. The Growth Curve Analysis Function R package (Turkarslan et al., 2011) was

Cultures were grown in standard flask growth conditions to mid-log phase (OD600 = 0.5–0.7). Cells (0.3 OD units) were removed, pelleted, washed in basal salt solution (CM without peptone) and lysed by resuspending in 0.3 ml H2O. ATP was measured using the ATP Bioluminescent Assay Kit (Sigma) by injecting 0.1 ml of 1:625 dilution of the bioluminescence assay mix into 0.1 ml of lysate and reading luminescence using a BioTek Synergy H4 Hybrid reader. © 2014 John Wiley & Sons Ltd, Molecular Microbiology, 92, 369–382

RNase-mediated environmental response 379

Analysis of E. coli transcript regulation

Parameter values for results presented in Fig. 6 are the following:

E. coli operon membership was retrieved from RegulonDB (Salgado et al., 2012). Putative RNase targets were determined as the union of genes with ≥ 2.5-fold increased expression in strains lacking RNase E, G, R, II, III or PNPase (Lee et al., 2002; Mohanty and Kushner, 2003; Stead et al., 2011; Phadtare, 2012; Supplementary File S1). Within these studies, validation for representative genes had been performed by qRT-PCR or Northern blot analysis.

Mathematical modelling The ordinary differential equations for Model 1 (PAR, a motif wherein a TF positively autoregulates levels of its own mRNA), Model 2 (RPAR, a motif with positive autoregulation plus a negative regulator that is a RNase that posttranscriptionally increases degradation of the target mRNA; the RNase is newly synthesized in response to a stimulus) and Model 3 (TPAR, a motif with positive autoregulation plus a negative regulator that is a TF that decreases synthesis of the target mRNA; the TF is newly synthesized in response to a stimulus) are as follows: Model 1 (PAR):

⎧ dz r = k f + (z , k , k , h ) − k z 0 zr szr zzr zzr dzr r ⎪ dt , ⎨ ⎪ dz = k sz z r − k dz z ⎩⎪ dt

⎧ dy = k f + ( x , k , k , h ) − k y sy 0y xy xy dy ⎪ dt ⎪ ⎪ dz r = k szr f + (z , k 0 zr , k zzr , hzzr ) − f + ( y , 0, k yzr , hyzr ) z r − k dzr z r , ⎨ dt ⎪ ⎪ dz ⎪⎩ dt = k sz z r − k dz z Model 3 (TPAR):

⎧ dy = k f + ( x , k , k , h ) − k y sy 0y xy xy dy ⎪ dt ⎪ ⎪ dz r = k szr f + (z , k 0 zr , k zzr , hzzr ) f − ( y , k yzr , hyzr ) − k dzr z r , ⎨ ⎪ dt ⎪ dz ⎪⎩ dt = k sz z r − k dz z

f + ( x , k 0, k x , hx ) =

x 1+ ⎛ ⎝k x

hx

hx

, f − ( x , k x , hx ) =

k 0 y = k 0 zr = 0.1, k xy 0 = k yzr 0 = k zzr 0 = 10 a.c.u., hxy = hyzr = hzzr = 1, where a.c.u. denotes arbitrary concentration units and a.t.u. denotes arbitrary time units. To estimate the sensitivity of the response time of the RPAR and TPAR systems to variation of the strength of the influences regulating the system, kxy, k yzr and k zzr were varied in the range of 0.1–10 times their reference values (kxy0, k yzr 0 and k zzr 0 respectively). The equations were solved numerically with Matlab 7.13.0.

Computational analysis of mathematical modelling The response time (τx) was calculated as the time that the system takes to reach x portion of its response between the initial and steady-state levels. P-values comparing τ0.5 value distributions between Models 2 and 3 were calculated by two-sample Kolmogorov–Smirnov test using the kstest2 MATLAB routine.

1 x 1+ ⎛ ⎝k x

We thank A. Brooks, C. Plaisier and D. Durudas for technical assistance and discussion and C. Funk for comments on the manuscript. This work conducted by ENIGMA was supported by the Office of Science, Office of Biological and Environmental Research, of the US Department of Energy under Contract No. DE-AC02-05CH11231. Additional funding was provided by grants from: the US Department of Energy (DE-SC0004877 to N.S.B.); the US National Science Foundation (EAGER – MSB-1237267 to N.S.B., NSF-1262637 to N.S.B. and MSB-1330912 to N.S.B. and J.D.A.); and the US National Institutes of Health (2P50GM076547 to N.S.B. and J.D.A., 2U54GM103511 to J.D.A., U54GM103511 to J.D.A., and Ruth L. Kirschstein National Research Service Award 5F32GM097931 to E.J.W.).

References

where x is a level of signal, y is the concentration of the RNase for Model 2 or the TF for Model 3, zr is a concentration of the target mRNA and z is a concentration of the target protein (Fig. 3). f + and f − represent fractional activity functions (Likhoshvai and Ratushny, 2007; Ratushny et al., 2011b) and are calculated using the following equations:

) )

k dy = k dzr = k dz = 0.1 a.t.u.−1,

Acknowledgements

Model 2 (RPAR):

x k0 + ⎛ ⎝k x

k sy = k szr = k sz = 1 a.c.u. a.t.u.,

)

hx

.

© 2014 John Wiley & Sons Ltd, Molecular Microbiology, 92, 369–382

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