SutA is a bacterial transcription factor expressed during slow growth in Pseudomonas aeruginosa

Brett M Babin; Megan Bergkessel; Michael J Sweredoski; Annie Moradian; Sonja Hess; Dianne K Newman; David A Tirrell

doi:10.1073/pnas.1514412113

. 2016 Jan 19;113(5):E597–E605. doi: 10.1073/pnas.1514412113

SutA is a bacterial transcription factor expressed during slow growth in Pseudomonas aeruginosa

Brett M Babin ^a,¹, Megan Bergkessel ^b,^c,^d,¹, Michael J Sweredoski ^e, Annie Moradian ^e, Sonja Hess ^e, Dianne K Newman ^b,^c,^d,², David A Tirrell ^a,²

PMCID: PMC4747698 PMID: 26787849

Significance

Pathogens that are dormant or growing slowly play important roles in chronic infections, but studying how cells adapt to these conditions is difficult experimentally. This work demonstrates that time-selective analysis of cellular protein synthesis, using bioorthogonal noncanonical amino acid tagging (BONCAT), can provide the sensitivity needed to identify important factors in slow-growth physiology. We identified in Pseudomonas aeruginosa, a previously uncharacterized transcriptional regulator that is expressed preferentially under slow-growth conditions, binds RNA polymerase, and has widespread effects on gene expression. This factor is one of several proteins of unknown function identified in our proteomic analysis, and our results suggest that further characterization of fundamental cellular processes under these conditions will shed light on important and understudied realms of biology.

Keywords: Pseudomonas aeruginosa, slow growth, transcription, proteomics, BONCAT

Abstract

Microbial quiescence and slow growth are ubiquitous physiological states, but their study is complicated by low levels of metabolic activity. To address this issue, we used a time-selective proteome-labeling method [bioorthogonal noncanonical amino acid tagging (BONCAT)] to identify proteins synthesized preferentially, but at extremely low rates, under anaerobic survival conditions by the opportunistic pathogen Pseudomonas aeruginosa. One of these proteins is a transcriptional regulator that has no homology to any characterized protein domains and is posttranscriptionally up-regulated during survival and slow growth. This small, acidic protein associates with RNA polymerase, and chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing suggests that the protein associates with genomic DNA through this interaction. ChIP signal is found both in promoter regions and throughout the coding sequences of many genes and is particularly enriched at ribosomal protein genes and in the promoter regions of rRNA genes. Deletion of the gene encoding this protein affects expression of these and many other genes and impacts biofilm formation, secondary metabolite production, and fitness in fluctuating conditions. On the basis of these observations, we have designated the protein SutA (survival under transitions A).

The cosmopolitan bacterium Pseudomonas aeruginosa is notorious as an opportunistic pathogen of burn wounds, medical devices, and the lungs of cystic fibrosis (CF) patients. The bacterium’s genome is large and encodes an unusually high proportion of regulators (1). Compared with Escherichia coli, P. aeruginosa possesses more σ factors that direct RNA polymerase (RNAP) to promoter regions (24 vs. 7), more DNA-binding activators and repressors that enhance or prevent RNAP binding and transcription (∼550 vs. 150) (2, 3) and more small, noncoding RNAs (ncRNAs) that modulate the stability or translation of target transcripts (200 vs. 100) (4, 5). Much effort has been directed toward understanding the mechanisms by which this regulatory capacity governs the behaviors—such as quorum sensing, protein secretion, secondary metabolite production, and biofilm formation—that contribute to P. aeruginosa virulence.

The physiological states of bacteria involved in chronic infections are substantially different from those most often studied in standard laboratory experiments; chronic infections are characterized by slow growth rates imposed by limited nutrients or oxidants or by host immune responses. Direct measurements of in situ microbial growth rates in the context of lung infections in CF patients have revealed doubling times of several days (6). Measurements of expectorated sputum show that hypoxic and anoxic zones exist within infected CF airways and can experience dramatic fluctuations in redox potential (7); P. aeruginosa strains isolated from the CF lung show gene expression patterns consistent with adaptations to hypoxia (8), suggesting that a lack of oxygen may limit growth. Although P. aeruginosa can generate energy in this environment by using nitrate as the terminal electron acceptor for respiration (9), levels of nitrate may be too low or too variable for nitrate respiration to represent the sole energy source in anoxic zones. P. aeruginosa can also remain viable for weeks in an anaerobic survival state by carrying out substrate-level phosphorylation to generate ATP, using either pyruvate [assisted by phenazines (10)] or arginine as a carbon and energy source (11, 12). The cells do not grow when limited to this type of metabolism, and little is known about how basic cellular processes are maintained.

We explored the P. aeruginosa anaerobic survival state by identifying the proteins that are synthesized in this energy-limited condition. Previous studies have characterized transcriptomic responses to low oxygen (13, 14) and have identified a few proteins that increase in abundance under conditions of anaerobic survival (15). The potential for important posttranscriptional regulation under stress conditions (16, 17) led us to take a proteomic approach, and the low metabolic rates that occur during anaerobic survival meant that the quantity of protein made after the shift to anaerobic conditions would likely be small relative to the size of the preexisting proteome. To address these challenges and specifically identify proteins associated with the anaerobic survival state, we used a time-selective proteome-labeling approach, referred to as bioorthogonal noncanonical amino acid tagging (BONCAT) (18, 19) to enrich and identify proteins made during anaerobic survival. We identified 91 proteins that were preferentially synthesized under anaerobic survival conditions compared with aerobic growth conditions in the same medium. Phenotypic screens of mutants lacking these proteins led us to focus on a single uncharacterized protein that is expressed under multiple slow-growth conditions and plays a role in biofilm formation, virulence factor production, and survival under transitions between different conditions. We used a combination of coimmunoprecipitation (co-IP), mass spectrometry, and sequencing to establish this protein as a transcriptional regulator. The protein binds RNA polymerase, causes widespread changes in gene expression, and plays a direct role in the regulation of genes encoding ribosomal components.

Results

BONCAT Enables Enrichment and Identification of Proteins Synthesized at Low Rates During Anaerobic Survival.

The BONCAT technique relies on pulse-labeling cultures with the methionine (Met) surrogate l-azidohomoalanine (Aha) (Fig. S1A), which is incorporated into nascent proteins by a cell’s endogenous translational machinery. Aha provides a chemical handle by which newly synthesized proteins can be distinguished and physically enriched from the prepulse proteome (Fig. S1B). To probe protein synthesis during anaerobic survival on arginine, we shifted an aerobic arginine culture to anaerobic conditions, allowed cells to adapt for 24 h, and then treated them with Aha (Fig. 1A). The total amount of incorporation of Aha into cellular protein during a 16-h pulse was approximately fourfold lower than that observed for an aerobic sample treated for only 15 min (Fig. 1B and Fig. S1 C and D), providing evidence of slow, but detectable, protein synthesis during anaerobic survival. Lysates from anaerobic and aerobic cultures were treated with an alkyne-biotin affinity tag, enriched for Aha-labeled proteins with streptavidin beads (Fig. S1F), and analyzed by liquid chromatography–tandem mass spectrometry (LC-MS/MS).

We identified 869 proteins overall; 50 were detected only in the anaerobic sample, and 273 were detected only in the aerobic sample (Fig. 1C). For the 546 proteins identified in both samples, we used label-free quantification to find proteins preferentially synthesized under each set of conditions. Peptide intensities were normalized to the total peptide intensity for each run, and the ratio for each protein was calculated as the median of its peptide ratios. We found 41 and 74 proteins whose anaerobic:aerobic ratios were significantly greater than or less than 1, respectively (Fig. 1D). Complete proteomic results are listed in Dataset S1. The 91 proteins that were more abundant or detected only in the anaerobic sample included proteins previously implicated in anaerobic growth or survival, such as targets of the oxygen-sensing regulator Anr: NirM, CcpR, PctA, PA14_06000, and the universal stress protein UspK (14, 15). More than one-third of the proteins, however, are annotated as “hypothetical proteins.” We hypothesized that this list of “anaerobic hits” might contain poorly characterized proteins that play important roles in regulating slow-growth physiology. To identify general regulators, we tested the ability of transposon mutants of these genes [from a mutant library (20)] to form biofilms—another growth condition in which nutrients and oxygen are limited and cells experience low metabolic rates (21).

We looked for defects in two modes of biofilm growth: as attached biofilms on a polystyrene substrate and as colony biofilms on agar plates (Fig. S2 A and B). Mutants for three genes showed defects in both biofilm assays: the pilus assembly protein FimV and hypothetical proteins PA14_44460 and PA14_69770. FimV and PA14_44460 have previously been implicated as contributors to type II secretion—a process known to be important for biofilm formation (22). In contrast, PA14_69770 has no homology to any characterized proteins or domains and has not been investigated to date. For this reason, we chose to study further the role of PA14_69770 in P. aeruginosa under survival and slow-growth conditions. Based on its contribution to fitness during transitions to and from these states, uncovered in our studies, we refer to this protein as SutA (survival under transitions A).

SutA Promotes Biofilm Formation, Inhibits Pyocyanin Production, and Confers a Fitness Advantage Under Fluctuating Conditions.

We generated a clean deletion strain (ΔsutA) and an arabinose-inducible overexpression strain (P_ara:sutA) to verify the results of the biofilm phenotype screens. Arabinose cannot support growth of P. aeruginosa when supplied as the sole carbon source and so does not act as a nutrient during induction of gene expression in this context. For all experiments involving arabinose-induced overexpression, arabinose was also added to the wild-type and ΔsutA strains to control for any potential physiological impacts. The deletion mutant formed smooth colony biofilms that lacked the complex wrinkled structures observed in wild-type biofilms, whereas the overexpression strain did not show substantially different colony morphology (Fig. 2A). The deletion strain also formed smaller biofilms, and the overexpression strain larger biofilms, on polystyrene compared with the wild type (Fig. 2B). The biofilm deficiencies of the mutant strain were not attributable to a growth defect, because there were no differences in growth rates between ΔsutA and the wild-type strain during aerobic planktonic culture in either rich or minimal media (Fig. S2C). There was, however, a strong effect of SutA on the colors of planktonic cultures; ΔsutA cultures were more blue and P_ara:sutA cultures less blue than the wild type. This effect was pronounced under nutrient-poor conditions, following aerobic growth in minimal medium containing pyruvate as a carbon source (Fig. 2C). The blue color of high-density P. aeruginosa cultures is often attributable to the presence of the redox-active phenazine pyocyanin (PYO), which plays roles in signaling and virulence and whose production is sensitive to various regulatory inputs (23–25). We measured the concentrations of PYO and its metabolic precursor phenazine-1-carboxylic acid (PCA) in culture supernatants using HPLC and found that ΔsutA produced more PYO and less PCA than the wild type, whereas P_ara:sutA showed the opposite effect (Fig. 2D). Absorbance measurements of culture supernatants gave the same results (Fig. S2D).

Fig. 2. — Phenotypic characterization of *sutA* mutants. (A) Colony biofilms were grown for 6 d at room temperature. (B) Biofilm growth on polystyrene was measured with the crystal violet assay (n = 4). (C) Cultures were grown in pyruvate minimal medium to stationary phase overnight at 37 °C. (D) Concentrations of PYO (blue) and PCA (orange) in culture supernatants were measured via HPLC. Average molar ratios are indicated above the plot (n = 3). (E) Cocultures of wild-type and *ΔsutA* strains were subjected to repeated rounds of anaerobic survival followed by outgrowth to midexponential phase in LB. After each outgrowth, the proportion of *ΔsutA* was measured by fluorescence microscopy. Error bars show SE (n = 6). The asterisk indicates a significant difference from the initial time point (paired Student’s t test, P < 0.05).

Because control of biofilm formation and phenazine production relies on integration of multiple regulatory inputs, particularly those related to changes in cell density and nutrient availability, we tested SutA’s contribution to the fitness of cells exposed to changing conditions. To detect subtle effects, we competed fluorescently marked wild-type and ΔsutA strains while they alternated between aerobic growth in Luria–Bertani (LB) and anaerobic survival in minimal arginine medium. On average, the wild-type strain significantly outcompeted ΔsutA after four transitions (Fig. 2E), and in five out of six trials, the wild-type strain showed a clear advantage after two transitions (Fig. S2E), suggesting that SutA is important during transitions to and from the survival state.

SutA Up-Regulation During Slow Growth Is Posttranscriptional.

We initially focused on SutA based on its up-regulation under anaerobic survival conditions, but its roles in biofilm formation and phenazine production under aerobic conditions suggested that its expression is not solely dependent on anoxia. To assay SutA expression at both the transcript and protein levels, we generated a reporter strain carrying a fusion of the sutA promoter, 5′ untranslated region (UTR), and 3′ UTR to gfp (P_sutA:gfp). Both 5′ and 3′ UTRs have previously been shown to impact transcript stability and translation (26), so our construct was designed to capture effects conferred by both regions. We measured GFP fluorescence per cell using flow cytometry during growth in LB and pyruvate minimal media, starting in midexponential phase (which takes longer to reach in pyruvate minimal media than in LB). In LB, reporter protein levels per cell were low during mid- and late-exponential phase (0–3 h) but increased up to eightfold in late-stationary phase, whereas transcript levels (shown normalized to the level measured at time 0 in LB) varied less than twofold throughout the experiment (Fig. 3, solid lines). In pyruvate medium, in which cells grow approximately fourfold slower compared with LB and remain in exponential phase for a longer time (0–14 h) (see also Fig. S2C), GFP fluorescence per cell was higher than in LB during exponential growth and increased slightly with culture density before decreasing in late-stationary phase. As in LB, normalized transcript levels showed little variation (Fig. 3, dashed lines).

Fig. 3. — SutA up-regulation during slow growth is posttranscriptional. A P_sutA:gfp cassette was transposed into a neutral locus of the wild-type strain. Optical density at 500 nm (A), per-cell GFP fluorescence (B), and *gfp* transcript abundance (C) were measured throughout growth in LB (circles and solid lines) and pyruvate minimal medium (squares and dashed lines). Error bars represent the SE of biological replicates (n = 3) and, in some cases, are smaller than the marker. RNA abundances were normalized by the housekeeping gene *oprI*. RNA and GFP measurements are relative to the value for the P_sutA:gfp strain in LB at time 0.

To verify that changes in fluorescence measurements reflected regulation of transcription and translation and were not attributable to accumulation of GFP, we constructed an analogous reporter that encoded a fusion of the promoter, 5′ UTR, and 3′UTR of the ribosomal protein gene rpsG to gfp (P_rpsG:gfp). As expected, per-cell GFP expression was high in exponential phase and decreased sevenfold in stationary phase (Fig. S2 F–H). In contrast to the sutA reporter construct, transcript and protein levels followed the same trend.

These results indicate that SutA up-regulation occurs in conditions that cause slow growth and does not require a lack of oxygen. Because slow growth in pyruvate minimal medium resulted in constitutive moderate expression of SutA and because we could clearly observe a phenazine phenotype resulting from sutA mutation in this medium, we chose to use late-exponential phase in pyruvate minimal medium for further study of the functions of SutA.

SutA Interacts with RNA Polymerase.

To gain insight into how SutA brings about the observed phenotypic changes, we sought to identify interacting protein partners. We generated an N-terminal hemagglutinin (HA)-tagged copy of SutA (HA-SutA) and verified that expression of this protein from the pMQ72 plasmid backbone in the ΔsutA background complemented the phenazine (Fig. 4A) and biofilm (Fig. 4B) phenotypes. We performed an immunoprecipitation (IP) against the HA epitope in this strain and in the ΔsutA strain carrying the empty pMQ72 vector following induction with arabinose in late-exponential phase in pyruvate minimal medium. We identified coprecipitating proteins via LC-MS/MS analysis of the eluent fraction. Proteins coprecipitated with HA-SutA or from the empty vector control were digested with trypsin and reacted with “medium” or “light” dimethyl labels, respectively. Peptides from both IPs were mixed and ratios directly quantified by LC-MS/MS. In two experiments, we identified three proteins that were enriched at least fivefold in the strain expressing HA-SutA compared with the empty vector control: the α, β, and β′ subunits of RNAP (RpoA, RpoB, and RpoC) (Fig. 4C). We also detected coprecipitation of RpoA with HA-SutA in the IP eluent fraction by Western blot (Fig. 4D). The presence of some RpoA signal in the unbound (“FT”) fraction suggests that not all cellular RNAP is tightly bound by SutA under the condition tested. We also performed the experiment in reverse by immunoprecipitating RNAP from the same cell lysates with an anti-RpoA antibody and identifying coprecipitated proteins via LC-MS/MS. When coprecipitated proteins were ordered by total peptide intensities, HA-SutA ranked above known RNAP-binders σ⁷⁰, NusA, and Rho (Fig. S3 and Dataset S2).

Fig. 4. — RNA polymerase coprecipitates with SutA. (A and B) Absorbance measurements of culture supernatants (A) and crystal violet (CV) measurements (B) of biofilm formation. (C) LC-MS/MS detection and quantification of proteins coimmunoprecipitated with HA-SutA. Each axis represents the protein abundance ratio as determined by dimethyl quantification between proteins coprecipitated from the pHA-SutA [medium (M)] or pMQ72 control [light (L)] strains. The three main subunits of RNAP are indicated. (D) IP fractions were analyzed for the presence of HA-SutA and RpoA via Western blots and for total protein via Coomassie staining (*Lower*). E, eluent; FT, flow-through; L, lysate; W, washes.

Fig. S3. — RpoA coimmunoprecipitated proteins. Total peptide intensities for proteins that coprecipitated with RpoA. Proteins are ranked by intensity from left to right. The α, β, and β′ subunits of RNAP (RpoA, RpoB, and RpoC, respectively), as well as the sigma factors RpoD and RpoS, the elongation factor NusA, and the termination factor Rho are shown in black. SutA is shown in red. Also see Dataset S2.

SutA Associates with Genomic Loci and Enhances Transcription of Ribosomal Genes.

To investigate the context of the interaction between SutA and RNAP and the effects this interaction might have on gene expression, we performed a chromatin IP (ChIP)-sequencing (Seq) experiment and an RNA-Seq experiment. The ChIP-Seq experiment was performed with the same strains and conditions used to detect the interaction with RNAP: the ΔsutA strain carrying HA-SutA on the pMQ72 arabinose-inducible plasmid and the ΔsutA strain carrying the pMQ72 empty vector as a control, both grown to late-exponential phase in pyruvate minimal medium in the presence of arabinose. We cross-linked protein–DNA complexes with formaldehyde, sonicated chromosomal DNA to generate fragments 0.5–1 kb in length, performed IPs against the HA epitope or against RpoA, and sequenced the coprecipitated DNA. For the RNA-Seq experiment, we sequenced rRNA-depleted RNA extracted from the wild-type, ΔsutA, and P_ara:sutA strains using the same growth medium and time point as for the ChIP-Seq experiment.

Because our IP experiment suggested that not all cellular RNAP was associated with SutA, we first sought to determine whether the interaction between SutA and RNAP occurs while RNAP is engaged in transcription, which should result in efficient formaldehyde cross-linking of SutA to genomic DNA, through concurrent interactions with RNAP. IP of HA-SutA led to an average recovery of 4% of input DNA compared with 0.2% in IPs from the empty vector control strain that did not encode HA-SutA (Fig. S4A), indicating that SutA likely interacts with RNAP while RNAP is interacting with genomic DNA. Over 1,400 of the ∼6,200 annotated genes showed a statistically significant enrichment in the HA-SutA IP compared with the empty vector IP, although the enrichment was greater than twofold for only 85 genes (Dataset S3). We next assessed the relationship between SutA and RNAP occupancies at genomic loci by comparing average per-gene reads per kilobase per million reads mapped (RPKM) from each IP. We saw a moderately strong correlation between the associations of SutA and RpoA across all genes (Fig. 5A; Pearson’s r = 0.77), suggesting that SutA and RNAP tend to colocalize throughout the chromosome. This degree of correlation with RNAP ChIP signal is similar to what has been observed for NusG in E. coli (r = 0.86) and GreA in Bacillus subtilis (r = 0.86), both of which bind RNAP during transcription elongation (27, 28). When the ChIP data were divided into 100-bp tiles across the entire chromosome, the correlation between RNAP signal and HA-SutA signal had an r value of 0.66, which is lower than the value previously calculated in E. coli for DksA (r = 0.79) but higher than that for σ⁷⁰ (r = 0.57), which dissociates from polymerase before transcription elongation (29). We noted that a subset of genes had ratios of SutA ChIP signal to RpoA ChIP signal that were substantially higher than the mean for all genes and found that many of these genes encoded ribosomal proteins (Fig. 5 A and B).

Fig. S4. — HA-SutA and RpoA ChIP. (A) DNA yields from ChIPs against the HA epitope from *ΔsutA* pHA-SutA and *ΔsutA* pMQ72 relative to input DNA were estimated by quantitative PCR for an intergenic region that was not enriched in the HA-SutA ChIP samples. (B) Average RPKM mapped for all genes from the RpoA IPs from *ΔsutA* pHA-SutA and *ΔsutA* pMQ72 (Pearson’s r = 0.94). (C) Normalized and scaled ChIP signals for HA IP from *ΔsutA* HA-SutA (blue) and *ΔsutA* pMQ72 (gray) and for RpoA IP from *ΔsutA* pHA-SutA (green) and *ΔsutA* pMQ72 (orange) across a chromosomal region containing the S10 (*rpsJ)* ribosomal protein operon. (D) ChIP results for tRNA genes. Heat maps show ratios for HA-SutA ChIP RPKM values compared with RpoA ChIP RPKM values from *ΔsutA* pHA-SutA (left column) and RpoA ChIP RPKM values between *ΔsutA* pHA-SutA and *ΔsutA* pMQ72. tRNAs encoded within rRNA operons are excluded. Because many tRNAs have substantial sequence similarity with each other, only sequencing reads that could be mapped uniquely are displayed, and only tRNAs with at least 10 unique RPKM in the RpoA IP from *ΔsutA* pHA-SutA are shown (45 of 62 tRNA genes). (E) qPCR measurements for the 16S leader sequence in the *ΔsutA* and P_ara:sutA strains compared with the wild-type strain. Circles show individual measurements. These data were averaged to generate the expression heat map shown in Fig. 5G. (F–I) Normalized ChIP signals at selected genetic loci. (Scale bar: 500 bp.) Traces are colored as in C. (J) COG distributions for genes up- and down-regulated by SutA, compared with the entire genome. The percentage of genes in each category is indicated with colored bars. Open black bars represent the proportion of the entire genome in each category. Markers indicate categories that are significantly overrepresented (‡) or underrepresented (*) (Fisher’s exact test, P < 0.001).

Fig. 5. — SutA localizes throughout the chromosome and enhances transcription of ribosomal genes. (A) ChIP signals (RPKM) for HA-SutA vs. RpoA for each gene. Genes encoding ribosomal proteins are highlighted (green) (Pearson’s r = 0.77). (B) Distribution of HA-SutA:RpoA ChIP signal ratios from the *ΔsutA* pHA-SutA strain for all genes (gray probability density plot) and for ribosomal protein genes (green histogram). (C) Distribution of the ratios of RpoA ChIP signal from *ΔsutA* pHA-SutA vs. *ΔsutA* pMQ72 for all genes (gray probability density plot), tRNAs (orange histogram), and rRNAs (blue histogram). The mean ratios for each subset are indicated above. (D and E) Normalized ChIP signals from each IP at the *rpsLG-fusA1* ribosomal protein operon (D) and for rRNA operons (E). (E, *Lower*) Legend describing strains and IPs for each trace. (F and G) Heat maps for ribosomal protein genes (F) and rRNA (G) showing ChIP signal ratios as calculated in B and C and transcript abundance ratios for *ΔsutA* and P_ara:sutA strains, each compared with the wild-type strain as determined by RNA-Seq (F) or qPCR (G).

We next asked whether RNAP association at genomic loci was affected by the presence of SutA. We compared average per-gene ChIP signals for RpoA between the strain expressing HA-SutA and the strain carrying the empty vector. We found a very high correlation in per-gene RpoA ChIP signals between these two strains (Fig. S4B; Pearson’s r = 0.94), suggesting that changes in the distribution of polymerase caused by the presence of SutA are subtle or limited to a small number of loci. Although the differences in RPKM per gene were not statistically significant on an individual gene basis, we did note some departures from the overall high correlation. In particular, both rRNA and tRNA loci tended to show higher RpoA ChIP signals in the strain expressing HA-SutA compared with the strain lacking SutA (Fig. 5C and Fig. S4D).

To establish a higher-resolution view of SutA and RNAP associations at ribosomal protein and rRNA loci, we examined ChIP-Seq reads per 100-bp tile across the relevant loci. We adapted the “apparent occupancy” metric described previously for displaying ChIP-chip data (27). Because some nonspecific IP of DNA is expected, the normalized read counts observed at the least expressed genes in the genome were used to define a baseline signal representing no true occupancy, and the counts observed at the highest peaks in each sample that were associated with protein coding genes were used to define a maximum signal for that sample. All count values in each sample were then scaled from 0 to 1 based on the calculated baseline and maximum values for that sample. The count values for the IP from the empty vector strain are included for comparison and are scaled to the baseline and maximum values calculated for the HA-SutA IP to best facilitate the comparison (the dynamic range for the empty vector IP was small, as expected for a control IP in which association is nonspecific) (see SI Experimental Methods and Datasets S4 and S5 for more information).

Ribosomal protein loci exhibited distinct peaks in RNAP and SutA signal near their transcription start sites (Fig. 5D and Fig. S4C). The SutA peak was shifted very slightly downstream from the RpoA peak, and the ratio of SutA signal to RpoA signal was high over promoter and coding regions, consistent with what was observed in the per-gene analysis. The presence of SutA did not result in a significant difference in RpoA signal at any individual ribosomal protein gene locus, but across all ribosomal protein genes, there appears to be a trend toward increased RpoA signal in the presence of SutA (Fig. 5F). Because the sequences of the four rRNA operons are nearly identical, these loci were aligned and the signals for homologous 100-bp tiles from each operon were averaged (Fig. 5E). Although the rRNA genes did not show high levels of HA-SutA ChIP signal relative to RpoA ChIP signal in our per-gene analysis, this higher-resolution view shows that a very strong peak of SutA signal is centered just upstream of the start of the 16S gene, near the predicted P2 transcription start site, with a lower ratio of SutA to RpoA signal across the coding region. This view also shows a statistically significant increase in the RpoA signal at the rRNA promoter region in the presence of SutA, which was missed in our per-gene analysis. These two features are distinct from the observations for the ribosomal protein loci.

We then investigated whether the presence of SutA at ribosomal protein and rRNA genomic loci, and the changes in RNAP localization to rRNA in particular, might impact their expression. To assess the effects of SutA on ribosomal protein gene mRNA levels, we queried our RNA-Seq dataset. We measured small but statistically significant differences in mRNA abundance among the three strains for a majority of the ribosomal protein genes [46 of 55 genes; false-discovery rate (FDR)-adjusted P value, <0.05] (Dataset S3). In general, ribosomal protein genes were expressed at higher levels in the P_ara:sutA strain, and at lower levels in the ΔsutA strain, compared with the wild-type strain (Fig. 5F). Because the stability of mature rRNA makes it a poor indicator of rRNA transcription rates, and because rRNA was intentionally depleted from our RNA-Seq samples before library construction, we used quantitative PCR (qPCR) against cDNA from the 16S leader sequence as a proxy for levels of new rRNA synthesis. The ΔsutA strain had levels of the 16S leader that were twofold lower compared with either the wild-type strain or the overexpression strain (Fig. 5G and Fig. S4E). Taken together, the ChIP and RNA abundance measurements suggest that the presence of SutA has a direct and positive effect on the transcription of both ribosomal protein and rRNA genes but that the nature of the interactions with these two types of loci may be distinct. Extensive work by many laboratories (reviewed in ref. 30) has shown that regulation of rRNA transcription occurs primarily at the level of initiation, whereas regulation of ribosomal protein gene transcription occurs mostly during elongation. Consistent with this regulatory paradigm, our ChIP data suggest association of SutA primarily in the promoter regions of rRNA genes but throughout the coding regions of ribosomal protein genes. Also potentially consistent with these two modes of regulation, we see a decrease in RpoA ChIP signal in the absence of SutA for rRNA genes but much less so for ribosomal protein genes. Further study will be required to elucidate the mechanistic details of these two possible regulatory modes.

SutA Localizes to Many Nonribosomal Genes and Has Broad Effects on Gene Expression.

Ribosomal proteins and rRNAs are notable as classes of genes that had high levels of SutA association and whose transcript levels were significantly changed. However, the influence of SutA was not limited to these loci; much of the chromosome (∼20% of all 100-bp regions) showed statistically significant enrichment for the HA-SutA IP compared with the empty vector IP. To explore the general pattern of association of SutA with genomic loci, we identified a “high ChIP signal” subset of 230 transcriptional units that (i) had high-quality peaks in both RpoA and SutA ChIP signals near their starts (defined as having an apparent occupancy greater than 0.25 for RpoA and 0.20 for SutA) and (ii) showed a statistically significant enrichment in the HA-SutA ChIP signal compared with the empty vector ChIP signal. For those that had annotated transcriptional start sites and were not among the ribosomal protein and RNA genes discussed above (n = 171), we averaged ChIP signal values from 500 bp upstream to 1,000 bp downstream of that location to generate aggregate traces of the associations of RNAP and HA-SutA across nonribosomal loci (Fig. 6A). The average pattern of RpoA and SutA association across these transcriptional units was similar to that observed for the ribosomal protein genes: RpoA association was centered at the transcriptional start site and a broader peak of HA-SutA was centered slightly downstream. This aggregate includes upstream regions that drive transcription of diverging transcription units as well as those for which adjacent transcription units are on the same strand, so the breadth of the observed peaks may reflect limits of the resolution of our ChIP technique as well as contributions from binding to adjacent transcriptional units.

Fig. 6. — SutA has broad effects on gene expression. (A) Average ChIP signals around transcriptional start sites (TSS) for genes in the high ChIP signal subset. Shaded regions around each trace represent the 95% confidence interval for the mean (n = 171). Traces represent the following: *ΔsutA* pHA-SutA, anti-HA (blue); *ΔsutA* pHA-SutA, anti-RpoA (green); and *ΔsutA* pMQ72, anti-RpoA (orange). The direction of transcription is from left to right. (B) Numbers of genes in the high ChIP signal subset and genes whose expression changed more than twofold between the *ΔsutA* and P_ara:SutA strains. (C) Heat maps (as in Fig. 5 F and G) for genes found in both subsets.

We next investigated whether SutA association at nonribosomal transcriptional units was also associated with increased expression. To focus on likely direct effects, we examined the 24 genes that were among the high ChIP signal subset and also showed greater than twofold changes in transcript levels; 22 of these genes (92%) had higher transcript levels in the overexpression strain than in the deletion strain (Fig. 6 B and C), suggesting, as was observed for the ribosomal protein and rRNA genes, that the presence of SutA at these genomic loci tends to enhance their transcription. Higher-resolution views of specific loci reinforced the observations from the aggregate analysis: transcription units exhibited a broad peak of HA-SutA association centered downstream of the peak of RpoA association. PA14_10380 is predicted to encode a protein that is structurally similar to bacteriocins and is among the highest ranked-genes both in terms of SutA association and differential expression between the ΔsutA and the P_ara:sutA strains (Fig. S4F) (31). PA14_21220 encodes the universal stress protein UspK (Fig. S4G), and PA14_26020 encodes an aminopeptidase (Fig. S4H). In each of these cases, the apparent occupancy of RpoA in the promoter region is higher in the SutA-containing strain.

Many of the genes that were differentially expressed in the SutA mutants were not among the genes that showed the highest ChIP signal, and many genes that had high ChIP signal did not show large SutA-dependent changes in gene expression (Fig. 6B). This pattern is likely attributable to several factors. First, because the presence of SutA generally enhances transcription at loci to which it is recruited, decreased expression in the presence of SutA may be attributable largely to the shift of free RNAP to highly expressed loci that are up-regulated by SutA (e.g., rRNA). Our data show several transcriptional units that recruit significantly more RNAP in the absence of SutA (as evidenced by higher RpoA ChIP peaks in the strain lacking HA-SutA and no significant SutA association in the HA-SutA ChIP experiment) and that have increased expression in the ΔsutA strain; PA14_40800 and PA14_40100-40110, divergently transcribed, are two examples (Fig. S4I). Second, the list of genes that are likely directly regulated by SutA includes the components of the ribosome as well as known master regulators such as the stationary-phase transcription factor psrA (32). Increased expression of these genes is likely to cause widespread secondary effects, which may explain why some genes that are up-regulated in the presence of SutA do not show strong HA-SutA ChIP signal. Third, as suggested by our analysis of rRNA and ribosomal protein genes, SutA may affect different aspects of transcription for different genes (e.g., initiation vs. elongation), with different patterns of ChIP signals and expression levels resulting. Further work is required to fully understand the impacts of SutA on different genes and different phases of gene expression.

Finally, to take a broad view of the effects of SutA, both direct and indirect, on the physiological state of the cell, we grouped the genes that differed more than twofold between the ΔsutA and the P_ara:sutA strains according to their functional designations from the Clusters of Orthologous Groups (COG) categories (33) and asked whether any groups were differentially represented compared with the genome as a whole (Fig. S4J). In general, genes that were up-regulated in the presence of SutA tended to have functions related to energy generation and maintenance; these genes included proteases, oxidoreductases, and alternate metabolism genes. Conversely, genes involved in growth and carbohydrate and amino acid metabolism were significantly underrepresented. Genes that were down-regulated were more likely to be involved in defense mechanisms, signaling, and motility. For the full set of results, see Dataset S3 and GEO accession no. GSE66181.

SI Experimental Procedures

Strain Construction.

See Table S1 for a full list of strains. An unmarked deletion of sutA (DKN1625) was generated by first cloning 1 kb of sequence upstream and downstream of this gene into the pMQ30 suicide vector (49). This vector carries the URA3 gene from Saccharomyces cerevisiae, which facilitated the use of homologous recombination in yeast to stitch together the three DNA pieces. The upstream and downstream 1-kb regions were amplified from P. aeruginosa genomic DNA (gDNA) and cleaned up using the PCR purification kit (Qiagen). Linearized pMQ30 plasmid was transformed along with the 1-kb flanking regions into S. cerevisiae using standard methods, and successful transformants were selected on media lacking uracil. The pMQ30 plasmid carrying the upstream and downstream sequences for sutA was recovered from the yeast colonies by extraction with phenol:chloroform:isoamyl alcohol and transformed into E. coli DH5α cells. The construct was verified by sequencing and introduced into P. aeruginosa UCBPP-PA14 by triparental conjugation. Successful exoconjugants were selected on VBMM medium (3 g/L trisodium citrate, 2 g/L citric acid, 10 g/L K₂HPO₄, 3.5 g/L NaNH₄PO₄, 1 mM MgSO₄, 100 μM CaCL₂, pH 7) containing 100 µg/mL gentamicin as described by Choi and Schweizer (50) and were then subjected to counterselection on LB plates lacking NaCl and containing 10% (wt/vol) sucrose. Colonies resulting from homologous recombination to remove the wild-type copy of sutA and retain the clean deletion were identified by PCR.

Table S1.

Strains and plasmids

Name	Genotype	Source
P. aeruginosa strains
DKN263	P. aeruginosa UCBPP-PA14
DKN1625	UCBPP-PA14 ΔsutA	This study
DKN1626	UCBPP-PA14 attTn7:: P_ara:sutA Gm^R	This study
DKN1627	UCBPP-PA14 attTn7:: mini-Tn7T-Gm^R P_sutA:gfp	This study
DKN1628	UCBPP-PA14 attTn7:: mini-Tn7T-Gm^R P_rpsG:gfp	This study
DKN1632	UCBPP-PA14 attTn7:: mini-Tn7T-Gm^R P_A1/04/03:gfp	This study
DKN1633	UCBPP-PA14 attTn7:: mini-Tn7T-Gm^R P_A1/04/03:cfp	This study
DKN1634	UCBPP-PA14 ΔsutA attTn7:: mini-Tn7T-Gm^R P_A1/04/03:gfp	This study
DKN1635	UCBPP-PA14 ΔsutA attTn7:: mini-Tn7T-Gm^R P_A1/04/03:cfp	This study
Transposon insertion mutants	UCBPP-PA14 Gene::MAR2xT7	Ref. 20
E. coli strains
DKN1298	SM10, pTNS1	Ref. 50
DKN1299	HB101	Ref. 50
	(F− λ− Δ(gpt-proA)62 leuB6 glnV44(AS) araC14 galK2(Oc) lacY1 Δ(mcrC-mrr) rpsL20(StrR) xylA5 mtl-1 recA13 hsdS20), pRK2013
	pRK2013 has a ColE1 replicon and carries the RK2 tra genes and Tn903 (which is KanR)
DKN1323	Tpr Smr recA thi pro (rK mK) RP4:2-TC:MuKm Tn7 lambda pir, pMCM11(containing attTn7:: mini-Tn7T-Gm^R P_A1/04/03:gfp)	Gary Schoolnik
DKN1325	Tpr Smr recA thi pro (rK mK) RP4:2-TC:MuKm Tn7 lambda pir, pMCM11 derivative (containing attTn7:: mini-Tn7T-Gm^R P_A1/04/03:cfp)	Gary Schoolnik
DKN1637	DH5α (F⁻ endA1 glnV44 thi-1 recA1 relA1 gyrA96 deoR nupG Φ80dlacZΔM15 Δ(lacZYA-argF)U169, hsdR17(r_K⁻ m_K⁺), λ–),pMQ30_sutA	This study
DKN1639	Mach1 (ΔrecA1398 endA1 tonA Φ80ΔlacZM15 ΔlacX74 hsdR(r_K⁻ m_K⁺)), pUC18T-mini-Tn7T-Gm^R P_ara:sutA	This study
DKN1640	Mach1 (ΔrecA1398 endA1 tonA Φ80ΔlacZM15 ΔlacX74 hsdR(r_K⁻ m_K⁺)), pMQ72_HasutA	This study
DKN548	F− Δ(argF-lac)169 Φ80dlacZ58(ΔM15) glnV44(AS) λ− rfbC1 gyrA96(NalR) recA1 endA1 spoT1 thi-1 hsdR17 deoR, pMQ72	George O’Toole
DKN1641	DH10β (F⁻ endA1 recA1 galE15 galK16 nupG rpsL ΔlacX74 Φ80lacZΔM15 araD139 Δ(ara,leu)7697 mcrA Δ(mrr-hsdRMS-mcrBC), λ⁻), pUC18T-mini-Tn7T-Gm^R P_sutA:sfgfp	This study
DKN1642	DH5α (F⁻ endA1 glnV44 thi-1 recA1 relA1 gyrA96 deoR nupG Φ80dlacZΔM15 Δ(lacZYA-argF)U169, hsdR17(r_K⁻ m_K⁺), λ–), pUC18T-mini-Tn7T-Gm^R P_rpsG:sfgfp	This study
S. cerevisiae strains
DKN569	InvSc1:MATa/MATα his3D1/his3D1	Invitrogen
DKN569	leu2/leu2 trp1-289/trp1-289 ura3-52/ura3-52	Invitrogen

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Strains and plasmids used in this study. Plasmids are stored as E. coli strains carrying the plasmid, and requests should be for the E. coli strain.

The strain overexpressing SutA (DKN1626) was constructed by first cloning the sutA coding sequence into the multiple cloning site of the expression vector pMQ72, downstream of the arabinose-inducible P_ara promoter, using yeast homologous recombination as described above. The P_ara promoter:sutA coding sequence cassette was then cloned into the pUC18T-miniTn7T-Gm^R vector to direct its insertion into the attTn7 site of P. aeruginosa (50), using the Gibson reaction (51). This vector was introduced into P. aeruginosa UCBPP-PA14 by tetraparental conjugation and verified by PCR.

To construct the plasmid for overexpression of HA-tagged SutA, the sutA gene, along with 1 kb upstream and downstream, was cloned from P. aeruginosa gDNA with a 5′ overhang encoding the HA epitope (MYPYDVPDYA) and inserted into pMQ30 using the Gibson reaction. The HA-sutA gene was then amplified and cloned into the multiple cloning site of pMQ72 between the SacI and KpnI restriction sites (DKN1640). This vector was transformed into P. aeruginosa by electroporation.

The GFP- and CFP-marked wild-type and ΔsutA strains (DKN1632-1635) carry their respective fluorescent proteins under the control of the strong P_A1/04/03 promoter, integrated into the attTn7 site and marked by a gentamicin resistance cassette. The fluorescent markers were introduced into P. aeruginosa by tetraparental conjugation with E. coli strains carrying the respective fluorescent protein-encoding plasmids, which were gifts from the laboratory of Gary Schoolnik, Stanford University, Stanford, CA (52).

The superfolder GFP reporter strains (DKN1627-1628) were generated by first amplifying 1 kb of sequence upstream and the intergenic sequence downstream of the sutA and rpsG genes from P. aeruginosa gDNA. These fragments were cloned upstream and downstream of the sfGFP coding sequence (53) in the pUC18T-miniTn7T-Gm^R vector using the Gibson reaction, and the resulting construct was introduced into the attTn7 site in P. aeruginosa by tetraparental conjugation.

Media and Growth Conditions.

All cultures were grown at 37 °C with shaking unless otherwise noted. Liquid media were LB (5 g/L yeast extract, 10 g/L tryptone, and 10 g/L NaCl), 2xYT (10 g/L yeast extract, 16 g/L tryptone, and 5 g/L NaCl), or phosphate-buffered minimal medium (35.9 mM K₂HPO₄, 14.2 mM KH₂PO₄, 9.3 mM NH₄Cl, 42.8 mM NaCl, 1.0 mM MgSO₄, 7.5 µM FeCl₂·4H₂O, 0.8 µM CoCl₂·6H₂O, 0.5 µM MnCl₂·4H₂O, 0.5 µM ZnCl₂, 0.2 µM Na₂MoO₄·2H₂O, 0.1 µM NiCl₂·6H₂O, 0.1 µM H₃BO₃, and 0.01 µM CuCl₂·2H₂O) with carbon sources added as noted. All anaerobic cultures were incubated in butyl rubber-stoppered Balch tubes in a Coy anaerobic chamber supplied with an atmosphere of 5% H₂, 15% CO₂, and 80% N₂, with trace amounts of oxygen removed by palladium-catalyzed reaction with the hydrogen gas. Anaerobic cultures were incubated without shaking.

BONCAT Labeling and Enrichment.

Aha (54) and the dialkoxydiphenylsilane (DADPS) biotin-alkyne probe (55) were synthesized as previously described. P. aeruginosa PA14 was grown overnight in LB and diluted to an OD₅₀₀ of 0.02 into minimal medium containing 40 mM arginine, pH 7.2. The culture was grown to an OD₅₀₀ of 0.4 and split into aerobic and anaerobic samples. To label aerobic cultures, Aha was added to a final concentration of 1 mM. After 15 min of incorporation, cells were washed once with PBS and cell pellets were frozen at –80 °C. Anaerobic samples were moved to an anaerobic chamber, washed with PBS, resuspended in minimal medium with 40 mM arginine, and sealed in Balch tubes. Anaerobic cultures were allowed to consume residual oxygen and adapt to anoxia for 24 h. Aha was then added to a final concentration of 1 mM. After 16 h of incorporation, cells were pelleted, washed with PBS, and lysed immediately. For anaerobic samples, all steps up to and including lysis were performed using degassed solutions in the anaerobic chamber.

All samples were lysed by resuspension in lysis buffer (100 mM Tris⋅HCl, pH 8, 1% SDS). Lysates were heated to 65 °C for 5 min and clarified by addition of Benzonase Nuclease (Sigma Aldrich) for 1 h at 37 °C, followed by centrifugation. For fluorescence detection of Aha-labeled proteins, lysates were reacted with 5 µM TAMRA-alkyne (Click Chemistry Tools), 100 µM CuSO₄, 500 µM Tris(3-hydroxypropyltriazolylmethyl)amine (THPTA), 5 mM aminoguanidine hydrochloride, and 5 mM sodium ascorbate (56) for 15 min at room temperature, precipitated with water, methanol, and chloroform, and washed twice with methanol. Reacted lysates were separated via SDS/PAGE and imaged on a Typhoon gel imager (GE Healthcare). Gels were stained with Colloidal Blue (Life Technologies) to verify equal protein loading.

For protein enrichment, 0.5 mg of each protein lysate was reacted with 100 µM DADPS biotin-alkyne probe as above for 3.5 h at room temperature. Proteins were precipitated with acetone at −20 °C and resuspended in PBS, 0.3% SDS. Streptavidin UltraLink Resin (Pierce Biotechnology) was washed twice with PBS, added to biotinylated lysates, and incubated overnight at 4 °C. Resin was transferred to microfuge spin columns (Pierce Biotechnology) and washed twice with 1% SDS in PBS and once with 0.1% SDS in PBS. Proteins were eluted by cleavage of the DADPS linker via incubation with 5% formic acid and 0.1% SDS in PBS for 2 h at room temperature. Resin was washed with 0.1% SDS in PBS to elute all proteins. Elution fractions were combined and concentrated by centrifugation through Amicon Ultra spin columns (EMD Millipore). The entirety of the concentrated eluents were separated via SDS/PAGE and stained with Colloidal Blue.

Protein Digestion, Mass Spectrometry, and Data Analysis.

For Gel LC-MS/MS (GeLCMS), gel pieces were destained by alternating washes with 50 mM ammonium bicarbonate (AB) and 1:1 50 mM AB:acetonitrile. Proteins were reduced by incubation with 6.7 mM dithiothreitol (DTT) in 50 mM AB at 50 °C for 30 min and alkylated by incubation with 37 mM iodoacetamide in 50 mM AB at room temperature for 20 min. Gel pieces were washed with 100 mM AB and then with acetonitrile. Proteins were digested with 300 ng of endoproteinase LysC in 100 mM Tris⋅HCl at 37 °C for 18 h. Peptides were extracted by sequential washing with the following: 1% formic acid/2% acetonitrile, 1:1 acetonitrile:water, and 1% formic acid in acetonitrile. Extracted peptides were dried and desalted using C18 StageTips as previously described (57).

For in-solution digestion, proteins were brought to a final concentration of 8 M urea, reduced by incubation with 3 mM Tris(2-carboxyethyl) phosphine (TCEP) for 20 min at room temperature, and alkylated by incubation with 10 mM iodoacetamide for 15 min at room temperature in the dark. For IP, proteins were digested with 250 ng of endoproteinase LysC for 18 h at room temperature. Samples were further digested by dilution with 100 mM Tris⋅HCl to a final urea concentration of 2 M and addition of 600 ng of trypsin and 1 mM calcium chloride at room temperature for 9 h. Digestion was quenched by addition of 5% formic acid. Digested peptides were desalted by HPLC using a Michrom Bioresources C18 macrotrap (buffer A: 0.2% formic acid in H₂O; buffer B: 0.2% formic acid in acetonitrile) and concentrated in vacuo. Peptides were dimethyl labeled following established protocols (47) and mixed in a 1:1 mass ratio.

Liquid chromatography–mass spectrometry was essentially carried out as previously described (58). Anaerobic vs. aerobic BONCAT and IP experiments were performed on a nanoflow LC system, EASY-nLC 1000 coupled to a hybrid linear ion trap Orbitrap Classic mass spectrometer (Thermo Fisher Scientific) equipped with a nanoelectrospray ion source (Thermo Fisher Scientific) with the following modifications. For the EASY-nLC II system, solvent A consisted of 97.8% H₂O, 2% ACN, and 0.2% formic acid, and solvent B consisted of 19.8% H₂O, 80% ACN, and 0.2% formic acid. For the LC-MS/MS experiments, digested peptides were directly loaded at a flow rate of 500 nL/min onto a 16-cm analytical HPLC column (75 μm inside diameter) packed in-house with ReproSil-Pur C₁₈AQ 3-μm resin (120-Å pore size; Dr. Maisch). The column was enclosed in a column heater operating at 30 °C. After 30 min of loading time, the peptides were separated with a 50-min gradient at a flow rate of 350 nL/min. The gradient was as follows: 0–30% B (50 min) and 100% B (10 min). The Orbitrap was operated in data-dependent acquisition mode to automatically alternate between a full scan (m/z 400–1,600) in the Orbitrap and subsequent 10 collision-induced dissociation (CID) MS/MS scans in the linear ion trap. CID was performed with helium as collision gas at a normalized collision energy of 35% and 30 ms of activation time.

For the BONCAT experiment, raw files were searched using MaxQuant (59) against the P. aeruginosa PA14 UniProt entries (5,886 sequences) and a contaminant database (246 sequences). Trypsin was specified as the digestion enzyme with up to two missed cleavages. Carbamidomethylation of cysteine was set as a fixed modification, and protein N-terminal acetylation and methionine oxidation were variable modifications. We also included variable modifications of methionine corresponding to Aha, reduced Aha, Aha reacted to the DADPS linker, and Aha reacted to the cleaved DADPS linker. Protein ratios and their SEs were calculated using bootstrap estimates and pooled variance estimates at the peptide level (60). Briefly, peptide intensities were normalized to the total intensity for each run, and a global estimate of measurement error was calculated using pooled variance from all peptide ratios between each sample. The protein ratio was calculated as the median of peptide ratios. The SE of the protein ratio was calculated using a bootstrap procedure where resampling of peptide ratios is augmented by adding a random “noise” effect drawn from a normal distribution with mean zero and SD equal to the previously calculated global estimate of measurement error. In total, 1000 bootstrap iterations were performed. The SE of the protein ratio was then calculated as the SD of the bootstrapped peptide ratios. Z tests were then used to calculate P values of overall protein ratios with respect to a 1-to-1 ratio. P values were adjusted for false discovery by the Benjamini–Hochberg procedure.

For dimethyl-labeling experiments, raw files were searched using MaxQuant as above. Trypsin was specified as the digestion enzyme with up to two missed cleavages. Carbamidomethylation of cysteine was set as a fixed modification and protein N-terminal acetylation and methionine oxidation were variable modifications. Dimethyl mass modifications (light and medium) at lysine residues and peptide N termini were specified for quantification.

Colony Morphology Assay.

Cultures were grown overnight in LB medium, diluted 1:1,000, and spotted in a 10-µL volume on solid media (1% tryptone, 1% Bacto Agar, 20 μg/mL Coomassie blue, and 40 μg/mL Congo red) (44). Plates were incubated at room temperature for 6 d and then imaged using a Keyence VHX-1000 digital microscope.

Crystal Violet Assay.

The crystal violet assay was performed as previously described (45). Cultures were grown overnight in LB and diluted 1:1,000 into LB; 125 µL of each diluted culture was transferred to 96-well round-bottom polystyrene plates coated for tissue culture (Corning). Plates were sealed with parafilm and incubated for 18 h at 37 °C without shaking. Wells were washed with 0.9% NaCl and treated with 150 µL of 0.1% crystal violet for 20 min at room temperature. Wells were washed three times with water, and crystal violet was extracted from adherent cells by addition of ethanol. Ethanol containing crystal violet was transferred to a new well plate, and absorbance at 600 nm was measured. The average absorbance for wells containing only LB was subtracted from all measurements. Each strain was measured in two separate experiments, with four wells per experiment.

Phenazine Measurements.

Phenazine concentrations in culture supernatants were measured as described previously (23). Briefly, culture supernatants were filtered using SpinX columns with a 0.2-µm pore size and were directly loaded onto a Beckman System Gold reverse-phase HPLC instrument with a UV-visible light (Vis) detector and a Waters Symmetry C₁₈ analytical column (5-μm-particle size; 4.6 mm by 250 mm). A gradient of water-0.1% trifluoroacetic acid (TFA) (solvent A) to acetonitrile-0.1% TFA (solvent B) at a flow rate of 1.0 mL/min was used to elute phenazines, which can be detected based on their characteristic absorption wavelengths and retention times. Peak areas for samples were compared with peak areas from standards of purified PCA and PYO.

Competition Assay.

Individual overnight cultures of wild-type cells carrying a gfp or a cfp marker and ΔsutA cells carrying a gfp or a cfp marker were grown in 5 mL of LB medium. Cultures were diluted 1:1,000 in LB medium and mixed in equal proportions based on their OD500 in the following combinations: (i) wild type, gfp-marked plus ΔsutA, cfp-marked; (ii) wild type, cfp-marked plus ΔsutA, gfp-marked; and (iii) wild type, gfp-marked plus wild type, cfp-marked. The mixtures were allowed to grow to midexponential phase (OD500, ∼0.4), with shaking at 37 °C. Small aliquots of the mixed cultures were taken for microscopy (time 0 sample), and the remainders were pelleted and transferred to an anaerobic chamber (Coy), where they were resuspended in anaerobic minimal medium with 40 mM arginine and placed in sealed Balch tubes. Cultures were incubated anaerobically at 37 °C for 19–20 h, then were removed from the anaerobic chamber, diluted 1:100 or 1:200 into LB medium, and allowed to grow aerobically with shaking at 37 °C for 4–6 h, back to midexponential phase. No significant change in OD500 occurred during the anaerobic incubation. Once cells reached midexponential phase, a small aliquot of the culture was taken for microscopy (transfer 1 sample), and the remainders of the cultures were pelleted and resuspended again in the anaerobic arginine medium in the anaerobic chamber. This process was repeated for four transfers. At each transfer, epifluorescence microscopy using a Zeiss Axio Imager microscope was used to observe live cells placed on agarose pads. GFP was detected using the Zeiss 46HE filter cube, and CFP was detected using the Zeiss 47HE filter cube. The percentage of cells carrying each marker in each mixed culture was counted. At least 500 cells were counted for each sample. A very small bias in favor of carrying CFP over GFP was detected in the wild-type vs. wild-type mixed culture (combination iii), so at each time point, the proportion of each marker in this culture was taken to reflect the “no advantage” state, and the wild-type vs. ΔsutA proportions were adjusted by the difference observed because of carrying GFP vs. CFP. The adjusted proportions in the two marker-flipped cultures (combinations i and ii) were averaged together. The entire experiment was performed three times.

GFP Reporter Protein Measurement.

For growth, transcript, and reporter protein measurements, starter cultures were grown to stationary phase in LB medium, diluted 1:1,000 into either LB or pyruvate minimal medium, and allowed to grow into early-exponential phase (approximately 4 h for LB or 18 h for pyruvate), at which point, the “time 0” measurements were made. Live cells in liquid culture were diluted between 1:250 and 1:1,000 into TBS (50 mM Tris, pH 8.0, 150 mM NaCl) containing the SYTO 62 red-fluorescent, cell-permeant nucleic acid stain (Thermo Fisher Scientific) at a concentration of 500 nM. Cells were incubated at room temperature in the dark for 15–20 min to allow for DNA staining. The BD Accuri c6 flow cytometer was used to measure both red fluorescence from the SYTO 62 dye (excitation laser: 640 nm; emission filter: 675/25 nm) and green fluorescence from GFP (excitation laser: 488 nm; emission filter: 533/30 nm). Particles were gated on forward scatter vs. red fluorescence, and the mean green fluorescence for particles with red fluorescence and forward scatter values consistent with cells was measured. At each time point, mean green fluorescence in a strain lacking GFP was also measured to determine background autofluorescence, and this value was subtracted from the GFP values for that time point. GFP was measured for biological triplicates for each genotype and condition.

RNA Extraction.

Total RNA was extracted from cells using the RNeasy Mini Kit (Qiagen). Briefly, ∼10⁹ cells were pelleted rapidly by centrifugation at 14,000 × g, the supernatant was removed, and the pellet was immediately frozen in liquid nitrogen. After all samples were collected, pellets were resuspended in TE buffer (10 mM Tris, pH 7.5, 1 mM EDTA) plus 15 mg/mL lysozyme (Sigma) and 15 U per sample proteinase K (Qiagen), and incubated for 10 min at 37 °C to digest the cell wall. Samples were then processed according to the manufacturer’s instructions, including on-column DNase treatment. Purified RNA was quantified by absorbance at 260 nm, and 10 µg per sample was treated with Turbo DNase Free (Ambion) according to the manufacturer’s instructions. Samples were verified to be free of genomic DNA by qPCR.

qPCR.

DNase-treated total RNA (1 µg) was converted to cDNA using the iScript cDNA synthesis kit (Bio-Rad); 1/100th of this reaction mixture (representing 10 ng total RNA) was used per qPCR reaction, along with 500 nM each of forward and reverse primers and the iTaq SYBR Green reaction mix (Bio-Rad). Samples were run on the ABI platform (ABI) for 40 cycles with an annealing temperature of 60 °C. Standard curves for each primer pair were generated using serial dilutions of genomic DNA. The oprI gene was used to normalize against potential loading differences. See Table S2 for primer sequences. Measurements were made on biological triplicates.

Table S2.

Primers used in strain construction and qPCR experiments

Name	Purpose	Sequence
6977del1	Generating SutA deletion construct	`tgggtaacgccagggttttcccagtcacgacgttgtaaaaCTGCTCACCGGGATCTTCGC`
6977del2	Generating SutA deletion construct	`TGGCGGGCCTTGGGATGACGCGAAAGGTCAACCTCTCGGTGCTGCAAAAG`
6977del3	Generating SutA deletion construct	`CTTTTGCAGCACCGAGAGGTTGACCTTTCGCGTCATCCCAAGGCCCGCCA`
6977del4	Generating SutA deletion construct	`tgtgagcggataacaatttcacacaggaaacagctatgacGTTCAGCCGGGCGGCAGCGA`
P_ara:sutA1	Cloning SutA into pMQ72	`ccatacccgtttttttgggctagcgaattcgagctcAGGAGGGGTTGACCATGAGCGAAG`
P_ara:sutA2	Cloning SutA into pMQ72	`gcaaattctgttttatcagaccgcttctgcgttctgatttaaAAATCAGATGGGGCGGCT`
sutA_gfp1	Generating SutA:gfp reporter construct	`agtataggaacttcagagcgcttttgaagctaattcgatcCTGCTCACCGGGATCTTCGC`
sutA_gfp2	Generating SutA:gfp reporter construct	`TGAACAGCTCTTCGCCTTTACGCATGGTCAACCTCTCGGTGCTGCAAAAGC`
sutA_gfp3	Generating SutA:gfp reporter construct	`GCTTTTGCAGCACCGAGAGGTTGACCATGCGTAAAGGCGAAGAGCTGTTCA`
sutA_gfp4	Generating SutA:gfp reporter construct	`TGGCGGGCCTTGGGATGACGCGAAATCATCATTTGTACAGTTCATCCATA`
sutA_gfp5	Generating SutA:gfp reporter construct	`TATGGATGAACTGTACAAATGATGATTTCGCGTCATCCCAAGGCCCGCCA`
sutA_gfp6	Generating SutA:gfp reporter construct	`atagtttggaactagatttcacttatctggttggcctgcaGGGATGACAACCGATGTGTC`
rpsG_gfp1	Generating RpsG:gfp reporter construct	`agtataggaacttcagagcgcttttgaagctaattcgatcATCAAAGGCGACCAGGTGGA`
rpsG_gfp2	Generating RpsG:gfp reporter construct	`TGAACAGCTCTTCGCCTTTACGCATTGATAAGCCCTCAAACGGTCTTCAG`
rpsG_gfp3	Generating RpsG:gfp reporter construct	`CTGAAGACCGTTTGAGGGCTTATCAATGCGTAAAGGCGAAGAGCTGTTCA`
rpsG_gfp4	Generating RpsG:gfp reporter construct	`CCTTTTCTGATGGCAGGATCAGCGATCATCATTTGTACAGTTCATCCATA`
rpsG_gfp5	Generating RpsG:gfp reporter construct	`TATGGATGAACTGTACAAATGATGATCGCTGATCCTGCCATCAGAAAAGG`
rpsG_gfp6	Generating RpsG:gfp reporter construct	`atagtttggaactagatttcacttatctggttggcctgcaGACCTCAGACTCCAATTTAC`
HAsutA1	Generating HA-SutA	`GACCGCATGTACGCCGAAGcggggatcctctagagtcgacctgcaggca`
HAsutA2	Generating HA-SutA	`cagctatgaccatgattacgaattc`
HAsutA3	Generating HA-SutA	`tgcctgcaggtcgactctagaggatccccgCTTCGGCGTACATGCGGTC`
HAsutA4	Generating HA-SutA	`cagcaccgagaggttgaccATGTACCCATACGATGTTCCAGATTACGCT`
HAsutA5	Generating HA-SutA	`ATGTACCCATACGATGTTCCAGATTACGCTatgagcgaagaagaactggaac`
HAsutA6	Generating HA-SutA	`cagctatgaccatgattacgaattcACGAGATTGAACGGGGTAAC`
HAsutA7	Moving HA-SutA to pMQ72	`atatggtaccCTTCGGCGTACATGCGGTC`
HAsutA8	Moving HA-SutA to pMQ72	`atatgagctcACGAGATTGAACGGGGTAAC`
Sfgfp_f	qPCR	`TGGTGTTCAGTGCTTTGCTC`
Sfgfp_r	qPCR	`TGTACGTGCCGTCATCCTTA`
oprI_f	qPCR	`AGCAGCCACTCCAAAGAAAC`
oprI_r	qPCR	`CAGAGCTTCGTCAGCCTTG`
Intergenic_f	qPCR	`GGGGTGGGGGTAGTTAAAGA`
Intergenic_r	qPCR	`GCAAAACAAGCCCCTACAAA`
16Sleader_f	qPCR	`ACGAAAGCCTTGACCAACTG`
16Sleader_r	qPCR	`TTGCGCTGCTGATAATCTTG`

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f, forward; r, reverse.

Co-IP.

Cultures of ΔsutA carrying pMQ72 or pMQ72_HAsutA were grown overnight in minimal medium containing 40 mM sodium pyruvate, 20 mM arabinose, and 50 µg/mL gentamicin to an OD₅₀₀ of ∼1. Cells were washed once in PBS and frozen at −80 °C. Cell pellets were resuspended in IP lysis buffer [50 mM Hepes, 70 mM potassium acetate, 5 mM magnesium acetate, 0.2% n-dodecyl-β-d-maltoside, and cOmplete mini protease inhibitor, EDTA-free (Roche)]. Cells were gently lysed by passage through a 22-gauge needle 10 times. Lysates were clarified by incubation with Benzonase Nuclease for 1 h at 37 °C, followed by centrifugation.

For IP of HA-SutA, 50 µL of agarose beads conjugated to an anti-HA antibody (Sigma-Aldrich) were washed three times in IP lysis buffer, combined with 1 mL of lysate, and incubated with rotation overnight at 4 °C. For IP of RpoA, 1 mL of lysate was incubated with an anti-RpoA antibody (gift of Olaf Schneewind, University of Chicago, Chicago) for 1 h at 4 °C with rotation. 50 µL Protein A/G PLUS-agarose beads (Santa Cruz Biotechnology) were washed three times with IP lysis buffer, combined with the antibody–lysate mixture, and incubated with rotation overnight at 4 °C. For both IPs, beads were washed twice with 0.5 mL of IP lysis buffer and twice with 0.5 mL of 100 mM Tris⋅HCl, pH 8. Proteins were eluted by incubation with 64 µL of 10 M urea in 100 mM Tris⋅HCl. IP eluents were digested in-solution, reacted with dimethyl labels, and analyzed by LC-MS/MS, as described above.

For Western blotting, 10 µL of each IP fraction (lysate, flow-through, four washes, and elution) were separated by SDS/PAGE and transferred to a Hybond ECL membrane (GE Healthcare). Membranes were blocked with 5% milk in TBST (50 mM Tris⋅HCl, pH 7.5, 150 mM NaCl, 0.05% Tween 20). HA-SutA was detected by anti-HA antibody–Alexa Fluor 594 conjugate (Life Technologies). RpoA was detected by incubation with the primary anti-RpoA antibody described above, followed by incubation with a goat anti-mouse antibody–Alexa-Fluor 633 conjugate (Life Technologies). On a separate gel, the same samples were stained with Coomassie.

RNA Seq Library Preparation.

For RNA-Seq experiments, starter cultures were grown to stationary phase in LB, diluted 1:1,000 in pyruvate minimal medium containing 25 mM arabinose, and then allowed to grow 21 h until they reached late-exponential phase again (OD₅₀₀, ∼1), at which point, cells were collected for RNA extraction (described above); 3.8 µg of DNase-treated total RNA was subjected to rRNA depletion using the Gram Negative Magnetic Ribo-Zero kit (Epicentre), according to the manufacturer’s instructions. Following rRNA depletion, samples were cleaned up using the RNeasy MinElute kit (Qiagen), and libraries were generated for sequencing using the NEBNext mRNA Library Prep Kit for Illumina (NEB). Briefly, mRNAs were fragmented by treatment with MgCl-containing fragmentation buffer for 1 min at 94 °C and cleaned up using the RNeasy MinElute columns. Fragmentation to an average size of ∼200 bp was verified by running the samples on a Bioanalyzer RNA Pico chip (Agilent). The fragmented RNA was reverse-transcribed to cDNA, which was then end-repaired, dA-tailed, and ligated to adaptors. Each sample was PCR-amplified with a universal primer and a unique barcoded primer, using 12 amplification cycles. Final libraries were verified using the High-Sensitivity DNA chip on the Bioanalyzer and quantified using the Qubit fluorimeter and dsDNA dye (Invitrogen). Sequencing was performed on biological triplicates for each genotype.

ChIP.

Growth conditions were the same as for the RNA-Seq experiments, except 20 mM arabinose was used and 50 µg/mL gentamicin was added for plasmid maintenance. Late-exponential phase cultures of the ΔsutA strain (DKN1625) carrying either pMQ72 or pMQ72_HAsutA in pyruvate minimal medium were cross-linked by incubation with 1% formaldehyde at room temperature for 15 min, and then cross-linking was quenched by incubation with 125 mM glycine for 10 min. Cells were pelleted and washed twice with TBS (50 mM Tris, pH 7.5, 150 mM NaCl), and then pellets were frozen at −80 °C. Frozen pellets were resuspended in 1.5 mL of IP buffer (50 mM Tris, pH 7.5, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, 0.1% deoxycholic acid, 1 mg/mL lysozyme) and incubated at 37 °C for 15 min. Samples were then chilled on ice and sonicated using a microtip sonicator for 4 min at the 4.0 setting, using a cycle of 30 s on, 30 s off. Samples were split in half; one half was subjected to IP by an antibody against RpoA, whereas the other half was subjected to IP by an antibody against the HA epitope, as described for protein IP above. For the RpoA IP, samples were precleared by incubation with 1/10 volume Protein A/G PLUS-agarose beads (Santa Cruz Biotechnology) for 1 h at 4 °C and then were incubated overnight with rotation at 4 °C with the anti-RpoA antibody. Next, 50 µL of the protein A/G agarose beads were added, and the mixture was incubated for an additional 1 h at 4 °C. For the HA-SutA IP, samples were incubated with 50 µL of preconjugated HA bead slurry overnight with rotation at 4 °C. The beads from both IPs were then washed five times for 10 min per wash. Washes 1 and 2 were with IP buffer, wash 3 was with IP buffer with 500 mM NaCl, wash 4 was with stringent buffer (10 mM Tris, pH 8.0, 250 mM LiCl, 1 mM EDTA, 0.5% Nonidet P-40), and wash 5 was with TBS. DNA/protein complexes were eluted from the beads in 100 µL of elution buffer (50 mM, Tris pH 7.5, 10 mM EDTA, 1% SDS) by incubation for 15 min at 65 °C. The elution was repeated once, and both eluates were combined and then were incubated at 65 °C overnight to reverse cross-links; 200 µL of TE buffer (10 mM Tris, pH 8.0, 1 mM EDTA), 100 µg of proteinase K, and 20 µg of glycogen were added to each sample, and the samples were incubated for 2 h at 37 °C to digest proteins. DNA was extracted using 25:24:1 phenol:chloroform:isoamyl alcohol and precipitated with ethanol. The precipitated DNA was resuspended in 30 µL of TE buffer containing 10 µg of RNase A and incubated at 37 °C for 2 h, to remove RNA contamination, and then was cleaned up using a QIAquick column (Qiagen) with an elution volume of 50 µL (48).

ChIP Seq Library Preparation.

Purified genomic DNA (2–10 ng) isolated by IP was subjected to further fragmentation by treatment with the NEB ds Fragmentase enzyme mixture for 10 min at 37 °C. This treatment reduced the average fragment size from ∼500–1,000 to ∼200–500 bp for optimal high-throughput sequencing efficiency. Fragmented DNA was cleaned up using Agencourt AMPure XP magnetic beads (Beckman Coulter). Libraries were prepared from the fragmented gDNA using the NEBNext ChIP Seq Library Prep Reagent Set for Illumina (NEB). DNA fragments were end-repaired, dA-tailed, ligated to adaptors, and PCR-amplified with one universal and one barcoded primer, using 15 amplification cycles. Final libraries sizes were verified using the Bioanalyzer, and library amounts were quantified using the Qubit fluorimeter. All ChIP-Seq was performed on biological triplicates.

Sequencing and Data Analysis.

All sequencing was performed by the Millard and Muriel Jacobs Genetics and Genomics Laboratory at the California Institute of Technology using the Illumina HiSEq 2500 platform; 10–15 million reads of 50 or 75 bp each were collected for each sample. Base-calling and demultiplexing were performed by the Illumina HiSeq Control Software (HCS) (version 2.0). The resulting FASTQ files were concatenated into one file per sample and filtered and trimmed by quality score per base using the Trimmomatic software package with the following parameters: LEADING:27 TRAILING:27 SLIDINGWINDOW:4:20 MINLEN:35 (61). Surviving reads were mapped to the P. aeruginosa UCBPP-PA14 genome sequence (gi|116048575|ref|NC_008463.1) using the Bowtie package with the -n 2 and -best arguments (62). Specifically for assessing ChIP signal at tRNA genes, Bowtie was run with the -n 2 and -m 1 arguments to require reads to be uniquely mapped to be reported. Mapped reads were sorted, indexed, and converted to binary format using the SAMtools package (63). Reads per 100 bp, gene, or transcriptional unit (TU) were calculated using the easyRNASeq package from the Bioconductor project in R (64). The general feature format (.gff) file describing the location of genes was generated using the bp_genbank2gff3.pl script from the Bioperl project and the GenBank file for the Ref-Seq accession no. NC_008463.1. The .gff file was modified to additionally include small noncoding RNAs and novel ORFs detected by deep sequencing of the UCBPP-PA14 strain of P. aeruginosa (4) and to consistently name genes by their locus tags rather than a mixture of locus tags and gene names. The .gff file describing the locations of transcriptional units was derived from the table of transcriptional units published by Wurtzel et al. (4) and uses the start of the first coding sequence and the end of the last coding sequence in each operon as the operon boundaries. Average ratios and significance of differential expression or ChIP association between different genotypes or pulldowns were calculated using the Degust web server hosted by the Victorian Bioinformatics Consortium. The Degust project uses the voom and limma packages in R to perform calculations (65).

For viewing ChIP data across genomic loci, the counts per 100 bp for each sample were normalized to the size of the library by converting counts to RPKM and then further scaled based on the values observed for low- and high-signal regions. This method was adapted from the one described by Mooney et al. (27). The baseline value for each sample was defined as the average RPKM value for the 25 transcriptional units at least 1 kb in length that had the lowest signal in the RpoA pulldown from the HA-SutA strain. These transcriptional units were verified to have among the lowest RPKM values from the RNA-Seq data as well and were assumed to be essentially not transcribed under the conditions of the experiment. The maximum value for each sample was defined as the average RPKM value for the top 10 peaks associated with protein-coding genes for that type of pulldown. A peak was defined as two consecutive 100-bp regions that fell among the top one hundred 100-bp regions. Whereas some peak regions were the same for both the HA-SutA and the RpoA pulldown, some were distinct. See Dataset S4 for the regions and values used.

To scale the RPKM data, the baseline value was subtracted from each 100-bp RPKM value, and the result was divided by the maximum value, such that nearly all scaled values fall between 0 and 1. The biological triplicates for each pulldown were averaged. The MochiView software package (66) was used to smooth the scaled 100-bp values over a 300-bp rolling window, and then the coordinates of regions with scaled values above 0.20 for the HA-SutA pulldown and scaled values above 0.25 for either RpoA pulldown were extracted. Regions less than 100 bp apart were merged. This set of high ChIP signal regions was then filtered to include only 100-bp regions that also showed a statistically significant enrichment in the HA-SutA pulldown compared with the mock pulldown, which left a total of two thousand fifteen 100-bp regions that were considered “high ChIP”; 230 transcriptional units starting within a high ChIP region were identified. There were 405 genes that were contained within these transcriptional units and were considered the list of high ChIP genes that was compared with the list of up- and down-regulated genes. For the aggregate ChIP plot shown in Fig. 6A, transcriptional units containing ribosomal protein genes were excluded, because these transcriptional units had already been separately considered, and of the remaining, only transcriptional units with start sites defined by Wurtzel et al. (4) were included. See Dataset S6 for the transcriptional unit data that were used.

Functional analysis of genes transcriptionally affected more than twofold was carried out using the COG category designations recorded in the Pseudomonas Genome Database (www.pseudomonas.com) (67). For simplicity, several COG categories were grouped together for each bar in the bar plot. The category designated “unknown” contains COG categories R and S (“General functional prediction only” and “No functional prediction”) in addition to genes that did not have an associated COG. The category designated “maintenance and secondary metabolism” contains COG categories C, I, P, O, and Q (“Energy production and conversion,” “Lipid transport and metabolism,” “Inorganic ion transport and metabolism,” “Post-translational modification, protein turnover, and chaperones,” and “Secondary metabolites biosynthesis, transport, and catabolism”). The category designated “growth and primary metabolism” contains COG categories D, E, F, G, H, J, L, and M (“Cell cycle control, cell division, chromosome partitioning,” “Amino acid transport and metabolism,” “Nucleotide transport and metabolism,” “Carbohydrate transport and metabolism,” “Coenzyme transport and metabolism,” “Translation, ribosomal structure and biogenesis,” “Replication, recombination and repair,” and “Cell wall/membrane/envelope biogenesis”). The category designated “motility, defense, and signaling” contains COG categories N, T, U, and V (“Cell motility,” “Signal transduction mechanisms,” “Intracellular trafficking, secretion, and vesicular transport,” and “Defense mechanisms”). The category designated “transcription and nucleic acid processing” contains COG categories A, B, and K (“RNA processing and modification,” “Chromatin structure and dynamics,” and “Transcription”) (ftp://ftp.ncbi.nih.gov/pub/COG/COG2014/static/lists/homeCOGs.html) (33).

Software Analysis and Data Presentation.

This section describes software packages that were not mentioned above. Data processing and statistical analysis were performed with Python version 2.7.9 with NumPy version 1.9.2, SciPy version 0.15.1, and Pandas version 0.16.1. Data were plotted with Matplotlib version 1.4.3 (68) and Seaborn version 0.5.1. Gel images were analyzed with ImageJ 64-bit version 1.45 (69). Figures were assembled in Adobe Illustrator CS5.

Discussion

Although microbes have spent the majority of their evolutionary history enduring slow-growth conditions, relatively little is known about their physiology in these states. In part, this knowledge gap arises from technical challenges—slow metabolic rates and high phenotypic heterogeneity can lead to increased noise and decreased signal for many biomolecules of interest. However, slow-growth and survival states are of great relevance in many clinical and environmental contexts, and new tools are needed for their study. As illustrated here, the BONCAT method, which enables enrichment of newly synthesized proteins from large preexisting proteomes, is well suited to the exploration of slow-growth modes of microbial life.

We used the BONCAT method to discover a previously unknown RNAP-binding factor, which we have named SutA. We found SutA to be up-regulated posttranscriptionally in various growth-limiting conditions. Through its interaction with RNAP, SutA localizes to many genes throughout the chromosome and elicits broad transcriptional changes. Some of these changes are likely direct effects; for example, SutA associates strongly with loci encoding ribosomal components, and the transcription of these loci is reduced in the absence of SutA. Other changes may be attributable to secondary effects resulting from changes in the pool of free polymerase or from changes in downstream regulation by directly affected genes. Our broad analysis of transcriptional changes suggests that cells expressing SutA prioritize the expression of genes required for survival, and our phenotypic studies show that SutA is important for the establishment of biofilms, the regulation of phenazine production, and transitions to and from growth-limited states.

Understanding the molecular mechanism by which SutA effects these changes will require further study, but our observations suggest some intriguing comparisons to the well-studied regulator DksA. DksA acts with the small molecule alarmone ppGpp during nutritional downshifts to destabilize open promoter complexes, especially at rRNA promoters. This activity reduces rRNA transcription in response to a decreased availability of nucleotides (34). DksA may also influence elongation by helping to prevent the transition of RNAP from a paused to an arrested state (35). Interestingly, SutA appears to affect many of the same genes and phenotypes as DksA but in the opposite direction. Whereas DksA has been shown in both E. coli and P. aeruginosa to repress expression of ribosomal protein and rRNA genes (34, 36, 37), SutA enhances expression of these genes. Both DksA and SutA show high ChIP signal across the coding regions of highly expressed protein-coding genes, including ribosomal protein genes, and a lower signal across the coding regions of the rRNA genes. However, unlike DksA, SutA shows a high peak of ChIP signal at the promoters of rRNA genes, consistent with the observations that SutA enhances rRNA expression, whereas DksA represses rRNA expression (29). Disruptions of dksA or sutA in Pseudomonas species also appear to cause opposing phenotypes: disruption of dksA causes a decrease in PYO production and an increase in biofilm persistence (38, 39), whereas deletion of sutA causes overproduction of PYO and a decrease in biofilm accumulation. Taken together, these observations suggest that a subset of genes, including the rRNA and ribosomal protein genes, are sensitive to some modulation of RNAP activity, and DksA and SutA tend to modulate this activity in opposite ways.

In our BONCAT experiment, we detected new synthesis of DksA in the aerobic exponential growth condition but not in the anaerobic survival condition. This finding is consistent with a previous report that DksA is undetectable by Western blot during stationary phase in P. aeruginosa (36) and suggests that the repression by DksA of rRNA and ribosomal protein gene expression is down-regulated during protracted slow growth. DksA is advantageous in the context of actively growing cells because it protects against “traffic jams” of stalled RNAP that obstruct the completion of DNA replication (40) and allows limited cellular resources to be directed toward expression of genes important for ameliorating the limitations (e.g., amino acid biosynthetic genes) (41). However, for cells that are dividing infrequently or not at all, and that are limited for basic energy resources rather than specific metabolites, these functions may be counterproductive. Instead, the most adaptive response may be to maintain transcription, even at low levels, of core machinery to retain a capacity for cellular maintenance and to allow for a rapid up-regulation of biosynthetic pathways when conditions improve. Our results suggest that SutA contributes to this type of response, and set the stage for future biochemical and structural studies.

Recent reports have described RNAP-binding regulators that broadly affect transcription in different organisms under a range of conditions, suggesting that this is an important and diverse mode of regulation. For example, the nonessential δ subunit of B. subtilis RNAP (42) and the recently discovered AtfA from Acinetobacter spp. (43) are both small proteins that, like SutA, contain highly acidic domains and broadly impact transcription but, unlike SutA, are expressed during exponential phase. CarD is a mycobacterial protein that has recently been crystallized in a complex with RNAP; unlike SutA, CarD is essential and appears to localize primarily to promoter regions, but like SutA it broadly serves to stimulate transcription. One characteristic of all of these proteins is that they lack homologs in E. coli, the model organism from which much of our knowledge of bacterial transcriptional regulatory mechanisms has been derived. Each protein has a different phylogenetic distribution; SutA is found only in selected families of the Alteromonadales and Pseudomonadales orders of Gammaproteobacteria. This growing body of work, including the results described here, demonstrates that regulation of RNAP is diverse, and even in well-studied, clinically important pathogens, basic regulatory mechanisms governing slow growth remain to be discovered.

Experimental Procedures

For detailed descriptions of all experimental procedures, see SI Experimental Procedures. The strains and plasmids used are listed in Table S1.

Strains and Growth Conditions.

Rich medium was LB broth. Minimal medium was phosphate-buffered and contained 40 mM carbon source (10). In experiments involving P_ara:sutA, all cultures were grown in the presence of 20–25 mM arabinose. Where necessary, plasmids were maintained with the appropriate antibiotics. Aerobic growth was carried out with shaking at 37 °C. Anaerobic survival was carried out in Balch tubes in an anaerobic chamber (Coy) without shaking at 37 °C. Growth for colony morphology assays was carried out at room temperature. Genetic manipulations used standard procedures.

Biofilm Measurements.

Crystal violet and colony morphology assays were carried out as previously described (44, 45).

Phenazine Measurements.

Phenazine concentrations in culture supernatants were determined by HPLC as previously described (23) or estimated by measuring absorbance at 312 nm.

Individual Gene Expression Measurements.

Per-cell GFP measurements were made using the Accuri c6 flow cytometer, and RNA measurements were made by qPCR. Primers are listed in Table S2.

Proteomics.

BONCAT labeling, chemistry, and enrichment were performed as previously described (46). Label-free quantitation was used for the initial screen. Relative protein abundances for IPs were quantified via dimethyl labeling (47).

IP and ChIP.

Cultures of ΔsutA pMQ72 or ΔsutA pMQ72-HA-SutA were grown to late-exponential phase in pyruvate minimal medium containing 20 mM arabinose and 50 µg/mL gentamicin. HA-SutA or RpoA was purified with anti-HA agarose beads (Thermo Fisher Scientific) or protein A/G beads (Santa Cruz Biotechnology) and an anti-RpoA antibody, respectively. Fractions were saved for Western blot analysis, and eluents were analyzed via LC-MS/MS. For ChIP, cultures were grown as above, cross-linked with 1% formaldehyde, and lysed via sonication, and either HA-SutA or RpoA was immunoprecipitated. Protein digestion and DNA cleanup were performed as previously described (48).

Sequencing Library Preparation and Sequencing.

For RNA-Seq, cultures of wild-type, ΔsutA, and P_ara:sutA strains were grown to late-exponential phase in pyruvate minimal medium containing 25 mM arabinose. Total RNA was extracted using the RNeasy Mini Kit (Qiagen), and rRNA was depleted using the Magnetic Gram Negative Bacteria RiboZero Kit (Epicentre). For ChIP-Seq, immunoprecipitated DNA was further fragmented using DS Fragmentase (NEB). Both types of libraries were prepared using the relevant Library Prep kits for Illumina (NEB). Sequencing was performed to a depth of 10–15 million reads per sample on an Illumina HiSeq2500 machine, and data analysis was performed using standard open source software, or as described in more detail in SI Experimental Procedures. Sequencing was performed on biological triplicates.

Supplementary Material

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Acknowledgments

We thank Geoff Smith and Roxana Eggleston-Rangel for technical assistance with liquid chromatography–tandem mass spectrometry and Dr. Igor Antoshechkin for assistance with sequencing. We thank Dr. Olaf Schneewind for his gift of the anti-RpoA antibody. We appreciate constructive feedback on the manuscript from members of the D.K.N. and D.A.T. laboratories and Richard Gourse, as well as helpful comments from the editor and reviewers. This work was supported by NIH Grants 5R01HL117328-03 (to D.K.N.) and 1S10RR029594-01A1 (to S.H.), the Institute for Collaborative Biotechnologies through US Army Research Office Grant W911NF-09-0001 (to D.A.T.), Howard Hughes Medical Institute (HHMI), and the Millard and Muriel Jacobs Genetics and Genomics Laboratory at California Institute of Technology (Caltech). The Proteome Exploration Laboratory (M.J.S., A.M., and S.H.) was supported by Gordon and Betty Moore Foundation Grant GBMF775 and by the Beckman Institute at Caltech. D.K.N. is an HHMI Investigator.

Footnotes

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

Data deposition: The data reported in this paper have been deposited in the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. GSE66181).

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1514412113/-/DCSupplemental.

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Supplementary Materials

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PERMALINK

SutA is a bacterial transcription factor expressed during slow growth in Pseudomonas aeruginosa

Brett M Babin

Megan Bergkessel

Michael J Sweredoski

Annie Moradian

Sonja Hess

Dianne K Newman

David A Tirrell

Series information

Significance

Abstract

Results

BONCAT Enables Enrichment and Identification of Proteins Synthesized at Low Rates During Anaerobic Survival.

Fig. S1.

Fig. 1.

Fig. S2.

SutA Promotes Biofilm Formation, Inhibits Pyocyanin Production, and Confers a Fitness Advantage Under Fluctuating Conditions.

Fig. 2.

SutA Up-Regulation During Slow Growth Is Posttranscriptional.

Fig. 3.

SutA Interacts with RNA Polymerase.

Fig. 4.

Fig. S3.

SutA Associates with Genomic Loci and Enhances Transcription of Ribosomal Genes.

Fig. S4.

Fig. 5.

SutA Localizes to Many Nonribosomal Genes and Has Broad Effects on Gene Expression.

Fig. 6.

SI Experimental Procedures

Strain Construction.

Table S1.

Media and Growth Conditions.

BONCAT Labeling and Enrichment.

Protein Digestion, Mass Spectrometry, and Data Analysis.

Colony Morphology Assay.

Crystal Violet Assay.

Phenazine Measurements.

Competition Assay.

GFP Reporter Protein Measurement.

RNA Extraction.

qPCR.

Table S2.

Co-IP.

RNA Seq Library Preparation.

ChIP.

ChIP Seq Library Preparation.

Sequencing and Data Analysis.

Software Analysis and Data Presentation.

Discussion

Experimental Procedures

Strains and Growth Conditions.

Biofilm Measurements.

Phenazine Measurements.

Individual Gene Expression Measurements.

Proteomics.

IP and ChIP.

Sequencing Library Preparation and Sequencing.

Supplementary Material

Acknowledgments

Footnotes

References

Associated Data

Supplementary Materials

ACTIONS

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