CGA010 Proteome Implicates Extracytoplasmic Function Sigma Factor

Apr 8, 2015 - Department of Biological Sciences, University of North Texas, Denton, Texas 76203-5017, ... Center for Biosafety and Biosecurity Departm...
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Rhodopseudomonas palustris CGA010 Proteome Implicates Extracytoplasmic Function Sigma Factor in Stress Response Michael S. Allen,†,§,∥ Gregory B. Hurst,‡ Tse-Yuan S. Lu,† Leslie M. Perry,§ Chongle Pan,‡ Patricia K. Lankford,† and Dale A. Pelletier*,† †

Biosciences Division and ‡Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6123, United States § Department of Biological Sciences, University of North Texas, Denton, Texas 76203-5017, United States ∥ Center for Biosafety and Biosecurity Department of Molecular and Medical Genetics, University of North Texas Health Science Center, Fort Worth, Texas 76107, United States S Supporting Information *

ABSTRACT: Rhodopseudomonas palustris encodes 16 extracytoplasmic function (ECF) σ factors. To begin to investigate the regulatory network of one of these ECF σ factors, the whole proteome of R. palustris CGA010 was quantitatively analyzed by tandem mass spectrometry from cultures episomally expressing the ECF σRPA4225 (ecf T) versus a WT control. Among the proteins with the greatest increase in abundance were catalase KatE, trehalose synthase, a DPS-like protein, and several regulatory proteins. Alignment of the cognate promoter regions driving expression of several upregulated proteins suggested a conserved binding motif in the −35 and −10 regions with the consensus sequence GGAAC-18N-TT. Additionally, the putative anti-σ factor RPA4224, whose gene is contained in the same predicted operon as RPA4225, was identified as interacting directly with the predicted response regulator RPA4223 by mass spectrometry of affinity-isolated protein complexes. Furthermore, another gene (RPA4226) coding for a protein that contains a cytoplasmic histidine kinase domain is located immediately upstream of RPA4225. The genomic organization of orthologs for these four genes is conserved in several other strains of R. palustris as well as in closely related α-Proteobacteria. Taken together, these data suggest that ECF σRPA4225 and the three additional genes make up a sigma factor mimicry system in R. palustris. KEYWORDS: ECF σ factor, stress, gene regulation, Rhodopseudomonas palustris, proteomics



INTRODUCTION Within their respective niches, bacteria are subjected to extreme changes in environmental conditions such as fluctuations in temperature, light, pH, nutrient levels, or osmolarity. To deal with these changes, bacteria have evolved specific mechanisms to detect and respond to their environment. These cellular responses may involve numerous genes that are often coordinately expressed via alternative σ factors. One class of these is the extracytoplasmic function (ECF) σ factors.1 These proteins are evolutionarily distant but related to the σ70 proteins.2 The Group IV ECF σ factors differ from Group I (i.e., σ70) by loss of portions of the first and third of the four primary subdomains and the possession of an altered −35 site binding mechanism.3 ECF σ factors are involved in the regulation of genes involved in a number of cellular responses including periplasmic stress, iron transport, and pathogenesis (for further reviews, see refs 3a and 4). Additionally, ECF σ factors are often associated with and regulated by an anti-σ factor (reviewed in ref 5). Members of the α-Proteobacteria appear to lack the canonical stationary phase σ factor σS.6 An alternative ECF σ © 2015 American Chemical Society

factor involved in the general stress response in an α-Proteobacterium was first described in Sinorhizobium meliloti.7 This protein, RpoE2, was found to be responsible for regulating the expression of 44 genes, including the catalase katC, a heat shock σ factor, and an additional ECF σ factor. Contained within the rpoE2 operon is a second gene, smc01505. The latter could not be deleted in the host when rpoE2 was present, but when expressed in trans, smc01505 was found to act as a negative regulator of rpoE2-dependent gene expression, supporting its assignment as an anti-σ factor. The genomic organization of these two genes along with a putative response regulator, smc01504, located immediately upstream of the anti-σ factor smc01505 and divergently transcribed, was found to be highly conserved among α-Proteobacteria. An orthologous regulatory system was also reported in Methylobacterium extorquens and Bradyrhizobium japonicum.8 In both organisms, the response regulator PhyR was found to act as an anti-anti-σ-factor, binding the anti-σ factor NepR via a Received: December 10, 2014 Published: April 8, 2015 2158

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Journal of Proteome Research σ70-like domain present in its N-terminus.6 This novel regulatory mechanism was named sigma factor mimicry and has since been increasingly found among α-Proteobacteria. In several species, a histidine kinase is encoded immediately upstream of the σ factor.9 Work in Caulobacter crescentus indicated that this protein, PhyK, was responsible for phosphorylation of the response regulator PhyR and activation of the σ factor.10 The PhyK-PhyR-NepR-σT system in C. crescentus has been shown to regulate osmotic and oxidative stresses and carbon limitation.11 Similarly, the orthologous system in Brucella abortus provides protection during oxidative and acid stress during exponential growth.9b The emerging body of work suggests that this system is widespread among the α-Proteobacteria and is involved with regulation of a general stress response.6−8,10−12 In this work, we explored the effect of overexpression of rpa4225 encoding the σT-ortholog of the σ mimicry systems described above in the environmental bacterium Rhodopseudomonas palustris CGA010. This purple, nonsulfur bacterium, also belonging to the α-Proteobacteria, is both facultatively photosynthetic and diazotrophic.13 Hydrogen production during nitrogen fixation and anaerobic photosynthetic growth have been studied for its potential as a fuel source.14 Additionally, R. palustris is capable of the anaerobic degradation of a variety of lignin-derived aromatic acids.15 Little is known about the stress response in R. palustris. Its large (∼5.5 Mb) genome contains genes for 19 different RNA polymerase σ factors, including 16 classified as Type IV ECF σ factors.4a,13 While investigating protein expression under different metabolic conditions by shotgun proteomics, it was found that the putative ECF σRPA4225, hereafter referred to as EcfT, was upregulated during growth on benzoate and under stationary phase conditions.16 McKinlay et al. similarly reported an increase in transcription of rpa4223−4226 during nongrowth periods of nitrogen starvation.17 Its high abundance relative to other ECF-type σ factors suggested an important function in global gene regulation in this organism. To further investigate the role of EcfT in gene regulation, its gene was cloned into a broad host range plasmid and constitutively expressed in R. palustris CGA010. The total protein complement of these cells was analyzed by quantitative shotgun proteomics. Results indicated that EcfT positively regulates the expression of a number of genes involved in the oxidative stress response and is differentially regulated as a result of pH stress. Quantitative RT-PCR data confirmed the presence of ecf T mRNA during relatively high levels of oxidative and pH stress compared to nonstressed R. palustris cells. Two genes encoding an alternative catalase (katE) and an unknown protein displaying the putative ecf T promoter consensus sequence were also found to be highly upregulated to the same stresses. Additionally, the σ-factor transcript was found to be in greater abundance than other ECF σ factors at high levels of these stresses. Taken together these data indicate that σT of R. palustris is a regulator of the general stress response and is orthologous to systems described in other α-Proteobacteria.



2 g/L 4-aminobenzoic acid, 0.1 M sodium thiosulfate, and concentrated base minerals per liter.18 For all experiments, R. palustris CGA010, a derivative of R. palustris CGA009 that contains a 4 bp insertion in the hupV gene that repaired a frameshift mutation, was used.19 The genes of interest were amplified from R. palustris genomic DNA by PCR using primers containing the Gateway-compatible att sites (Invitrogen). Primer sequences are listed in Table 1. PCR products were directionally inserted into the pDONR221 entry vector using the BP recombination reaction with Clonase enzyme (Invitrogen), verified by sequencing, and subsequently transferred to the final destination vector pBBR5DEST4220 by LR recombination using the Invitrogen Gateway System enzymes and protocols. Escherichia coli strains DB3.1 and DH5α were used for cloning and plasmid propagation. LB media was used for E. coli growth. The vectors were purified and transferred to R. palustris by electroporation as previously described.20 Growth and Culture Conditions for Proteomics

A colony each of R. palustris containing pBBR5DEST42RPA4225 or pBBR1MCS5 (the empty parent vector, used as a control)21 was inoculated into 25 mL of PMS-10 containing 100 μg/mL gentamycin in anaerobic screw cap tubes and incubated statically in the light at 30 °C. After 3 days, cultures were pelleted and resuspended in 25 mL of PMS-10 medium. Five milliliters of these suspensions was subsequently transferred to serum bottles containing 100 mL of PMS-10 with either (14NH4)2SO4 for experimental culture or (15NH4)2SO4 (98 atom % excess, Sigma-Aldrich) for 15N-labeled control culture as the sole fixed nitrogen source. Cultures were incubated anaerobically in the light at 30 °C. Subsequently, 40 mL from these cultures was used to inoculate duplicate 1.5 L cultures in 2 L bottles containing the appropriate 14N or 15N ammonium sulfate isotopologue, and cultures were grown as described above with gentle stirring to maintain mixing and exposure of cultures to the light. Cultures at an OD λ660 ∼0.4 were harvested by centrifugation. Pellets were resuspended in 15 mL of PMS-10, and equivalent OD units from duplicate samples were mixed, pelleted, and frozen at −80 °C for subsequent quantitative proteomic analysis. Western Blot Analysis

To test for protein expression, individual colonies of expression strains were grown in appropriate liquid media as described above to mid log phase, and cells were harvested by centrifugation. Cell pellets were lysed in phosphate buffered saline (PBS) with 1 μL/mL BugBuster plus benzonase nuclease (Novagen, Madison, WI), 10 μg/mL lysozyme, 100 μg/mL phenylmethylsulfonylfluoride (PMSF), and 10 μg/mL leupeptin. After mixing gently for 20 min at room temperature, lysates were centrifuged at 16 000g for 15 min at 4 °C. Total protein was determined by the method of Lowry.22 Cleared lysates were separated on a 4−20% Precision Protein acrylamide gel (Pierce, Rockville, IL) and transferred to PVDF gel transfer stacks using the Invitrogen iBlot system (Invitrogen, Carlsbad, CA). Membranes were washed overnight at 4 °C in PBS containing 0.1% Tween 20 (PBS-Tween). Nonspecific binding was blocked by incubation of the membrane with gentle rocking for 0.5 h in 5% nonfat milk powder and 5% bovine serum in PBSTween (Blotto). The membrane was then incubated for 2 h with rocking at room temperature with the primary antibody, mouse anti-V5 epitope (Invitrogen), at 1:5000 dilution in Blotto. After extensive washing in PBS-Tween, the membrane was incubated with rocking for 1.5 h in horseradish peroxidase-conjugated

EXPERIMENTAL PROCEDURES

Media, Strains, and Cloning

Photoheterotrophic medium PMS-10 contains 20 mL each of 0.5 M Na2HPO4 and 0.5 M KH2PO4, 8 mL each of 10% (NH4)2SO4 and 1 M sodium succinate, and 0.8 mL each of 2159

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Journal of Proteome Research Table 1. List of Oligonucleotide Primers Used in This Work primers for qPCR

primer name

RPA0285 RPA0285 RPA0994 RPA0994 RPA1481 RPA1481 RPA2420 RPA2420 RPA2977 NRD RPA2977 NRD RPA3310 KatE RPA3310 KatE RPA3568 RPA3568 RPA3726 RPA3726 RPA3943 RPA3943 RPA4224 RPA4224 RPA4467 SoxY2 RPA4467 SoxY2 Primers for Cloning RPA4223

qRT qRT qRT qRT qRT qRT qRT qRT qRT qRT qRT qRT qRT qRT qRT qRT qRT qRT qRT qRT qRT qRT

RPA4224 RPA4225

forward reverse forward reverse forward reverse forward reverse forward reverse forward reverse forward reverse forward reverse forward reverse forward reverse forward reverse

RPA4223-for RPA4223-rev RPA4224-for RPA4224-rev RPA4225-for RPA4225-rev

sequence gaataccctggcaccgaat acgatcgaccgaatggatag ggagttcaactgcctgttctg caatccttacgcgtcagtcc cctggtgtttaccgacatcc cgtccagaggtggtgatgat atctgcagcgtctcaaaacc ggcgaaggtcatgatgtagg aatatgtcgacgccttcacc cggaacacgtagtcgaggat gatcaagtttcccgacctga aggctgatgaagtcccagaa atgctgatcgatagcgaagg actgcagcttgtcgaaggtt gcaaggagatcatggacgag cgtagcgggagatctcgtag cgaagtcgaggacaagaagg atgataccaggctgccgtag atggggaaaacattggatca ctggatttcggcgttaagac atggcgacaagaagttcgac ctttcgacttcgacggtgat gggg aca agt ttg tac aaa aaa gca ggc ttc gaa gga gat agaATGTCGAGATCACAGGTTGTTG ggg gac cac ttt gta caa gaa agc tgg gtcGGATGCGACGGCGCTGGG gggg aca agt ttg tac aaa aaa gca ggc ttc gaa gga gat agaATGCAAGAATTCAAGGCGCAAAC ggg gac cac ttt gta caa gaa agc tgg gtcTTCCCTCCCCTCGGTGGTAG gggg aca agt ttg tac aaa aaa gca ggc ttc gaa gga gat agaATGCCTCTCACCGATTCCCTT ggg gac cac ttt gta caa gaa agc tgg gtcGCCGTTACCGCCGATCACG

goat anti-mouse IgG (Bio-Rad, Hercules, CA) at 1:1500 dilution. After washing, color was developed by the addition of ImmunoPure metal enhanced DAB substrate (Pierce, Rockford, IL).

upstream of a 3 to 4 cm length of strong cation exchange phase (SCX; Luna, 5 μm particle, 100 Å pore size [Phenomenex]). The back column was attached via a filter union (Upchurch) to the front column, a 100 μm i.d. resolving column/nanospray tip (Picofrit, New Objective) packed with a ∼15 cm length of C18 reverse-phase resin (Jupiter, 5 μm particles, 300 Å pore size [Phenomenex]). The assembled columns were attached to the flow from an HPLC pump (Ultimate, LCPackings/Dionex, Sunnyvale, CA). A total flow of 150 μL/min from the pump was split to provide a flow through the column of ∼300 nL/min. Twelve HPLC cycles were performed per sample. The first cycle consisted of a reverse-phase gradient from 100% solvent A (95% H2O, 5% acetonitrile, 0.1% formic acid) to 50% solvent B (30% H2O, 70% acetonitrile, 0.1% formic acid) over 45 min followed by a ramp to 100% solvent B over 10 min. In cycles 2−11, 100% solvent A was applied for 5 min, followed by a 2 min salt step gradient of 400 mM ammonium acetate (10, 15, 20, 25, 30, 35, 40, 45, 50, and 60%, respectively, in solvent A), followed by 3 min of 100% solvent A and then a reverse-phase gradient (100% solvent A to 50% solvent B over 110 min). In cycle 12, 100% solvent A was applied for 5 min, followed by a 10 min salt step gradient of 400 mM ammonium acetate (100%), followed by 9 min of 100% solvent A and then a reverse-phase gradient (100% solvent A to 20% solvent B over 15 min and then to 100% solvent B over 80 min). The back column was removed, and the front column was subjected to a final wash and equilibration step, performed by ramping from 100% solvent A to 100% solvent B over 14 min, ramping to 100% solvent A over 1 min, and holding at 100% solvent A for 15 min. A single front column was used for all measurements,

Protein−Protein Interactions

For detection of protein−protein interactions in R. palustris cells, fusion proteins bearing a carboxy-terminal V5 epitope6×Histidine affinity tag were expressed in R. palustris from pBBR5DEST42 vector, and protein−protein interactions in which these baits participate were determined by mass spectrometric identification of proteins obtained from affinity purification as described previously.20 Of particular interest here were RPA4223 (encoding a response regulator receiver CheY-like protein), RPA4224 (encoding an unknown protein), and EcfT (all adjacently located on the genome and hence possibly interacting) as well as RPA3226 (encoding RpoA) and RPA3267 (encoding RpoC), which are components of the RNA polymerase holoenzyme and expected to interact with σ factors. LC−MS/MS

For quantitative proteome analysis, proteins were identified from digests using MudPIT,23 with two-dimensional HPLC interfaced with tandem mass spectrometry (LC−MS/MS). Triplicate MudPIT analyses (technical replicates) were performed for each of the two duplicate cultures (biological replicates). Aliquots of 50 μL of protein digest, containing ∼300 μg of protein, were loaded via a pressure cell (New Objective) onto a back column consisting of a 100 μm i.d. fused silica capillary column containing a 3 to 4 cm length of C18 reverse-phase (RP) resin (Jupiter, 5 μm particle, 300 Å pore size [Phenomenex]) 2160

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Journal of Proteome Research whereas a new back column was prepared for each LC−MS/ MS run. The LC eluent was interfaced with an LTQ mass spectrometer (ThermoFinnigan, San Jose, CA) via a nanospray source. Acquisition of tandem mass spectra was triggered in a data-dependent manner, with collision-activated dissociation (CAD) of three parent ions selected from the most intense ions in each full scan mass spectrum. Parent ions selected for tandem MS analysis more than once (i.e., repeat count = 1) within 1 min were placed on an exclusion list for 3 min, during which time they were not subjected to CAD. The isolation window for parent ions was 3 m/z units wide, and the normalized collision energy for parent ion activation was 35%. Four microscans were averaged per full-scan mass spectrum, and two microscans were averaged per tandem mass spectrum. Peptides were identified using the Sequest program24 to compare experimental tandem mass spectra with predicted fragmentation patterns of peptides obtained from a protein database. This database contained sequences for 4833 R. palustris CGA009 proteins, obtained from the ORNL microbial genome annotation pipeline at http://genome.ornl.gov/microbial/rpal/,13 with an additional 44 common contaminants and standard proteins. Two separate Sequest searches were performed for each MudPIT data set; in the first search, the amino acid masses specified in the parameters file (sequest.params) corresponded to the 14N isotopologues, whereas the second search specified 15N amino acid masses.25 14N and 15N Sequest search results from the three technical replicates of a biological duplicate were combined, filtered, and organized into protein identifications using DTASelect.26 Peptide identifications were retained for Sequest results of XCorr ≥ 1.8 (z = 1), XCorr ≥ 2.5 (z = 2), or XCorr ≥ 3.5 (z = 3) and DeltaCN ≥ 0.08. Protein identifications required a minimum of two peptide identifications or identification of a single peptide in at least two charge states. The ProRata program25 was used to estimate, from full-scan mass spectra, abundance ratios for 14N forms of proteins (from the culture episomally expressing RPA4225) to their respective 15 N counterparts (from the control culture with the empty plasmid) in each of the biological duplicates. Default ProRata parameters were used.25b Protein abundance ratios obtained from the two biological duplicates were then combined. Only proteins consistently quantified in both biological duplicates were retained for subsequent data analysis.

Figure 1. Western blot analysis of EcfT fusion protein expression in R. palustris cell lysates. Lane 1, MW standards; lanes 2 and 5, R. palustris + empty pBBR1MCS5 plasmid as negative controls; lanes 3 and 6, R. palustris + pBBR5DEST-RPA4225/42 (expression vector for EcfT); lane 4, blank. Each lane contains ∼100 μg of total cell lysate. The blot was probed with anti-V5 antibody.

The supernatant was decanted, and 1.8 mL of lysis buffer with β-mercaptoethanol was added to resuspend the cells. The cells were also mechanically disrupted using the FastPrep bead beater (MP Bio) with 0.1 mm glass beads at 6 m/s for 20 s. The cells were placed on ice, and then total RNA was extracted using the RNeasy minikit (Qiagen) following the manufacturer’s instructions for bacteria. An on-column DNase I digestion step was extended to 30 min. Total RNA was converted to cDNA using random hexamers and reverse transcriptase from the SuperScript II set (Bio-Rad), as per the manufacturer’s instructions. Samples from at least triplicate cultures were diluted to the same DNA concentration, and duplicate or triplicate samples (technical replicates) were subsequently analyzed by quantitative PCR on a BioRad iCycler using the iQ SYBR Green supermix (BioRad). Amplification conditions consisted of 40 cycles of 95 °C denaturation (10 s) and optimal annealing/extension for each primer (30 s). Efficiency of amplification was calculated for each primer set using standard curves of successive 10−1 dilutions of R. palustris genomic DNA. Primers and sequences are shown in Table 1. The housekeeping gene, rpa0994 (encoding GAPDH), was used as an endogenous reference to normalize cDNA concentration and as an interassay control. Relative quantifications are based on the ΔΔCT method using untreated samples as the calibrator. Fold-change was calculated based on efficiency of amplification according to Pfaffl.27 H2O2 Treatment

R. palustris cultures were grown as previously described for qPCR. A sufficient volume of 3% H2O2 was added to each culture tube to yield a final concentration of 2 or 10 mM. These and untreated sample were incubated for 30 min. Each condition was performed in at least triplicate.

Sequence Alignment and Analysis

Approximately 150 bp of the predicted untranslated region upstream of the translational start site (5′ UTR) for each gene of interest, or for the lead gene in a predicted operon when appropriate, was used for alignment. The 5′ UTRs were first manually aligned relative to their translational start and analyzed using AlignX of the Vector NTi Advance 10 software suite (Invitrogen). New sequences were iteratively added, and the alignments were repeated. The whole genome sequence of R. palustris CGA009 was manually searched for the determined consensus sequence GGAAC-18N-TT using Artemis, release 5 (www.sanger.ac.uk/Software/Artemis/).

Genomic Organization

The relative organization of genes RPA4223, -4, -5, and -6 was compared among α-Proteobacteria with Genome ProtMap (http://www.ncbi.nlm.nih.gov/sutils/protmap.cgi?) using either the predicted Pfam or Tigrfam protein family designation for each of the four proteins and screening for conservation of organization of the entire set. Individual genes in similar operons from other bacteria were then compared by translated BLAST against the whole translated genome of R. palustris CGA009 to gauge similarity.

Quantitative PCR



Triplicate cultures of wild-type R. palustris CGA010 were grown to midexponential phase either aerobically (oxidative stress) or anaerobically (pH) in 25 mL screw-top tubes in the light at 30 °C. At OD λ660 ∼ 0.38, the cultures were either exposed to stress or not exposed (light only) for 30 min, and then at least 10 mL was centrifuged to pellet the cells.

RESULTS

Proteomics

To identify proteins whose genes were affected by episomal expression of ecf T, we cloned the corresponding gene into a 2161

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Journal of Proteome Research

Table 2. Proteins Increased in Abundance in Cells Episomally Expressing ECF σRPA4225 as Determined by Proteomicsa gene RPA3310 RPA3726 RPA4580 RPA0566 RPA2033 RPA3308 RPA3568 RPA3943 RPA3309 RPA1481 RPA4418 RPA1500 RPA4217 RPA3510 RPA0651 RPA3642 RPA0474 RPA3647 RPA4225 RPA4456 RPA3702 RPA1274 RPA2973 RPA3643 RPA4441 RPA0653 RPA2061 RPA0537 RPA3117 RPA3577 RPA4224 RPA1009 RPA1205 RPA1668 RPA0274 RPA2432 RPA2433 RPA2560 RPA3011 RPA3054 RPA3096 RPA3573 RPA3635 RPA4232 RPA0938 RPA1747 RPA2743 RPA3088 RPA3092 RPA3929 RPA4223 RPA4498 RPA4645 RPA4821

description katE catalase heme-type conserved unknown protein Uncharacterized protein family conserved unknown protein possible AtsE YciF, putative structural proteins conserved unknown protein conserved hypothetical protein conserved unknown protein response regulator receiver conserved unknown protein unknown protein conserved unknown protein conserved unknown protein aliA cyclohexanecarboxylate-CoA ligase putative alpha-amylase conserved unknown protein putative glycosyl hydrolase RNA polymerase ECF-type sigma factor unknown protein metH methionine synthase possible Dps protein family starvation-inducible DNA-binding protein hypothetical protein putative trehalose synthase corA putative Mg transport badI 2-ketocyclohexanecarboxyl-CoA hydrolase nosZ nitrous-oxide reductase precursor coenzyme A transferase pyrB aspartate carbamoyltransferase thiC thiamin biosynthesis protein thiC unknown protein possible cytochrome putative alcohol dehydrogenase bchE Mg-protoporphyrin IX cyclase gln K2 nitrogen regulatory protein P-II putative signal transduction histidine kinase with response regulator receiver domain response regulator receiver (CheY-like protein) aapJ-1 ABC transporter, periplasmic amino acid binding protein unknown protein possible transcriptional regulator, Crp/Fnr family conserved unknown protein thiO thiamine biosynthesis oxidoreductase gnd 6-phosphogluconate dehydrogenase hypothetical protein conserved unknown protein conserved hypothetical protein bacterial regulatory protein, MerR family conserved hypothetical protein putative oxidoreductase conserved unknown protein response regulator receiver with σ70-like unknown domain trpG anthranilate synthase cbbF fructose-1,6-bisphosphatase putative 5′-methylthioadenosine phosphorylase

log2 ratio

lower CI

upper CI

putative function

4.1 3.8 3.7 2.9 2.8 2.8 2.7 2.5 2.3 2.2 2.2 1.8 1.8 1.7 1.6 1.6 1.5 1.5 1.3 1.3 1.2 1.1 1.1 1.1 1.1 1 1 0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.7 0.7

3.8 3.3 3.1 2.5 1.9 2.4 2.2 2 2 1.9 1.8 1.5 1.3 1.6 1 1.1 0.8 1 0.7 0.8 0.8 0.8 0.4 0.6 0.4 0.6 0.6 0.5 0.3 0.5 0.3 0.5 0.5 0.5 0.2 0.4

4.4 4.2 5.2 3.3 3.7 3.3 3.2 2.9 2.5 2.6 2.6 2.2 2.2 1.9 2.2 2.1 2.2 2 2 2.2 1.5 1.5 1.7 1.6 1.8 1.4 1.4 1.4 1.6 1.4 1.6 1.2 1.1 1.2 1.1 1

stress YciF-like unknown unknown host cell attachment YciF-like PRC-barrel domain YciF-like OM-like protein regulation stress unknown stress unique to Brady-Rpal carbon metabolism osmotic stress unknown osmotic stress regulation unique to CGA009 and TIE-1 stress stress unknown osmotic stress Mg uptake carbon metabolism energy metabolism carbon metabolism pyrimidine biosynthesis pyrimidine biosynthesis regulation energy metabolism unknown Bch biosynthesis regulation regulation

0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6

0.3 0.5 0.4 0.2 0.3 0.3 0.4 0.2 0.2 0.2 0 0 0.1 0.4 0.4 0.2 0.4 0.2

1.2 1 1.1 1.2 1.1 1.1 1 1.2 0.9 1 1.3 1.2 1 0.8 0.8 1 0.8 1

regulation amino acid pool adjustment? unknown regulation unknown nucleotide biosynthesis carbon metabolism unknown unknown unknown regulation unknown unknown unknown regulation amino acid biosynthesis carbon metabolism nucleotide metabolism

a

CI, confidence interval. Note that upstream DNA sequences from genes listed in bold were used to generate the multiple sequence alignment in Figure 3. Table contains 54 proteins that have log2 ratio ≥ 0.6 and confidence interval that does not cross zero, and 26 of the genes encoding these proteins contain the conserved motif GAAC-N18-TT either in the promoter region of this gene or upstream gene in a potential operon with the identified gene product. 2162

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Table 3. Proteins Decreased in Abundance in Cells Episomally Expressing ECF σRPA4225 as Determined by Proteomicsa gene RPA0285 RPA0407 RPA2977 RPA2126 RPA4424 RPA0994 RPA2094 RPA2096 RPA2873 RPA4460 RPA1424 RPA2125 RPA2311 RPA2377 RPA3985 RPA0483 RPA0955 RPA1006 RPA1423 RPA2090 RPA2097 RPA2123 RPA4179 RPA1609 RPA1732 RPA2105 RPA2488 RPA3217 RPA3472 RPA3485 RPA4822 RPA0453 RPA0580 RPA1131 RPA1725 RPA1929 RPA2083 RPA2084 RPA2978 RPA3725 RPA3798 RPA4403 RPA4433 RPA0207 RPA0280 RPA0529 RPA1007 RPA1043 RPA1655 RPA1687 RPA1690 RPA1763 RPA2086 RPA2120 RPA3756 RPA4076 RPA4336 RPA4465 RPA4467

description protein of unknown function possible TonB-dependent receptor nrd ribonucleotide reductase conserved unknown protein beta-lactamase-like unknown protein cobT1 putative nicotinate-nucleotide-dimethylbenzimidazole phosphoribosyltransferase cobQ2 cobyric acid synthase possible NADH oxidoreductase dhsU1 putative flavocytochrome C sulfide dehydrogenase subunit possible selenocysteine lyase conserved unknown protein hypothetical protein conserved hypothetical protein ADP-L-glycero-D-mannoheptose-6-epimerase unknown protein putative 5′-nucleotidase family protein possible protocatechuate 4,5-dioxygenase small subunit putative membrane protein cobH precorrin isomerase cobF putative cobF protein conserved unknown protein conserved unknown protein mdlC benzoylformate decarboxylase possible cytochrome P450 nonheme chloroperoxidase conserved unknown protein possible branched-chain amino acid transport system substrate-binding protein ilvD1 dihydroxy-acid dehydratase putative racemase possible alcohol dehydrogenase possible NifU-like domain possible branched-chain amino acid binding protein, ABC transport system putative glycolate oxidase subunit GlcE putative aldehyde dehydrogenase htrA-like serine protease cobB putative cobyrinic acid a,c-diamide synthase cobM precorrin 3 or 4 methylase unknown protein ilvJ possible leucine/isoleucine/valine-binding protein precursor conserved unknown protein morphinone reductase clpB endopeptidase unknown protein unknown protein putative diguanylate cyclase (GGDEF)/phosphodiesterase (EAL) mhpB possible 2,3-dihydroxyphenylpropionate 1,2-dioxygenase possible protease SohB possible urea/short-chain binding protein of ABC transporter putative aldehyde dehydrogenase conserved unknown protein putative long-chain-fatty-acid CoA ligase cobL putative precorrin 6y methylase putative hemin binding protein malate dehydrogenase-like protein transcriptional regulator, LysR family putative oxidoreductase soxB sulfur/thiosulfate oxidation protein soxY2 putative sulfur oxidation protein 2163

log2 ratio

lower CI

upper CI

putative function

−3.1 −2.6 −2.4 −1.4 −1.4 −1.3 −1.3

−3.5 −3.1 −2.6 −1.7 −1.9 −1.5 −2

−2.4 −2.2 −2.1 −1.1 −0.7 −1 −0.7

unknown iron uptake nucleic acid metabolism unknown unknown unknown cobalamin biosynthesis

−1.3 −1.2 −1.2 −1.1 −1.1 −1.1 −1.1 −1 −0.9 −0.9 −0.9 −0.9 −0.9 −0.9 −0.9 −0.9 −0.8 −0.8 −0.8 −0.8 −0.8 −0.8 −0.8 −0.8 −0.7 −0.7 −0.7 −0.7 −0.7 −0.7 −0.7 −0.7 −0.7 −0.7 −0.7 −0.7 −0.6 −0.6 −0.6 −0.6 −0.6 −0.6 −0.6 −0.6 −0.6 −0.6 −0.6 −0.6 −0.6 −0.6 −0.6 −0.6

−1.8 −1.8 −1.6 −1.4 −1.6 −1.7 −1.7 −1.5 −1.4 −1.3 −1.5 −1.3 −1.2 −1.3 −1.3 −1.2 −1.3 −1.4 −1 −1.3 −1.1 −1.3 −1.4 −1.1 −1.2 −0.9 −1.2 −1.2 −1.1 −1.2 −1 −1 −0.9 −1.1 −1 −0.9 −0.9 −1.1 −0.9 −1 −1.1 −1.1 −0.9 −0.9 −0.9 −1 −0.9 −1.1 −1 −1.1 −1.1 −1.2

−0.8 −0.7 −0.8 −0.8 −0.7 −0.6 −0.7 −0.5 −0.4 −0.3 −0.4 −0.6 −0.6 −0.6 −0.5 −0.6 −0.4 −0.3 −0.5 −0.4 −0.4 −0.4 0 −0.5 −0.3 −0.5 −0.3 −0.2 −0.3 −0.2 −0.3 −0.3 −0.4 −0.2 −0.5 −0.5 −0.4 0 −0.3 −0.2 −0.1 −0.1 −0.3 −0.2 −0.3 −0.2 −0.3 −0.1 −0.1 0 0 0

cobalamin biosynthesis metabolism sulfur oxidation selenium metabolism unknown unknown unknown LPS biosynthesis unknown nucleic acid metabolism carbon metabolism unknown cobalamin biosynthesis cobalamin biosynthesis unknown unknown carbon metabolism electron carrier hydralase unknown transport amino acid biosynthesis unknown unknown nitrogen fixation amino acid transport carbon metabolism unknown protease cobalamin biosynthesis cobalamin biosynthesis unknown amino acid transport unknown unknown protease unknown lipoprotein chaperone signal transduction carbon metabolism protein modification or turnover urea transport energy metabolism unknown lipid metabolisn cobalamin biosynthesis hemin transport energy metabolism unknown unknown sulfur oxidation sulfur oxidation DOI: 10.1021/pr5012558 J. Proteome Res. 2015, 14, 2158−2168

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Journal of Proteome Research Table 3. continued description

log2 ratio

lower CI

upper CI

possible sugar kinase possible Shikimate kinase with a HTH domain

−0.6 −0.6

−1.1 −1.1

−0.2 −0.1

gene RPA4656 RPA4787 a

putative function carbon metabolism carbon metabolism

CI, confidence interval.

broad-host-range vector under control of the lac promoter (constitutive in R. palustris) and introduced the plasmid into the wild-type R. palustris CGA010 by electroporation. Using this clone along with a second control strain containing the inset-free vector, we grew cultures of each strain anaerobically in the light to midexponential phase (OD λ660 ∼ 0.4) in PMS10 medium containing 14N (experiment) or 15N-labeled (control) ammonium sulfate. Duplicate cultures were centrifuged, and the pellets were resuspended to an equivalent OD. Cells from experimental and control conditions were mixed 1:1 (based on OD) and lysed, and total protein was extracted. The duplicate protein extracts were digested with trypsin, and each was analyzed in triplicate by LC−MS/MS. Expression of the EcfT fusion protein was verified by western blot analysis (Figure 1), showing a single band near 25 kDa, corresponding to the calculated mass of 24 kDa for the fusion protein. Additionally, on the basis of the isotope abundance ratios, EcfT fusion protein (RPA4225) was overexpressed approximately 2.5-fold relative to wild-type cells (Table 2). 14 N- and 15N-labeled proteins were identified from tandem mass spectra with the Sequest and DTASelect algorithms, and their abundance ratios were estimated from full-scan mass spectra with the ProRata program. The total proteome analysis yielded 1638 protein identifications from one biological replicate, 1587 identifications from the second biological replicate, and 1444 protein identifications common to both biological replicates. From the first biological replicate, 1338 R. palustris proteins were quantified, 1307 proteins were quantified from the second replicate, and 1148 R. palustris proteins were quantified at consistent abundance ratios from the two biological duplicates (Supporting Information Table S1). From this analysis, 54 proteins were found to have substantially higher abundance when using a log2 cutoff of ≥0.6 (i.e., 1.5-fold change) in R. palustris cells episomally expressing EcfT relative to control cells. These proteins are listed in Table 2. Among these, nearly half were found to be conserved proteins of unknown function. The protein with the highest fold change in expression was annotated as the catalase, KatE. Using the same requirements and a log2 cutoff of ≤ −0.6, 61 proteins were found to have a 1.5-fold or greater decrease in abundance as a result of expression of EcfT. A subset of proteins having the most dramatic decrease in abundance under the experimental conditions is shown in Table 3 (see Supporting Information Table S1 for full results). Many of these proteins were also classified as unknowns, whereas the remainder of them have numerous predicted protein functions including cobalamin biosynthesis enzymes, carbon metabolism, dehydratases, decarboxylases, and oxido-reductases.

Using an affinity chromatography−mass spectrometry approach, we demonstrated that the protein encoded by ecf T was coisolated when the α subunit (RpoA, RPA3226) or the β′ subunit (RpoC, RPA3267) of RNA polymerase were used as bait for affinity isolation from R. palustris cell lysates.20 These measurements identified 2−4 unique peptides from RPA4225, with up to 28% sequence coverage. This is consistent with the association of EcfT with RNA polymerase in its role as a σ factor. When the affinity isolation methodology was previously applied to the putative response-regulator RPA4223 as the bait protein, the putative anti-σ factor RPA4224 was identified as an interacting protein with a Bayes Odds score of 1.0 (high likelihood of interaction) and Ubiquity score of 0.476 (low likelihood of false positive interaction).20 In three out of four measurements (two technical replicates each for two biological replicates) with RPA4223 as bait, we identified 3 to 4 unique peptides from RPA4224 from 5 to 12 tandem mass spectra, with sequence coverage ranging from 8.1 to 26.6%.

qPCR

Promoter Alignment

Quantitative PCR (qPCR) was used to verify the proteomics data with a select subset of gene targets (Table 4). Messenger RNA was measured from total RNA extracts, from R. palustris cells episomally expressing EcfT, for a total of 11 genes. These corresponded to six proteins of increased abundance, four proteins of decreased abundance, and one protein with no

The predicted promoter regions for the genes whose protein products were found to be of increased abundance in the presence of EcfT were aligned using the VectorNTi (Invitrogen) software package. Predicted promoter regions that included approximately 150 bp of sequence 5′ to the predicted translational start site of the gene or of the first gene in a predicted

change in abundance, as determined by proteomics. For all cases of increased protein abundance, mRNA transcripts were also found to be increased relative to the wild-type control. For proteins with a decrease in abundance, all were downregulated at the mRNA level except for RPA0994, which was found to be unchanged. H2O2 Treatment

Transcription of five marker genes was selected for further investigation based on quantitative proteomics results indicating the largest change in abundance when ecf T was overexpressed. These were katA (the largest increase detected and a known stress-resistance gene), rpa3726 and rpa3568 (conserved unknown proteins previously associated with stress responses in E. coli), rpa4224 (in the same operon as the ecf T σ factor), and soxY2 (a representative gene selected from among proteins previously determined to have the greatest decrease in relative abundance when ecf T was overexpressed). Changes in relative expression of the five genes from R. palustris cultures exposed to 10 mM hydrogen peroxide were measured by qPCR (Table 5). The most dramatic increase in transcript level was found for the conserved unknown protein RPA3568, which showed a 50-fold increase over the untreated control. Results for katE (4.378-fold) and RPA4224 (1.442-fold) were also up, whereas soxY2 (0.846) was down slightly, in general agreement with both proteomics and qPCR of cells expressing ecf T in trans. However, in contrast to previous data, mRNA levels for RPA3726 were nearly unchanged or slightly downregulated (n-fold change = 0.9277). Protein−Protein Interaction

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32.3(0.71) 30.8(0.28) 0.35

operon were used. Results of the alignment are shown in Figure 2.28 A consensus sequence consisting of GGAAC-18N-TT was identified by pairwise comparison in each of the predicted promoter regions. CT values are averages of experimental duplicates or triplicates with standard deviations in parentheses. Fold change calculated based on ΔCT assuming 100% amplification efficiency.

Comparison of Gene Homologue Organization

The genome architecture containing these four genes was found to be highly conserved between R. palustris and S. meliloti orthologs smc01503, smc01504, rpoE2, and smc01507, with each of the S. meliloti genes returning the corresponding R. palustris gene as its top BLAST hit (Figure 3). A similar approach revealed orthologs also present in B. japonicum. In B. abortus 9-941, a similar gene cluster was found that lacked a homologue of the anti-σ factor RPA4224. Further investigation by two-way translated BLAST using a previously uncalled ORF in this strain located 5′ of the predicted ECF σ factor revealed a region of similarity between it and RPA4224, suggesting the presence of a previously unidentified putative anti-σ factor.



DISCUSSION In previous studies, we and others have shown that the R. palustris putative ECF σ factor encoded by ecf T was expressed under late-log or stationary phase growth conditions.16,17 After numerous failed attempts to construct a ecf T knockout in R. palustris, we decided to take an alternative approach to characterizing its potential role as a σ factor. Here, we utilized episomal expression of ecf T in wild-type cells and applied stable isotope labeling quantitative proteomics, qPCR, and comparative genomics to characterize the EcfT regulon in R. palustris. Using a stringent log2 cutoff of ≥0.6, 54 proteins were found to be significantly higher in abundance relative to control samples (Table 2). Results of qPCR were in good agreement with the trends measured by quantitative proteomics. However, it should be noted that qPCR data were normalized to expression levels of rpa0994 (gapdh), which may have resulted in underestimating the fold expression changes of other genes if it, too, was downregulated. In support of this, proteomics results indicated a possible slight decrease in abundance of the corresponding GAPDH protein relative to the control. These results highlight the difficulties of using housekeeping reference genes for qPCR, particularly when studying global gene regulatory systems. Among the proteins upregulated by episomal expression of the EcfT are a number of proteins with predicted roles in stress response. KatE (RPA3310) is a HPII catalase that in E. coli is upregulated during stationary phase.29 The Dps-like protein RPA1274 was also detected at high levels when EcfT was episomally expressed. Members of this family have been found to protect DNA and confer resistance to oxidation and stress in a variety of bacteria and archaea.30 A Dps protein lacking apparent DNA binding capacity has also been shown to confer hydrogen peroxide resistance in C. jejuni, probably by sequestration of free iron and limiting unwanted Fenton’s reactions.31 The presence of this protein in R. palustris should confer increased resistance to oxidative stress, particularly to hydrogen peroxide. The CheY-like response regulator RPA1481 is likely to further regulate a number of downstream genes. MetH is the cobalamin-dependent methionine synthase. Like E. coli, the genome of R. palustris encodes both metH as well as the cobalamin-independent methionine synthase metE. The latter has been shown in E. coli to be inactivated by oxidative stress, suggesting a rationale for the increased abundance of the alternative MetH in our system.32

a

RPA2420 RPA0994

29.2(0.21) 29.2(0.07) 1.00

RPA2977

25.5(0.15) 24.2(0.40) 0.41 30.5(0.28) 29.5(0.20) 0.50

RPA0285 katE

31.4(0.01) 32.5(0.21) 2.14 26.4(0.21) 27.1 (0) 1.68

RPA4224 RPA3943

35.4(0.71) 37.0(0.14) 3.03 36.6(0.28) 37.1(0.21) 1.37

RPA1481 RPA3568

24.5(0.15) 27.9(0.41) 10.56

RPA3726

27.7(0.21) 28.9(0.32) 2.41

sample

p4225 avg CT WT avg CT n-fold change

genes

Table 4. qPCR Validation of Proteomics Results for Selected Genes from Cultures Expressing RPA4224 in Trans or Wild-Type R. palustrisa

29.1(0.49) 28.5(0.14) 0.68

soxY2

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Journal of Proteome Research

Table 5. Changes in Gene Transcription in Wild-Type R. palustris upon Exposure to 10 mM H2O2 for Selected Genesa genes sample

RPA3726

RPA3568

RPA4224

katE

soxY2

10 mM H2O2 avg CT WT avg CT ΔΔCT n-fold change

25.03(0.39) 25.38(0.23) 0.1696 0.9277

24.64(1.19) 29.02(0.30) −7.397 50.118

26.23(0.15) 24.10(0.31) −1.121 1.442

27.54(0.29) 29.00(0.39) −2.488 4.378

29.74(0.33) 26.36(0.21) 0.3658 0.846

CT values are averages of experimental duplicates or triplicates with standard deviations in parentheses. Fold change calculated based on ΔΔCT using calculated amplification efficiency from standard curves of known DNA concentration. Data are normalized to glyceraldehyde-3-phosphate dehydrogenase (gapdh) mRNA as an internal standard. a

Figure 2. (A) Putative promoter alignments and predicted consensus sequence. (B) Logo depiction of predicted consensus sequence (weblogo. berkeley.edu). Letter height reflects frequency of nucleotide occurrence at a given position in the selected sequences.

by exposure to hydrogen peroxide may appear to contradict this conclusion (Table 5), cells are commonly exposed to multiple types of oxidative stress (e.g., singlet oxygen, organic hydroperoxides, superoxide, etc.) for which this gene may be more specifically suited. Alternatively, the results are also consistent with a protein that is regulated at the post-transcriptional level. Further studies will be necessary to address these questions. As for the other unknown proteins, the predicted role of MetH as a means of maintaining anabolism during oxidative stress conditions suggests the possibility that some of these unknowns may represent alternative, oxidation-resistant forms of proteins involved in essential biosynthetic pathways. It should also be noted that, owing to allocation of resources for the increased production of EcfT in our experimental system, the quantities of other proteins in general may be down relative to the control. This would tend to underestimate the total number of proteins upregulated in the presence of EcfT. The consensus sequence found within the promoters of several of the most highly expressed proteins under our experimental conditions is similar to that reported for the ECF σ factor RpoE2 of S. meliloti.7 RpoE2 is also thought to modulate stress response in that organism, and it was reported to directly control over one-third of the 60 stress response genes detected. A second, highly similar consensus sequence for the PhyR stress regulon from M. extorquens AM1 has also been reported.8a These data further support conservation among stress response pathways in α-Proteobacteria.

Figure 3. Genomic organization of RPA4225 and neighboring genes from R. palustris and orthologous genes in S. meliloti.

The roles of the numerous conserved unknown proteins showing substantial increases in abundance are, as yet, unclear. While they currently lack annotation, their association with oxidative stress, as presented here, suggests that these proteins would be good targets for future studies. RPA3726 is a protein that contains a conserved domain of unknown function (DUF892). While typically found as a single peptide, analysis of Pfam domain organization revealed this domain also associated with an N-terminal Mn-catalase domain33 in species of Bradyrhizobium, Rhizobium etli, and in R. palustris strain BisA53. The presence of a nonheme manganese-containing catalase was not identified in a search of the R. palustris CGA009 genome; however, the increase in protein abundance of RPA3726 in our test system concomitant with that of other oxidative stress related genes suggests that RPA3726 may possess a complementary or independent function in oxidative stress defense. While failure to induce RPA3726 transcription 2166

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Among the proteins that were dramatically reduced in abundance as a result of episomal expression of EcfT (Table 3) are several dehydrogenases and ABC-type transporters. RPA2977, which encodes a putative ribonucleotide reductase (Nrd), is involved in purine/pyrimidine metabolism. Also exhibiting decreased abundance were a number of enzymes in the cobalamin biosynthesis pathway (CobB, CobM, CobT1, CobQ2, CobH, and CobF). Our previously published protein−protein interaction data20 demonstrated that EcfT interacts with RNA polymerase subunits (RpoA and RpoC) and adds credence to the annotation of EcfT as a bona fide σ factor. The data presented here along with the upregulation of EcfT upon nitrogen starvation induced stationary phase17 is further evidence that this ECF σ factor has a role in stress response regulation. We know very little about the regulation of ecf T, but the proximity of genes for the predicted membrane-spanning histidine kinase (rpa4226) to the putative response-regulator (rpa4223), putative anti-σ factor (rpa4224), and the ECF σ factor (ecf T) described here is suggestive of a role in regulating EcfT activity. This is further supported by the conserved gene organization among other α-Proteobacteria, which is highly unlikely to be due to chance. Also, RPA4223 has amino acid identity to PhyR, which Gourion et al. have demonstrated is a regulator of global stress response in M. extorquens AM1.8a RPA4223 and PhyR both are predicted to have C-terminal response regulator receiver domains and lack predicted DNA-binding domains but possess ECF-σ factor-like domains on their N-termini.34 The conserved organization and genome location of RPA4223 and the protein−protein interaction identified between RPA4223 and RPA4224 suggest that this response regulator-like protein is the mediator between the sensor kinase RPA4226 and the anti-σ factor RPA4224. While the hypothesized regulation of EcfT has not yet been experimentally validated, a segment of this unique mechanism has been demonstrated for an orthologous system in M. extorquens and is composed of the response regulator phyR (ortholog of RPA4223), the anti-σ factor nepR (ortholog of RPA4224), and the ECF σ-factor ecf Tecf T1 (ortholog of rpa4225/ecf T).6 Regulation of that system, termed sigma factor mimicry by the authors, was found to involve the σ70-like domain present in the N-terminal region of PhyR, which, upon phosphorylation of the response regulator domain, interacts with the anti-σ factor NepR, thereby releasing the ECF-σ factor. A schematic illustrating this putative system is included as Supporting Information, Figure S1. The genes involved in the stress response regulation of M. extorquens appear to be distributed throughout its genome, and the sensor kinase has not yet been identified. In R. palustris, the HWE histidine kinase RPA4226 is located immediately upstream of the σ-factor. Future efforts will investigate the involvement of RPA4223 and RPA4226 in regulating ecf T expression.



Article

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: 865-576-2857. Fax: 865-241-1555. Notes

The authors declare no competing financial interest.



ACKNOWLEDGMENTS We thank Becky Maggard, ORNL, for assistance with manuscript preparation and Manesh Shah, ORNL, for assistance with proteome informatics. This research was supported by the DOE Genomic Science Program, sponsored by the Office of Biological and Environmental Research, United States Department of Energy Office of Science. Oak Ridge National Laboratory is managed and operated by UT-Battelle, LLC, under contract no. DE-AC05-00OR22725, for the U.S. Department of Energy.



REFERENCES

(1) Lonetto, M. A.; Brown, K. L.; Rudd, K. E.; Buttner, M. J. Analysis of the Streptomyces coelicolor sigE gene reveals the existence of a subfamily of eubacterial RNA polymerase sigma factors involved in the regulation of extracytoplasmic functions. Proc. Natl. Acad. Sci. U.S.A. 1994, 91, 7573−7577. (2) Lonetto, M.; Gribskov, M.; Gross, C. A. The sigma 70 family: sequence conservation and evolutionary relationships. J. Bacteriol. 1992, 174, 3843−3849. (3) (a) Brooks, B. E.; Buchanan, S. K. Signaling mechanisms for activation of extracytoplasmic function (ECF) sigma factors. Biochim. Biophys. Acta 2008, 1778, 1930−1945. (b) Lane, W. J.; Darst, S. A. The structural basis for promoter −35 element recognition by the group IV sigma factors. PLoS Biol. 2006, 4, e269. (4) (a) Helmann, J. D. The extracytoplasmic function (ECF) sigma factors. Adv. Microb. Physiol 2002, 46, 47−110. (b) Raivio, T. L.; Silhavy, T. J. Periplasmic stress and ECF sigma factors. Annu. Rev. Microbiol. 2001, 55, 591−624. (5) Hughes, K. T.; Mathee, K. The anti-sigma factors. Annu. Rev. Microbiol. 1998, 52, 231−286. (6) Francez-Charlot, A.; Frunzke, J.; Reichen, C.; Ebneter, J. Z.; Gourion, B.; Vorholt, J. A. Sigma factor mimicry involved in regulation of general stress response. Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 3467−3472. (7) Sauviac, L.; Philippe, H.; Phok, K.; Bruand, C. An extracytoplasmic function sigma factor acts as a general stress response regulator in Sinorhizobium meliloti. J. Bacteriol. 2007, 189, 4204−4216. (8) (a) Gourion, B.; Francez-Charlot, A.; Vorholt, J. PhyR is involved in the general stress response of Methylobacterium extorquens AM1. J. Bacteriol. 2008, 190, 1027−1035. (b) Gourion, B.; Sulser, S.; Frunzke, J.; Francez-Charlot, A.; Stiefel, P.; Pessi, G.; Vorholt, J. A.; Fischer, H.M. The PhyR−σEcfG signalling cascade is involved in stress response and symbiotic efficiency in Bradyrhizobium japonicum. Mol. Microbiol. 2009, 73, 291−305. (9) (a) Kaczmarczyk, A.; Campagne, S.; Danza, F.; Metzger, L. C.; Vorholt, J. A.; Francez-Charlot, A. Role of Sphingomonas sp. strain Fr1 PhyR−NepR−σEcfG cascade in general stress response and identification of a negative regulator of PhyR. J. Bacteriol. 2011, 193, 6629−6638. (b) Kim, H.-S.; Caswell, C. C.; Foreman, R.; Roop, R. M.; Crosson, S. The Brucella abortus general stress response system regulates chronic mammalian infection and is controlled by phosphorylation and proteolysis. J. Biol. Chem. 2013, 288, 13906− 13916. (10) Lourenço, R. F.; Kohler, C.; Gomes, S. L. A two-component system, an anti-sigma factor and two paralogous ECF sigma factors are involved in the control of general stress response in Caulobacter crescentus. Mol. Microbiol. 2011, 80, 1598−1612.

ASSOCIATED CONTENT

S Supporting Information *

Table S1: ProRata results for combined biological replicates. Figure S1: Proposed mechanistic model of RPA4225 (EcfT) interactions. This material is available free of charge via the Internet at http://pubs.acs.org. 2167

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Journal of Proteome Research (11) Foreman, R.; Fiebig, A.; Crosson, S. The LovK−LovR twocomponent system is a regulator of the general stress pathway in Caulobacter crescentus. J. Bacteriol. 2012, 194, 3038−3049. (12) Alvarez-Martinez, C. E.; Lourenco, R. F.; Baldini, R. L.; Laub, M. T.; Gomes, S. L. The ECF sigma factor σT is involved in osmotic and oxidative stress responses in Caulobacter crescentus. Mol. Microbiol. 2007, 66, 1240−1255. (13) Larimer, F. W.; Chain, P.; Hauser, L.; Lamerdin, J.; Malfatti, S.; Do, L.; Land, M. L.; Pelletier, D. A.; Beatty, J. T.; Lang, A. S.; Tabita, F. R.; Gibson, J. L.; Hanson, T. E.; Bobst, C.; Torres, J. L.; Peres, C.; Harrison, F. H.; Gibson, J.; Harwood, C. S. Complete genome sequence of the metabolically versatile photosynthetic bacterium Rhodopseudomonas palustris. Nat. Biotechnol. 2004, 22, 55−61. (14) Rey, F. E.; Heiniger, E. K.; Harwood, C. S. Redirection of metabolism for biological hydrogen production. Appl. Environ. Microbiol. 2007, 73, 1665−1671. (15) Harwood, C. S.; Gibson, J. Anaerobic and aerobic metabolism of diverse aromatic compounds by the photosythetic bacterium Rhodopseudomonas palustris. Appl. Environ. Microbiol. 1988, 54, 712− 717. (16) VerBerkmoes, N. C.; Shah, M. B.; Lankford, P. K.; Pelletier, D. A.; Strader, M. B.; Tabb, D. L.; McDonald, W. H.; Barton, J. W.; Hurst, G. B.; Hauser, L.; Davison, B. H.; Beatty, J. T.; Harwood, C. S.; Tabita, F. R.; Hettich, R. L.; Larimer, F. W. Determination and comparison of the baseline proteomes of the versatile microbe Rhodopseudomonas palustris under its major metabolic states. J. Proteome Res. 2006, 5, 287−298. (17) McKinlay, J. B.; Oda, Y.; Rühl, M.; Posto, A. L.; Sauer, U.; Harwood, C. S. Non-growing Rhodopseudomonas palustris increases the hydrogen gas yield from acetate by shifting from the glyoxylate shunt to the tricarboxylic acid cycle. J. Biol. Chem. 2014, 289, 1960−1970. (18) Kim, H. K.; Harwood, C. S. Regulation of benzoate-CoA ligase in Rhodopseudomonas palustris. FEMS Micriobiol. Lett. 1991, 83, 199− 203. (19) Rey, F. E.; Oda, Y.; Harwood, C. S. Regulation of uptake hydrogenase and effects of hydrogen utilization on gene expression in Rhodopseudomonas palustris. J. Bacteriol. 2006, 188, 6143−6152. (20) Pelletier, D. A.; Hurst, G. B.; Foote, L. J.; Lankford, P. K.; McKeown, C. K.; Lu, T. Y.; Schmoyer, D. D.; Shah, M. B.; Hervey, W. J. t.; McDonald, W. H.; Hooker, B. S.; Cannon, W. R.; Daly, D. S.; Gilmore, J. M.; Wiley, H. S.; Auberry, D. L.; Larimer, F. W.; Kennel, S. J.; Doktycz, M. J.; Morrell-Falvey, J. L.; Owens, E. T.; Buchanan, M. V. A general system for studying protein−protein interactions in Gramnegative bacteria. J. Proteome Res. 2008, 7, 3319−3328. (21) Kovach, M. E.; Elzer, P. H.; Hill, D. S.; Robertson, G. T.; Farris, M. A.; Roop, R. M., II; Peterson, K. M. Four new derivatives of the broad-host-range cloning vector pBBR1MCS, carrying different antibiotic-resistance cassettes. Gene 1995, 166, 175−176. (22) Lowry, O. H.; Rosebrough, N. J.; Farr, A. L.; Randall, R. J. Protein measurement with the Folin phenol reagent. J. Biol. Chem. 1951, 193, 265−275. (23) (a) Link, A. J.; Eng, J.; Schieltz, D. M.; Carmack, E.; Mize, G. J.; Morris, D. R.; Garvik, B. M.; Yates, J. R., III Direct analysis of protein complexes using mass spectrometry. Nat. Biotechnol. 1999, 17, 676− 682. (b) McDonald, W. H.; Ohi, R.; Miyamoto, D. T.; Mitchison, T. J.; Yates, J. R., III Comparison of three directly coupled HPLC MS/MS stratagies for identificaiton of proteins from complex mixtures: singledimension LC-MS/MS, 2-phase MudPIT, and 3-phase MudPIT. Int. J. Mass Spectrom. 2002, 219, 245−251. (24) Eng, J. K.; McCormack, A. L.; Yates, J. R., III An approach to correlate MS/MS data to amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 1994, 5, 976−989. (25) (a) Pan, C.; Kora, G.; McDonald, W. H.; Tabb, D. L.; VerBerkmoes, N. C.; Hurst, G. B.; Pelletier, D. A.; Samatova, N. F.; Hettich, R. L. ProRata: a quantitative proteomics program for accurate protein abundance ratio estimation with confidence interval evaluation. Anal. Chem. 2006, 78, 7121−7131. (b) Pan, C.; Kora, G.; Tabb, D. L.; Pelletier, D. A.; McDonald, W. H.; Hurst, G. B.; Hettich, R. L.; Samatova, N. F. Robust estimation of peptide

abundance ratios and rigorous scoring of their variability and bias in quantitative shotgun proteomics. Anal. Chem. 2006, 78, 7110−7120. (26) Tabb, D. L.; McDonald, W. H.; Yates, J. R., III DTASelect and Contrast: tools for assembling and comparing protein identifications from shotgun proteomics. J. Proteome Res. 2002, 1, 21−26. (27) Pfaffl, M. W. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 2001, 29, 6. (28) (a) Crooks, G. E.; Hon, G.; Chandonia, J. M.; Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 2004, 14, 1188− 1190. (b) Schneider, T. D.; Stephens, R. M. Sequence logos: a new way to display consensus sequences. Nucleic Acids Res. 1990, 18, 6097−100. (29) (a) Loewen, P. Probing the structure of catalase HPII of Escherichia colia review. Gene 1996, 179, 39−44. (b) Mulvey, M. R.; Sorby, P. A.; Triggs-Raine, B. L.; Loewen, P. C. Cloning and physical characterization of katE and katF required for catalase HPII expression in Escherichia coli. Gene 1988, 73, 337−345. (30) (a) Nair, S.; Finkel, S. E. Dps protects cells against multiple stresses during stationary phase. J. Bacteriol. 2004, 186, 4192−4198. (b) Ramsay, B.; Wiedenheft, B.; Allen, M.; Gauss, G. H.; Martin Lawrence, C.; Young, M.; Douglas, T. Dps-like protein from the hyperthermophilic archaeon Pyrococcus f uriosus. J. Inorg. Biochem. 2006, 100, 1061−1068. (c) Wiedenheft, B.; Mosolf, J.; Willits, D.; Yeager, M.; Dryden, K. A.; Young, M.; Douglas, T. An archaeal antioxidant: characterization of a Dps-like protein from Sulfolobus solfataricus. Proc. Natl. Acad. Sci. U.S.A. 2005, 102, 10551−10556. (31) Ishikawa, T.; Mizunoe, Y.; Kawabata, S.; Takade, A.; Harada, M.; Wai, S. N.; Yoshida, S. The iron-binding protein Dps confers hydrogen peroxide stress resistance to Campylobacter jejuni. J. Bacteriol. 2003, 185, 1010−1017. (32) Hondorp, E.; Matthews, R. Oxidative stress inactivates cobalamin-independent methionine synthase (MetE) in Escherichia coli. PLoS Biol. 2004, 2, e336. (33) Chelikani, P.; Fita, I.; Loewen, P. C. Diversity of structures and properties among catalases. Cell. Mol. Life Sci. 2004, 61, 192−208. (34) Gourion, B.; Rossignol, M.; Vorholt, J. A. A proteomic study of Methylobacterium extorquens reveals a response regulator essential for epiphytic growth. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 13186− 13191.

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DOI: 10.1021/pr5012558 J. Proteome Res. 2015, 14, 2158−2168