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Responses of Pseudomonas putida to zinc excess determined at the proteome level: pathways dependent and independent of ColRS Karl Mumm, Kadi Ainsaar, Sergo Kasvandik, Tanel Tenson, and Rita Hõrak J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00420 • Publication Date (Web): 31 Oct 2016 Downloaded from http://pubs.acs.org on November 2, 2016

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Journal of Proteome Research

Responses of Pseudomonas putida to zinc excess determined at the proteome level: pathways dependent and independent of ColRS

Karl Mumm1§, Kadi Ainsaar1§, Sergo Kasvandik2, Tanel Tenson2 and Rita Hõrak1*

1

Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia

2

Institute of Technology, University of Tartu, Tartu 50411, Estonia

§

The authors contributed equally to the work

*Corresponding author: E-mail [email protected], Tel +372 737 4077

Keywords: zinc excess, proteomic response, stress response, ColRS two-component system, Pseudomonas putida

Short title: zinc-induced response of P. putida and its colR knockout

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ABSTRACT Zinc is an important micronutrient for bacteria but its excess is toxic. Recently, the ColRS twocomponent system was shown to detect and respond to zinc excess and to contribute to zinc tolerance of Pseudomonas putida. Here, we applied a label-free whole-cell proteome analysis to compare the zinc-induced responses of P. putida and colR knockout. We identified dozens of proteins that responded to zinc in a ColR-independent manner, among others, known metal efflux systems CzcCBA1, CzcCBA2, CadA2 and CzcD. Nine proteins were affected in a ColR-dependent manner and besides known ColR targets, four new candidates for ColR regulon were identified. Despite the relatively modest ColR-dependent changes of wild-type, colR deficiency resulted in drastic proteome alterations, with 122 proteins up- and 62 down-regulated by zinc. This zincpromoted response had remarkable overlap with the alternative sigma factor AlgU-controlled regulon in P. aeruginosa. The most prominent hallmark was a high induction of alginate biosynthesis proteins and regulators. This response likely alleviates the zinc stress as the AlgUregulated alginate regulator AmrZ was shown to contribute to zinc tolerance of colR knockout. Thus, the ColRS system is important for zinc homeostasis and in its absence, alternative stress response pathways are activated to support the zinc tolerance.

INTRODUCTION Zinc, the second most important transition metal in prokaryotes, functions as an essential structural and catalytic metal cofactor for numerous enzymes that participate in diverse biological reactions 1,2. Although zinc plays a vital role in cell physiology, excess zinc is toxic because it easily interacts with thiol groups and can replace other metals in their complexes 3. Therefore, the cytosolic zinc concentration is kept low 4, which is mostly achieved by strict control over the zinc import and efflux as well as by production of specific metal-binding proteins that are able to sequester free zinc ions 5,6

. Export systems that rid the cells of surplus zinc include, for instance, CzcABC-like RND multi-

drug efflux transporters, CadA- and ZntA-like P-type ATPases and CzcD-like cation diffusion


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facilitators 5,7. The expression of these metal efflux systems is induced in zinc excess conditions by CzcRS-, MerR/ZntR- and ArsR/SmtB-like metal-responsive regulators 7,8,9. Members of the genus Pseudomonas are known for their high intrinsic tolerance to different chemical stressors, including aromatics, antibiotics, detergents and, also, to heavy metals 10,11, which mostly relies on their versatile metabolic, transport and signaling ability 12,13. Species of P. putida inhabit soil, water and the rhizosphere and several of them are used as laboratory model strains. P. putida KT2440, for instance, has been extensively employed for determining the bacterial stress tolerance mechanisms as it is capable of degrading environmental pollutants and endures exposure to heavy metals 14,15. The genome of P. putida encodes an unexpected capacity to heavy metal tolerance with some efflux systems even present in two copies 14. For instance, two CadA Ptype ATPase and two Czc chemiosmotic metal transporter operons are present in P. putida genome. Both the CzcCBA1 and the CzcCBA2 have been found to influence zinc resistance 16, however, as CzcCBA2 activity was detected only in a czc1-defective background, CzcCBA1 is considered to be the main zinc efflux system in P. putida 16. Besides CzcCBA efflux systems, the Ptype ATPase CadA2 has also been shown to contribute to zinc resistance of P. putida, yet, its effects are small and it primarily confers resistance to cadmium 17,18. Sensing of extracellular metal concentration is the first line of defence in bacteria against metal toxicity permitting quick response and appropriate rearrangement of gene expression. In Pseudomonas aeruginosa, the zinc-responsive CzcRS two-component system has been shown to confer zinc, cadmium and cobalt resistance by regulating the expression of the czcCBA efflux system 19. Recent results show that another two-component system, the ColRS signaling pathway, contributes to zinc tolerance of Pseudomonads as well, by ColS sensor kinase sensing the excess of external zinc in P. putida 20 and probably also in P. aeruginosa 21. Aside from zinc, also excess of iron, manganese and cadmium activate ColS in P. putida and this, consequently, results in changed expression of the ColR regulon genes 20. ColRS deficiency not only impairs metal tolerance of P. putida and P. aeruginosa 17,20,21 but also of different Xanthomonas species 22,23. However, although the ColRS system is shown to be important for metal homeostasis in different bacteria, the 3 ACS Paragon Plus Environment


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mechanistic basis for ColRS-conferred metal tolerance has remained unclear. Still, the phenotypic effects of colR and colS mutants suggest that the ColRS system is involved in regulation of membrane functionality 21,23,24,25,26. This is further supported by the finding that most of the ColR regulon genes encode membrane proteins or proteins involved in membrane biogenesis 20,24,27. For instance, in P. putida ColR regulates genes such as pagL, cptA and dgkA 27 which are involved in the synthesis and/or modification of lipopolysaccharides (LPS) and phospholipids 28,29,30. A recent study in P. aeruginosa demonstrated that ColR indeed regulates LPS remodeling under zinc stress condition as ColR specifically induces EptA which catalyzes the phosphoethanolamine (pEtN) addition to lipid A in lieu of 4-amino-4-deoxy-L-arabinose (L-Ara4N) 21. Nevertheless, such a zincand ColR-induced LPS remodeling does not seem to be a major contributor to metal tolerance of P. aeruginosa because an eptA knockout displayed no growth defect in media with zinc 21. Given that inactivation of any individual ColR-regulated gene did not change the metal sensitivity of P. putida either, and considering that deletion of multiple genes was necessary to observe sensitization of bacteria to zinc, the ColRS-mediated metal tolerance is probably provided by the joint action of the whole ColR regulon 20. Thus, to get more in-depth insight into the zinc-induced response of P. putida and to clarify the regulatory effects of the ColRS system in adaptation to metal excess condition, we analyzed the zinc response of wild-type and colR-deficient P. putida at proteome level. Our data revealed dozens of ColR-independent proteomic changes common to both zinc-treated wild-type and colR knockout. However, we also disclosed hundreds of proteins that responded to zinc in colR-deficient strain only, suggesting activation of alternative stress response pathways to compensate the lack of ColRS signalling.

MATERIAL AND METHODS Bacterial strain and growth conditions Bacterial strains used were P. putida PaW85 31, which is isogenic to the fully sequenced KT2440 32, and its colR-deficient derivative strain 33. Bacteria were grown in lysogeny broth (LB). When


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selection was necessary, the growth medium was supplemented with kanamycin (50 µg ml-1), streptomycin (20 µg ml-1) or benzylpenicillin (1500 µg ml-1). For generation of zinc excess, ZnSO4 was added to the growth medium in the concentrations specified in each experiment. Bacteria were grown at 30°C. P. putida was electrotransformed according to the protocol of Sharma and Schimke 34

. Strains and plasmids used are in Table 1.

Construction of plasmids and strains Oligonucleotides used are listed in Table 2. For generation of amrZ (PP4470) and PP0032 deletion strains, a pEMG-based plasmids were constructed according to the protocol described elsewhere 35. Briefly, the upstream and downstream regions of the amrZ and PP0032 (about 500 bp) were amplified with the primer pairs amrZyl/amrZalgus and amrZpikk/amrZKpn, and 0032Kpn/0032ees and 0032pikk/0032Xba, respectively. PCR fragments were then joined into one fragment by overlap extension PCR using either oligonucleotides amrZyl and amrZKpn or 0032Kpn and 0032Xba. For construction of the plasmids pEMG/∆amrZ and pEMG/∆PP0032, the PCR fragments of about 1 kb were cut with BamHI-KpnI and XbaI-KpnI, respectively, and ligated into the corresponding sites of the plasmid pEMG. Plasmids pEMG/∆amrZ and pEMG/∆PP0032 were delivered to P. putida PaW85 and colR-deficient strains by electroporation and after 2.5 hours of growth in LB medium the bacteria were plated onto LB agar supplemented with kanamycin. Kanamycin-resistant cointegrates were selected and electrotransformed with the I-SceI expression plasmid pSW(I-SceI). For cointegrate resolution, the plasmid-encoded I-SceI was induced with 1.5 mM 3-methylbenzoate overnight. Kanamycin-sensitive colonies were selected and the deletion of amrZ or PP0032 was verified by PCR. The plasmid pSW(I-SceI) was eliminated from the deletion strains by growing them overnight in LB medium without antibiotics. To disrupt the algD gene (PP1288), the coding region of algD was PCR-amplified from the chromosome of P. putida PaW85 with oligonucleotides algDalgus and algDlopp. algD-containing PCR fragment was cloned into HincII-EcoRV-opened pBluescript KS. The central 150-bp region of algD in pKS/algD was excised with Eco130I and replaced with the Smr gene cut from pUTmini-



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Tn5Sm/Sp with VspI. The obtained algD::Sm sequence was subcloned as an XbaI-Acc65I fragment into pGP704L. The interrupted algD gene was inserted into the chromosome of P. putida PaW85 and colR mutant by homologous recombination. Plasmid p704L/algD::Sm was conjugatively transferred from E. coli CC118 λ pir into P. putida PaW85 and colR mutant using a helper plasmid pRK2013. The algD knockout strains were verified by PCR analysis. To construct the transcriptional fusions of the amrZ (PP4470) and PP0092 with lacZ, the upstream regions of the respective loci were amplified from the P. putida PaW85 chromosome with the primer pairs amrZyl/amrZal and Bam92algus/92allBam. The resulting PCR fragments were treated with BamHI and inserted into BamHI-opened p9TTBlacZ.

Proteomics analysis Growth conditions and nano-LC/MS/MS analysis. Bacteria were pregrown overnight in liquid LB at 30°C and then diluted into fresh LB medium to an OD580 of ~0.1. After 2.5 h of growth (at an OD580 ~0.8), both wild-type and colR-mutant cultures were divided into two: while one half was further grown untreated, the other was supplemented with 0.6 mM ZnSO4. After 3 h of zinc treatment, the cells were pelleted at 5000 g at 4°C for 10 minutes, resuspended in lysis buffer (4% SDS, 100 mM Tris pH 7.5, 20 mM dithiothreitol), heated at 95°C for 5 min and sonicated. The protein concentration was measured by tryptophan fluorescence and 30 µg of cellular protein was loaded onto 30 kDa cut-off Vivacon 500 ultrafiltration spin columns (Sartorius). Three independent samples of non-induced and zinc-induced P. putida wild-type and colR knockout were digested onfilter with 1:50 proteomics grade dimethylated trypsin (Sigma-Aldrich) in the presence of 1.0% sodium deoxycholate. After phase transfer removal of the detergent with ethyl acetate, the samples were desalted with C18 StageTips 36. Samples were eluted, dried and reconstituted in 0.5% trifluoroacetic acid. Nano-LC/MS/MS analysis was performed as described in 37 using an Ultimate 3000 RSLCnano system (Dionex) and a Q Exactive mass-spectrometer (Thermo Fisher Scientific) operating with a top-10 data-dependent acquisition. MS raw data processing. Raw data were identified and quantified with the MaxQuant 1.4.0.8 software package 38. Label-free quantification with MaxQuant LFQ algorithm 39 was enabled with 6 ACS Paragon Plus Environment

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default settings. Methionine oxidation and protein N-terminal acetylation were set as variable modifications, while cysteine carbamidomethylation was defined as a fixed modification. Search was performed against UniProt (www.uniprot.org) Pseudomonas putida KT2440 database (2015 April version) using the tryptic digestion rule (cleavage after lysine and arginine without proline restriction). Identifications with minimally 1 peptide of at least 7 amino acids long were accepted and transfer of identifications between runs was enabled. Protein quantification criteria were set to 2 peptides with minimally 2 consecutive MS1 scans per peptide. Peptide-spectrum match and protein false discovery rate (FDR) was kept below 1% using a target-decoy approach. All other parameters were kept default. Quantitative protein profiling. The obtained dataset contained 3462 different proteins and was analyzed with the MaxQuant statistics module Perseus (version 1.5.1.6). Data were analyzed in pairs, where at least one condition has values for all three parallels. Four groups were created: (i) P. putida wild-type (wt) vs colR knockout (R) (2705 proteins), (ii) wt vs wt+Zn (2740 proteins), (iii) R vs R+Zn (2713 proteins) and (iv) R+Zn vs wt+Zn (2759 proteins). Missing protein intensities (i.e. proteins below the limit of detection) were replaced for each of those comparison groups by values from an imputed distribution created by shrinking (width: 0.3, 0.25, 0.3 and 0.25 SD units, respectively) and shifting (down shift: 2, 2, 1.9 and 1.9 SD units, respectively) the measured intensity distribution to the left side of the intensity distribution (i.e. to derive noise level intensities). If protein amounts compared in a group had imputed values the relative amount value has an “i” as a reference. Proteins in each comparison group were differentially compared using the independent samples ttest. To account for multiple testing Benjamini-Hochberg false discovery rate procedure (FDR

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