Comparative Proteomics Study of Salt Tolerance between a

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Comparative Proteomics Study of Salt Tolerance between a Nonsequenced Extremely Halotolerant Cyanobacterium and Its Mildly Halotolerant Relative Using in vivo Metabolic Labeling and in vitro Isobaric Labeling Jagroop Pandhal, Saw Yen Ow, Phillip C. Wright, and Catherine A. Biggs* Biological and Environmental Systems Group, Department of Chemical and Process Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, United Kingdom Received April 14, 2008

Euhalothece sp. BAA001 is an extremely halotolerant cyanobacterium, and recent proteomic investigations have revealed many shared survival strategies with its well-studied and moderately halotolerant relative Synechocystis sp. PCC6803. We exploit the shared tryptic peptides between these organisms and directly compare the relative protein abundance in cells grown in the exact same salt conditions. This comparison is made with added salt (NaCl) concentrations of 0, 3, and 6% (w/v), where significant abundance differences are explained in terms of prioritization of essential cellular processes in relation to salinity tolerance. Implementation of 15N in vivo metabolic labeling in conjunction with conventional search software, Mascot, and quantification software MSQUANT allowed 243 unique proteins to be quantified. The characteristic “stress” response that Euhalothece displays in 0% salt is observed through higher abundance of stress associated proteins, including a putative DNA binding stress protein and antioxidative enzymes. In contrast, Synechocystis expresses a greater number of “stress” proteins in 3% and 6% salt. In addition to in vivo metabolic labeling, an experiment using in vitro isobaric labeling (iTRAQ) was also carried out, which successfully demonstrated its applicability in cross-species proteomics. Keywords: cross-species • cyanobacteria • iTRAQ • metabolic labeling • salt adaptation

Introduction It is estimated that up to half of the world’s irrigated land is affected by salinity, a problem exacerbated by current agricultural methods.1 Salt stress greatly affects plant growth mainly by influencing cellular homeostasis through disruption of water potential and ion distribution and can often lead to secondary harmful effects, such as oxidative damage. Ultimately, this reduces overall world crop production.1 In order to overcome these detrimental impacts on crop growth, understanding the cellular response to high salinity is a major area of research.2-16 Much of this research is focused in cyanobacteria, which are phylogenetically related to plant chloroplasts. Cyanobacteria are the only prokaryotes which, similar to plants, can both photosynthesise and possess photosynthetic apparatus in thylakoid membranes,17 thus they are often used as models to understand plant cellular function and metabolism. The majority of cyanobacterial stress studies have been carried out in the unicellular, fresh water, oxygenic photoautotroph Synechocystis sp. PCC6803 (henceforth referred to as Synechocystis). Its relative ease to culture, possibility of photoheterotrophic growth and naturally transformable cells, have led to Synechocystis being regarded as a suitable model organ* To whom correspondence should be addressed. E-mail: c.biggs@ sheffield.ac.uk. Fax number: 0114 222 7501.

818 Journal of Proteome Research 2009, 8, 818–828 Published on Web 12/17/2008

ism to aid in understanding metabolism and overall cell functioning in plants. Since its genome was sequenced,18 understanding the Synechocystis salt response at the molecular level has vastly improved, attributable to a variety of genetic and biochemical analyses.19-23 Working with Synechocystis is particularly attractive because of the technological advances made using this model system, including the relative ease in protein identification due to availability of a genome sequence. This fresh water cyanobacterium can survive in salt concentrations ranging from ca. 0 to 7%24 (optimum growth at 0% salt) by using techniques including activation of ion exchangers and synthesis or uptake of the compatible solute glucosylglycerol.14 The accumulation of compatible solutes primarily stabilizes proteins and membrane structure via restoring an acceptable osmotic potential with the surroundings.25,26 High salinity is also known to increase fluidity and decrease permeability of membranes to H+ and Na+ ions.14 Lipids are likely to be involved in protection against salt stress,4 and composition changes include increases in the proportion of unsaturated fatty acids and phosphatidylglycerides which have been observed in mildly halotolerant Synechococcus PCC631127 and in halophilic Aphanothece halophytica.28 The presence of unsaturated fatty acids in thylakoid membranes is also important for the tolerance of photosynthetic machinery to salt stress.30 10.1021/pr800283q CCC: $40.75

 2009 American Chemical Society

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Comparative Proteomics Study of Salt Tolerance Table 1. General Comparison between Synechocystis and Euhalothece Cells15,35,39

Cell shape Cell type Cell size Ø Pigments Salt tolerance Compatible solute

Euhalothece

Synechocystis

coccoid/bacilloid (some sheathed) unicellular 2.25-3.4 µM chl a/b high (max ∼15%) glycinebetaine, glutamatebetaine glucosylglycerol

coccoid (nonsheathed) unicellular 2-4 µM chl a/b moderate (max ∼7%) glucosylglycerol

However, further long-term adaptation to the stress conditions is dependent upon the synthesis of “adaptation proteins”, and therefore the field of proteomics has developed into a powerful tool for elucidating cell adaptation to adverse conditions.16,31-33 Adaptation to ∼4% salt in Synechocystis was recently reported using a combination of two-dimensional electrophoresis (2DE) and matrix assisted laser desorption ionization mass spectrometry (MALDI-TOF MS), where essential acclimation proteins (after 5 days of incubation) were grouped into four main categories: salt specific stress proteins, general stress proteins, enzymes of basic carbon metabolism and hypothetical proteins.32 Salt specific stress proteins include enzymes involved in compatible solute production, for example, ADP-glucose pyrophosphorylase, which is responsible for synthesizing the precursor ADP-glucose for the compatible solute glucosylglycerol. The need to produce this carbon-based osmoprotective compound means cells divert organic carbon pathways toward its production, and this has been proposed through protein abundance changes in enzymes involved in glycolysis and the Calvin cycle.2,32 General stress proteins are synthesized in response to a variety of stresses and include molecular chaperones responsible for maintaining optimum protein activity and removing degraded macromolecules. Proteomic salt tolerance studies have also been conducted on specific cellular compartments including the membranes and periplasmic space.16,33,34 Although there have been several proteomic studies on salt tolerance in Synechocystis, it is a moderately halotolerant species, and extremely halotolerant cyanobacteria, which may enable the mechanisms of high salt tolerance to be investigated, have received little attention. An extremely halotolerant cyanobacterium, Euhalothece sp. BAA001 (henceforth referred to as Euhalothece), was isolated from a salt lake in the heart of the Sahara.35Euhalothece can survive in salt concentrations ranging from ca. 0% up to ca. 12%, with a growth optimum at 3% salt.2 Varying degrees of similarity between Euhalothece and Synechocystis has been demonstrated in several characteristics, including 16S rRNA sequence (80%), morphology and size (see Table 1). Perhaps more importantly in terms of proteomic analysis, ca. 95% of proteins identified in a cross-species in vivo metabolic labeling proteomic study of Euhalothece were orthologous to peptides originating from Synechocystis.2 Taji et al.36 carried out a comparative cDNA microarraybased study on salt tolerance in the higher plants, Arabidopsis and Salt Cress (Thellungiella halophile).36 Salt Cress can tolerate high salt concentrations, and is therefore referred to as a halophyte, whereas Arabidopsis, like most plants, is a glycophyte. The cDNA microarray approach was possible because the similarity of genome sequence between the two organisms is >90%,36 and they share several other similar characteristics. The basis for the comparison is the theory that nearly all salt

tolerance genes are present in both highly salt sensitive and highly salt tolerant plants, suggesting differences in gene regulation are significant.36,37 Interestingly, the results indicate that salt stress tolerance of Salt Cress may be due to constitutive overexpression of many genes that function in stress tolerance under normal growth conditions, and these genes are stress inducible in Arabidopsis.36 Consequently, crossspecies studies do not solely seek to reveal novel salt responsive mechanisms, but rather highlight the level of control associated with adaptation strategies, and this initiative is applied here to the study of the two cyanobacteria. A proof of concept has been demonstrated by Snijders et al.,38 that shared tryptic peptides from proteins which are encoded in different species are suitable for relative quantification across the species boundary. By metabolically labeling one proteome (15N) and mixing it with the nonlabeled proteome (14N), the relative abundance of proteins can be calculated by mass spectrometry (MS) for those which share tryptic peptides. However, for this to be successful with an unsequenced organism, a significant number of shared peptides are necessary. In the case of Euhalothece, as mentioned above, we found that there were sufficient orthologous peptides within the nonredundant NCBI database to result in sufficient identifications to be meaningful.2 The similarities between Synechocystis and Euhalothece therefore offer distinct advantages that aid a rounded proteomic comparison of salt adaptation strategies. First, a direct comparison with Synechocystis, which is known to be less salt tolerant, can be readily weighted. Second, with the reliance on peptide sequence orthology to provide cross-species protein identification and quantitation, a direct relative comparison of this nature has the potential to reveal distinctive proteomic patterns in Synechocystis cells compared to Euhalothece cells, over a range of salinity concentrations. The extent of homology between these cyanobacteria is thus suitable for a relative quantitation study of proteins across the species boundary. Therefore, we utilize this cross-species approach to compare the relative abundance of proteins through sequence identity of shared peptides in conditions of low (0% w/v), medium (3% w/v) and high (6% w/v) salt, in the mildly halotolerant Synechocystis and extremely halotolerant Euhalothece. By assessing protein abundance differences between the organisms, implications for adaptation mechanisms to high salt conditions can be investigated. In conjunction with metabolic labeling, an in vitro isobaric labeling (iTRAQ) experiment is also designed and implemented to complement proteomic findings on Euhalothece and Synechocystis cells cultured in 6% salt. To our knowledge, this is the first application of iTRAQ-based protein quantification across the species boundary and its use in this study will therefore demonstrate its potential applicability.

Material and Methods An overview of the experimental program conducted in this study is given in Figure 1. All chemicals were purchased from Sigma-Aldrich (Gillingham, Dorset, U.K.) unless otherwise stated. Cell Culture Preparations and Growth. Euhalothece sp. BAA001 and Synechocystis sp. PCC6803 cells were grown separately at 24 °C in batch culture using 250-mL flasks, in a modified BG11 medium.39 Three separate cultures were grown in biological triplicate, differing in added salt (NaCl) concentration, low (0% w/v), medium (3% w/v) and high (6% w/v) salt. The total salt concentration in the growth medium prior to Journal of Proteome Research • Vol. 8, No. 2, 2009 819

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Figure 1. Overview of the experiment design. Euhalothece was cultured in 15N-BG11 media and mixed in equal amounts with Synechocystis cells cultured in 14N-BG11 media, in three different added salt concentrations (0, 3 and 6 w/v%). Technical (n ) 2) and biological replicate (n ) 3) gels were run for each salt concentration. Shared tryptic peptides were used for quantitation. iTRAQ was performed using biological replicate cultures in BG11 media. Solid lines represent interspecies comparisons, dotted lines represent intraspecies comparisons.

addition of NaCl was 0.02% w/v. All Euhalothece cultures were grown in modified BG11 medium, where sources of 14N nitrogen were replaced with 15N nitrogen.2 This ensured that quantitation of only heavy isotopic peptides (15N) were representative of peptides sourced from Euhalothece. The main nitrogen source (1.5 g/l), sodium nitrate (Na14NO3) was replaced with heavy sodium nitrate (Na15NO3) for the metabolic labeling strategy.2 Growth in 15N media was followed for at least eight doubling times to ensure maximum label incorporation.2 It was imperative to keep all external environmental factors as similar as possible for both organisms. All cultures were buffered to pH 7.4 with 50 mM MOPS, illuminated on a 12 h light-dark cycle at 90 µEinsteins m-2s-1 with continuous shaking at 220 rpm. Cell growth was measured using optical density measurements (wavelength of 730 nm) using an UltraSpec 2100 Pro spectrophotometer (Amersham Biosciences, Buckinghamshire, U.K.). Protein Preparation. Growth was followed to late-exponential phase where O.D.730nm ) ∼1.0. 50-mL of cells were harvested by centrifugation (10 000× g and 4 °C) for 20 min. Two additional biological replicate sets of these cultures were treated in parallel. Cells were washed with 5 volumes of a sucrose based buffer consisting of 50 mM Tris (pH 7.4), 100 mM EDTA (pH 8) and 25% (w/v) sucrose. Pelleted cells were resuspended in 1 mL freshly made extraction buffer consisting of 40 mM Tris-HCl at pH 8.7, 1 mM ascorbic acid, 5 mM MgCl2, 1.1 g/L polyvinylpolypyrrolidone (PVPP), 1 mM dithiothreitol (DTT) and a 5% (w/v) protease inhibitor cocktail specific for bacterial cell extracts, and soluble protein was extracted with mechanical cracking and liquid nitrogen in a mortar and 820

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pestle. Recovery of soluble proteins was achieved by centrifugation (21 000× g at 4 °C) for 30 min. A HiTrap desalting column (Amersham Biosciences) was implemented according to the manufacturer’s protocol. The total protein concentration obtained was quantified in triplicate using a range of dilutions (to maximize accuracy) with the RC DC Protein Assay (BioRad, Hertfordshire, U.K.). Protein Fractionation. As detailed in Figure 1, three different protein mixtures (50 µg of each protein extract) were prepared in microcentrifuge tubes and made up to a volume of 25 µL using sterile H2O: (1) a mixture of 0% salt Synechocystis protein extract (14N) and 0% salt Euhalothece protein extract (15N), (2) a mixture of 3% salt Synechocystis protein extract (14N) and 3% salt Euhalothece protein extract (15N), and (3) a mixture of 6% salt Synechocystis protein extract (14N) and 6% salt Euhalothece protein extract (15N). For SDS-PAGE, each protein mixture was combined with Sample Buffer/Laemmli Buffer (Bio-Rad), to 2 volumes of the original sample (50 µL). Stacking gel (4%) and 12% resolving gel was used for SDS-PAGE gels (size: 17 cm long × 17 cm width × 0.75 mm thick). Technical (n ) 2) and biological replicate (n ) 3) gels were run for each salt concentration. The samples were loaded into wells and using the Protean II Multicell (BioRad) apparatus, they were run at 100 V for 40 min and 280 V for 4 h. Afterward, gels were washed with deionized water for 5 min. Gels were stained overnight with Coomassie Brilliant Blue G250 dye (Bio-Rad), and then destained for 3 h using deionized water. Gel lanes were incised into small bands in preparation for in-gel digestion as described previously.2 Gel pieces were dried in a vacuum concentrator at room temperature (Model 5301, Eppendorf, Cambridgeshire, UK) prior to digestion of proteins. Trypsin (20 µg/mL), with acetonitrile (20% v/v) was added to the gel pieces, which were incubated overnight at 37 °C. After overnight digestion, peptides were extracted as described previously, and then dried.2 Protein Identification with Peptide Fractions from SDS-PAGE. Peptide separation was accomplished on a PepMap C-18 RP capillary column (Famos, Switchos and Ultimate liquid chromatography system from Dionex/LC Packings, Amsterdam, Netherlands) interfaced to a QStar XL Hybrid ESI Quadrupole time-of-flight tandem mass spectrometer (Applied Biosystems, Framingham, MA; MDS- Sciex, Concord, Ontario, Canada). Chromatography and mass spectrometry settings were used as described previously.2 Information dependent acquisition (IDA) data was searched using Mascot Daemon (Matrix Science) version 2.1.3 in a sequence query type of search based on MS/MS spectra against the Synechocystis protein database (3264 ORF’s, retrieved from NCBI Refseq, March 2007). Search parameters included a 1.2 Da peptide tolerance and 0.6 Da MS/MS tolerance. One mis-cleavage of trypsin was allowed. Modifications were set as carbamidomethyl of cysteine (fixed), and oxidation of methionine (variable). Only high confidence Mascot identifications were considered further, where a MOWSE score of >38 is equal to a significant hit of p < 0.05.41 Protein Quantification Using MSQuant. Protein quantitation was achieved using MSQuant software (http://msquant. sourceforge.net/) with default settings for 15N labeling quantitation. This software requires raw data files from the mass spectrometer together with its search result file (HTML) generated using the Mascot Daemon search engine. It processes spectra and presents peptide quantitations which are further

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Comparative Proteomics Study of Salt Tolerance used to calculate protein quantitations. All quantitations were manually inspected. Proteins were only treated as significantly different in abundance between the species with fold changes of 2 or greater (see Metabolic Labeling Data Analysis). In addition, the detection of 2 or more peptides was a prerequisite for quantification significance (which ensured greater confidence in the accuracy of the ratio). Statistical evidence of quantitation was improved by also subjecting the same bands excised from two separate biological replicate gels (3 biological replicates in total) to the same workflow. iTRAQ Labeling and Peptide Fractionation. One-hundred micrograms of protein from (i) Euhalothece in 6% salt, (ii) Synechocystis in 6% salt and (iii) Synechocystis in 6% salt (a biological replicate culture of (ii)) were precipitated using TCA/ acetone. This was done by mixing the exact volume of protein solution corresponding to 100 µg of protein with 3 volumes of acetone with 5% TCA and 20 mM DTT, and incubating at -20 °C overnight. After incubation, the protein was recovered by centrifugation (21 000× g at 4 °C) for 30 min. The resulting pellet was washed with ice-cold acetone, and resuspended in 30 µL of 0.5 M triethylammonium bicarbonate (TEAB) buffer. Each sample was reduced, alkylated, digested with trypsin, and labeled with iTRAQ reagents according to the manufacturer’s (Applied Biosystems) protocol with modifications. iTRAQ tags 115, 116 and 117 were used for Synechocystis in 6% salt, Synechocystis in 6% salt (biological replicate) and Euhalothece in 6% salt, respectively. After labeling, samples were combined and dried in a vacuum contrifuge at room temperature. Fractionation of samples was achieved using strong cation exchange (SCX) as described previously.2 In brief, separation was performed on a BioLC HPLC unit (Dionex, Surrey, U.K.) using a PolySULFOETHYL A column (PolyLC, Columbia, MD) 5 µm of 200 mm length × 2.1 mm i.d. and 200 Å pore size. Buffer A and B consisted of 10 mM KH2PO4 and 25% acetonitrile at pH 3.0, and 10 mM KH2PO4, 25% acetonitrile, and 500 mM KCl at pH 3.0, respectively, both filter sterilized. The 60 min program commenced with 100% Buffer A for 5 min, 5 to 40% Buffer B for 40 min, 40 to 100% Buffer B for 5 min, 100% Buffer B for 5 min, and finally 100% Buffer A for 5 min. A flow rate of 0.2 mL/min was maintained with an injection volume of 180 µL. A UV detector UVD170U and Chromeleon Software v6.50 (Dionex/LC Packings, The Netherlands), was used to monitor the chromatogram as fractions were collected every minute using a Foxy Jr. Fraction Collector (Dionex). Pooling of fractions was performed depending on the peak intensities in the UV chromatogram and produced 20 samples. Samples were dried in a vacuum centrifuge and stored at -20 °C prior to mass spectrometric analysis. Protein Identification and Data Analysis Using iTRAQ. Dried peptide samples (20) were resuspended in aqueous Buffer I (3% acetonitrile, 0.1% formic acid) and injected into a capillary liquid chromatography system coupled to a QStar XL Hybrid ESI Q-TOF-MS/MS. Using a flow rate of 0.3 µL/min, peptides were separated on a PepMap C-18 RP capillary column with a gradient commencing with 97% Buffer I and 3% Buffer II (97% acetonitrile, 0.1% formic acid) for 5 min, followed by an increment in Buffer II from 3 to 30% for 120 min, and then up to 90% Buffer II for 7 min. Finally, a return to 97% Buffer I for 7 min. Data acquisition in the mass spectrometer was set to the positive ion mode, with a selected precursor mass range of 300-2000 m/z over an accumulation period of 1 s. Tandem fragmentation of 2 dynamically selected precursors was performed over an extended scan from 65-1600 over a 3 s

accumulation. An elevated collision energy range was also implemented to overcome the stabilizing effects of the isobaric tags.42 Tandem mass spectrometry was performed preferably on peptide ions with +2 and +3 charge states. Sample fractions were injected twice to increase coverage confidence in identification and quantification. A pooled list of acquisition data were analyzed for protein identification and quantitation using ProteinPilot Software v 2.0 (Applied Biosystems, MDS Sciex), which employs the Paragon Search algorithm.43 The search was performed using the Synechocystis protein database. Customised search parameter settings have been employed to include cysteine-fixed modification, mass tolerance and mis-cleavages. Protein identifications were accepted as positive with a probability filter cutoff of 95% (Prot Score of g2.0) and P-value. The false positive rate was calculated using a decoy interrogator search database.44-46 The decoy database strategy using reversed concatenated proteome sequences has been shown to allow an accurate estimation of the theoretical error associated with false determination rate (FDR) measurements.45 To achieve a rounded estimation for false positive determination, reversed proteomes of three cyanobacteria which are fully sequenced, including Synechocystis and two closest related to Euhalothece (based on 16S rRNA analysis, Nostoc punctiforme PCC73102, 78% and Nostoc sp. PCC7120, 75%) were employed for the estimation of FDR. Sequence reversal and concatenation was performed using a basic preprogrammed Perl script. FDR search was subsequently performed by reanalyzing mass spectrometry data files with identical parameters though ProteinPilot Software version 2.0.

Results and Discussion Growth Analysis. The growth rate of Euhalothece is slower than Synechocystis in all three salt concentrations (Figure 2). Therefore, to allow a meaningful comparison of protein abundance data, cell harvesting and protein extraction were performed in each species during the same growth phase when cell density (based on O.D.) was as similar as possible (Figure 2). The late-exponential growth phase (prior to phase 2 in Figure 3) was used. Figure 3 shows the response of how Euhalothece and Synechocystis cells tolerate the effects of high salt. During phase 2, complete pigment loss (chlorosis) occurs in the Synechocystis cells in 6% salt concentration and higher. The maximum salt concentration Synechocystis can tolerate is ca.7%,15 but the observations indicate that cells must be revived in fresh media to maintain survival. Conversely, Euhalothece cells can tolerate all five salt concentrations, but begin to show signs of chlorosis in 12% salt during midstationary phase (Figure 3, phase 3). The loss of green coloration is due to a decrease in chlorophyll levels, but these high salt conditions have also been associated with the production of carotenoids in extremely halotolerant cyanobacteria, and this could explain the yellow/brown pigmentation.2,47 Metabolic Labeling Data Analysis. The 15N incorporation efficiency was computed using three peptides from the highly abundant protein phycocyanin, and was found to be 98.05% ( 0.21. Consistency of isotope incorporation efficiency from protein to protein has been verified previously.48 The comparison of protein abundance across the species boundary, in three different salt concentrations, produced three data sets. These were denoted data set A (0% NaCl comparison), data set B (3% NaCl comparison) and data set C (6% NaCl Journal of Proteome Research • Vol. 8, No. 2, 2009 821

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Figure 2. Growth curves of Synechocystis (dashed lines), Euhalothece (solid lines). The lines represent averaged figures of three biological replicates grown in addition of 0%, 3% and 6% salt. Cells were harvested in the same growth phase, late-exponential (connector line). Inset: specific growth rates (d-1) for both organisms in the three different salt concentrations and total protein (mg/mL) extracted from cells at time indicated by indicator line.

comparison). Protein identifications were only treated as statistically relevant if 2 or more peptides per protein were identified and this meant a total of 203 unique proteins were confidently identified across all three conditions (summarized in Supporting Information). As for quantitations, 87, 67 and 93 proteins were quantified in data sets A, B and C, respectively. Many proteins were identified and quantified in more than one data set. There were 34, 30 and 22 proteins identified and quantified in both data sets A/B, B/C and A/C, respectively (Supporting Information). The number of membrane proteins identified was predicted using in silico tools PSortb v.2.049 and LipoP v.1.0,50 and totalled 22 proteins. Selected bands from two biological replicate gels for all three salt concentrations were subjected to the same work-flow and a comparison of protein quantitation data gave a log mean average coefficient of variation (CV) of 0.15 ( 0.05. Therefore, for sufficient statistical stringency, proteins with an increase in abundance (note here that up-regulation and down-regulation is inappropriate terminology in this cross-species quantitation study) by >2-fold only were considered as biologically relevant differences between the species. Using these criteria, 26, 16 and 17 proteins had significant differential abundance between the species in 0%, 3% and 6% salt, respectively (Table 2). Protein abundance differences are interpreted in respect to salt concentration and species differentiation in the section Protein Abundance Differences. In addition to the identification of multiple proteins per excised gel band, several proteins were identified in more than one excised gel band. In the majority of cases these bands were adjacent to each other, and therefore size separation was still evident, however, for situations where this was not the case, only proteins which corresponded to gel bands of the correct molecular weight range were reported. Protein positioning which did not correctly match theoretical molecular weight 822

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may be caused by damage or truncation. In all duplicate cases, quantitations were checked manually for consistency. When using tryptic peptides as identifiers for proteins, they are often of sufficient length to be used as a unique identifier for the protein, referred to as distinct peptides. However, in many cases they may not be distinct, and could be sourced from more than one protein and termed shared peptides. This makes it increasingly difficult to obtain meaningful quantitative data, and several studies have dealt with this protein inference problem with specific algorithms.51,52 Here, prefractionation using SDS-PAGE means protein size can be used as an additional identifier ensuring peptides are correctly assigned to a protein, and are unlikely to be shared peptides from a paralogous protein. It is important to note here that an unsequenced organism is a subject in this study, and it is not possible to elucidate whether peptides are shared or distinct within its proteome. Therefore avoiding misleading quantitations requires high confidence protein identifications. By way of example, inconsistent quantitations occurred once in the 3% salt gel, where ribulose biphosphate carboxylase was identified in two gel bands. The following three peptides were used for quantitation, LTYYTPDYTPK, TFQGPPHGITVER, and LEDIRFPVALIK, and different results were calculated in the different gel bands (0.35- and 1.22-fold changes). Although these peptides are not shared in Synechocystis and are sourced from ribulose biphosphate carboxylase only, it is not certain at this stage which is the relevant protein quantitation. Therefore, by elucidation of the molecular weight in the gel (∼50 kDa), we can see that the peptides quantified as 0.35fold difference (14N and 15N) are sourced from ribulose biphosphate carboxylase (∼53 kDa). In this study, peptides from orthologous proteins in different organisms are used for quantitation and these proteins will have different amino acid sequences. This will affect the

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research articles of false positive identifications was calculated using a well accepted methodology outlined by Elias et al.45 A reverse database was created using Synechocystis sp. PCC6803, Nostoc punctiforme PCC73102 and Nostoc sp. PCC7120 proteomes in order to assign a “decoy” to identify falsely determined spectra using the same algorithm. Twenty-four falsely determined spectra were identified using the decoy database above the confidence interval of 80%, and 10722 spectra were assigned to valid peptides above the same level of confidence interval in the “true” database search. Thus, as described by Elias et al.,45 the resulting false positive rate determined should be in the region of 0.4%. In an attempt to statistically relate our observations to biological variation in these systems, two biological replicate samples (115 and 116 tag labeled peptides) were used to estimate the CV, using parameters as described previously.2,53 The measure provided an average variation of 0.2 (20%). Upon estimating a measure for biological variation, there were 21 which were similarly determined to be significantly changed (>2 fold increase) in relative abundance across both biological replicates.

Figure 3. Effect of increasing salt concentration on the pigmentation of Euhalothece (left-hand side) and Synechocystis (righthand side) cells. Phase 1 – early exponential phase, phase 2 – late exponential phase, phase 3 – midstationary phase, phase 4 – late stationary/death phase. Star denotes where cell growth was not observed.

physicochemical properties of the protein, and hence potentially change the accessibility of trypsin to its cleavage sites, C-terminal to arginine and lysine. Ultimately this would lead to varying peptide intensities for the same protein and lead to large standard deviation values. This does not seem to pose a problem using the in-gel digestion strategy implemented here, because the standard deviations are low with an average 0.09 ( 0.10 across all proteins quantified. iTRAQ Data Analysis. An iTRAQ experiment was run with biological replicate cultures comparing Euhalothece and Synechocystis cells adapted to 6% salt. As noted, this was conducted first as an investigation to gauge the applicability of in vitro isobaric tagging on cross-species proteomics, and second as a corroborative technique to compare with findings obtained using an in vivo metabolic labeling technique. Naturally, the limitations on proteomic identifications and quantifications for metabolic labeled cross-species studies also apply for the iTRAQ technique, where quantitation of proteins across all three labels could only occur where peptides of identical sequences exist across both species of interest. Using dynamic filters for protein confidence at a cutoff of 95%, 207 unique proteins were reliably identified with tandem mass spectrometry. Psortb v2.0 and LipoP v1.0 identified 39 membrane proteins. A measure of false positive identifications is invaluable in the case of handling a large proteomic data set, hence the rate

Using iTRAQ labeling in cross species proteomics is successfully demonstrated here, with the identification (using sufficiently stringent criteria) and quantification of over 200 proteins. The overall success of similar cross-species proteomics studies using isobaric tags will always be dependent on the number of shared tryptic peptides between species, or between the unsequenced organism and sequences currently available in databases. These criteria equally apply to cross-species quantitation using metabolic labeling. An in silico analysis using shared tryptic peptides for cross-species quantitation was performed using related species in all three domains of life (archaea, eubacteria and eukaryotes).38 It revealed that approximately one-third of all proteins shared equal to or over 40% of peptides with at least one other protein in a related genus or species. This shows the strong application potential of this technique.38 This study is the first application of this method, to our knowledge, in comparing biological adaptation. Protein Abundance Differences. Differences in protein abundance were interpreted in three parts relating to the salt concentrations under investigation. 0% Salt. The greatest differences in protein abundance (26 proteins) quantified using metabolic labeling was observed in 0% salt (see Table 2). This was expected, because a previous study in Euhalothece showed that this organism potentially belongs to a unique cluster in its genera, and exhibits a “stress” response in no salt.2,35 In contrast, Synechocystis is essentially a freshwater cyanobacterium.15,39 Nine proteins were more abundant in Synechocystis (i.e., with a quantitation ratio 14 N/15N g 2), while 17 were more abundant in Euhalothece (i.e., with a quantitation ratio 14N/15N e 0.5) (Table 2). Five proteins involved in energy metabolism as well as energy synthesis were more abundant in Euhalothece. It is not certain why Euhalothece would require more energy than Synechocystis in these conditions, although it could be related to findings in a previous study,2 which demonstrated that Euhalothece displays a characteristic cyanobacterial stress response in these conditions. Three stress related proteins, hypothetical protein slr1894, rehydrin and methionine sulfoxide reductase A, were also more abundant in Euhalothece. The hypothetical protein slr1894 (2.03-fold more abundant in Euhalothece, i.e. with a quantitation ratio 14N/15N of 0.49 in Table 2) is a putative DNAbinding stress protein. Rehydrin (2.08-fold more abundant) Journal of Proteome Research • Vol. 8, No. 2, 2009 823

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Table 2. Significant Cross-Species Protein Abundance Differences Calculated Using Metabolic Labelling of Proteins (14N is Synechocystis and 15N is Euhalothece)a protein

gI number no. of peptides score quantitation

Abundance differences in 0% salt Glutamate-ammonia ligase 620123 2 96 0.35 Negative aliphatic amidase regulator 16331081 3 219 0.50 Transketolase 16329902 12 925 0.46 Phosphoglycerate kinase 2499503 13 915 0.44 Fructose- bisphosphate aldolase 16331386 4 280 0.21 Hypothetical protein slr1894 16329942 2 132 0.49 Hypothetical protein slr 1841 16330041 7 443 0.48 Hypothetical protein slr1762 16330280 3 200 0.47 Hypothetical protein sll0529 16332076 4 307 0.32 Rehydrin 16329971 2 139 0.48 Phosphoribulokinase 585368 3 213 0.24 Ribulose bisphosphate carboxylase SSU 16331394 2 157 0.24 Uracil phosphoribosyltransferase 16329360 2 128 0.44 SOS function regulatory protein 16330362 3 205 0.50 Methionine sulfoxide reductase A 16329407 7 343 0.50 Iron transport protein 16329434 4 224 0.29 Periplasmic iron-binding protein 16331793 7 455 0.25 Hypothetical protein sll0314 16331369 8 515 4.00 Phosphate binding periplasmic protein prec. 16331543 4 237 3.85 Superoxide dismutase 16330619 7 448 3.23 Elongation factor Tu 16330913 6 464 2.70 6-phosphogluconolactonase 2829619 3 161 2.56 Phycobilisome rod-core linker polypeptide 16329710 3 146 2.56 Hypothetical protein slr2018 16329857 4 263 2.44 Putative phosphoketolase 16332268 2 68 2.38 Hypothetical protein sll1785 16330236 3 182 2.17 Abundance differences in 3% salt PII protein 1403577 3 205 2.27 GDP-D-mannose dehydratase 16329177 3 156 2.44 Superoxide dismutase 16330619 5 289 2.04 Glycogen phosphorylase 16330178 5 253 3.33 Phosphoglucomutase 16332219 3 151 2.86 Fructose-1,6- biphosphatase 1753218 7 332 5.26 Hypothetical protein sll0529 16332076 8 386 2.22 Hypothetical protein slr0006 16331389 3 122 2.04 Hypothetical protein slr 1963 16330780 12 811 2.00 ATP synthase subunit B 16330679 9 683 7.14 ATP synthase subunit A 16329327 9 531 5.56 Phosphoribulokinase 16331050 12 702 2.08 Methionine sulfoxide reductase A 16331392 5 262 2.86 Phycocyanin beta subunit 1008532 9 613 0.44 Phycocyanin a subunit 16329823 5 374 0.38 Abundance differences in 6% salt molybopterin biosythesis protein MoeB 16331030 2 143 2.78 Protein confer. resistance to acetazolamide 16329873 4 227 2.70 Tryptophan synthase subunit beta 16332038 2 87 2.17 Phosphoglycerate dehydrogenase 16330470 5 243 2.17 Trigger factor 16332069 5 234 3.33 60kD chaperonin 2 16331442 20 1007 3.33 Molecular chaperone DnaK 16331261 7 314 2.22 Chaperonin GroEL 16330003 13 684 2.22 Transaldolase 16329404 5 253 2.56 Hypothetical protein slr0244 16329299 3 160 2.04 Ferredoxin-NAPH oxidoreductase 16331051 5 227 4.00 ATP synthase subunit A 16329327 6 252 3.45 Carbon dioxide concentrating mechanism prot. 16329366 6 250 3.03 Elongation factor Tu 16330913 17 1021 5.26 50S ribosomal protein L7/L12 16330008 2 119 3.33 ATP-dependent Clp protease regulatory SU 16331384 12 533 2.70 Elongation factor G 46483 10 492 2.08 a

N/15N

SD

functional category

0.41 0.1 0.21 0.25 0.24 0.6 0.19 0.06 0.3 0.11 0.12 0.42 0.08 0.03 0.07 0.4 0.24 0.01 0.02 0.04 0.04 0.01 0.03 0.06 0.04 0.04

Amino acid biosynthesis Amino acid trans. and metabolism Energy metabolism Energy metabolism Energy metabolism Hypothetical Hypothetical Hypothetical Hypothetical Other categories Photosynthesis and respiration Photosynthesis and respiration Purines/pyrimidines/nucleotides Regulatory Translation Transport and binding proteins Transport and binding proteins Hypothetical Transport and binding proteins Cellular processes Translation Energy metabolism Photosynthesis and respiration Hypothetical Hypothetical Hypothetical

0.02 0.06 0.03 0.15 0.09 0.02 0.02 0.16 0.05 0.07 0.06 0.09 0.08 0.24 0.44

Amino acid trans. and metabolism cell envelope Cellular processes Central interm. metabolism Central interm. metabolism Energy metabolism Hypothetical Hypothetical Hypothetical Photosynthesis and respiration Photosynthesis and respiration Photosynthesis and respiration Translation Photosynthesis and respiration Photosynthesis and respiration

0.01 0.01 0.04 0.02 0.04 0.05 0.08 0.05 0.01 0.06 0.14 0.04 0.06 0.03 0.02 0.11 0.03

Unknown Unknown Amino acid biosynthesis Amino acid biosynthesis Cellular processes Cellular processes Cellular processes Cellular processes Energy metabolism Hypothetical Photosynthesis and respiration Photosynthesis and respiration Photosynthesis and respiration Translation Translation Translation Translation 2

A quantitation ratio >2 or 2 or 2.0 was essential in Proteinpilot, as was a statistical significant MOWSE score (>38) using Mascot. Quantitations using both methods were verified manually and low intensity ions (2-fold, metabolic labeling appears to produce higher expression ratios (Figure 5). A previous study using both iTRAQ and cleavable isotope-coded affinity tags (cICAT) techniques to search for cancer markers, analogous to this study, found several proteins that were differentially expressed in cICAT, and also had a smaller than critical (2-fold) change by iTRAQ labeling.62 The direction of fold change (higher or lower relative abundance) was the same for 91.5% of proteins (see Supporting Information). In addition, both methods provide complementary information as to how Synechocystis and Euhalothece differ in their response to 6% salinity. Cumulatively, 18.1% and 11.3% of the proteins identified and quantified in the 6% salt comparison using metabolic labeling and iTRAQ respectively, were in higher abundance in Synechocystis. These included proteins involved in energy generation (photosynthesis and respiration), energy metabolism and protein synthesis (see 826

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Figure 5. Scatter plot of the relative protein abundance ratios of protein quantitations for the 47 spots identified and quantified in both methods (metabolic labeling and iTRAQ) in 6% salt. Dashed line denotes the ratio (i.e., fold ratio of 2) where differential protein abundance is deemed to be significant (refer to Metabolic Labeling Analysis and iTRAQ Data Analysis).

Tables 2 and 3). Conversely, none of the identified proteins were in higher abundance in Euhalothece using both methods.

Concluding Remarks A comparison of protein abundances in two contrasting cyanobacteria was undertaken by exploiting shared tryptic peptides. Low standard deviation calculations of isotope abundance differences imply in-gel digestion using trypsin was similar between orthologous proteins, and a log mean average CV across biological replicates of 0.15 ( 0.05 ensured high confidences in differentially abundant proteins which were identified. Although limited to proteins which are orthologous to both species, we were able to propose essential differences in cell behavior in three different salt concentrations. As comparisons were made in cells cultured in the same conditions, differences in protein abundance allowed a direct comparison of how

Comparative Proteomics Study of Salt Tolerance through evolutionary divergence, protein orthologs have altered activity with different amounts of gene expression, across the species barrier. For example, differences in the amount of molecular chaperones were used to postulate relative stress levels experienced. Euhalothece cells display stress characteristics in 0% salt, unlike Synechocystis cells. Salt concentrations of 3% and 6% lead to a similar stress response, but this time dominating in Synechocystis cells, although reduced energy synthesis was a mechanism associated with Euhalothece salt adapted cells previously.2 This makes it possible to assume that many salt adaptation strategies involving protein synthesis are shared by both cyanobacteria, and their quantitative expression affects acclimation. The results imply that lower salinity levels (3% and 6% NaCl) lead to these characteristic responses in Synechocystis cells, when compared to Euhalothece cells. It is probable that these differences have evolved at different times through evolution and could include alterations in gene control by transcription factors, differences in protein activity and functionality, changes in the way cells respond to external signals through adjustment of signal transduction and response regulators or even generation of paralogs of essential genes for salt acclimation.63,64 Ultimately, further work is required to understand exactly how Euhalothece cells have higher tolerance levels, more specifically, how this organism is able to survive in 6% salt by expressing salt adaptive protein coding genes at lower levels compared to its fresh water relative.

Acknowledgment. Funding was provided by The University of Sheffield, Biology Research Division Devolved Funds, and an EPSRC studentship. P.C.W. also thanks the EPSRC for the provision of an Advanced Research Fellowship (GR/A11311/01) and funding (GR/S84347/01 and EP/E036252/1). C.A.B. also acknowledges the EPSRC for the provision of an Advanced Research Fellowship (EP/E053556/ 01). We also acknowledge B. A. Ashhuby (University of Sheffield, [email protected]) for provision of Euhalothece sp. BAA001 cells. Supporting Information Available: Supplemental tables. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Rhoades, J. D.; Loveday, J. Salinity in irrigated agriculture. In Irrigation of agricultural crops; Stewart, B. A., Nielsen, D. R., Eds.; American Society of Agronomists: Madison, WI, 1990; pp 10891142. (2) Pandhal, J.; Snijders, A.; Biggs, C.; Wright, P. A cross-species quantitative proteomic study of salt adaptation in a halotolerant environmental isolate using 15N metabolic labelling. Proteomics 2008, 8 (11), 2266–84. (3) Allakhverdiev, S. I.; Nishiyama, Y.; Miyairi, S.; Yamamoto, H.; Inagaki, N.; Kanesaki, Y.; Murata, N. Salt stress inhibits the repair of photodamaged photosystem II by suppressing the transcription and translation of psbA genes in Synechocystis. Plant Physiol. 2002, 130 (3), 1443–1453. (4) Allakhverdiev, S. I.; Nishiyama, Y.; Suzuki, I.; Tasaka, Y.; Murata, N. Genetic engineering of the unsaturation of fatty acids in membrane lipids alters the tolerance of Synechocystis to salt stress. Proc. Natl. Acad. Sci. U.S.A. 1999, 96 (10), 5862–5867. (5) Allakhverdiev, S. I.; Sakamoto, A.; Nishiyama, Y.; Inaba, M.; Murata, N. Ionic and osmotic effects of NaCl-induced inactivation of photosystems I and II in Synechococcus sp. Plant Physiol. 2000, 123 (3), 1047–1056. (6) Apte, S. K.; Bhagwat, A. A. Salinity-stress-induced proteins in two nitrogen-fixing Anabaena strains differentially tolerant to salt. J. Bacteriol. 1989, 171 (2), 909–915. (7) Apte, S. K.; Haselkorn, R. Cloning of salinity stress-induced genes from the salt-tolerant nitrogen-fixing cyanobacterium Anabaena torulosa. Plant Mol. Biol. 1990, 15 (5), 723–733.

research articles (8) Blumwald, E.; Wolosin, J. M.; Packer, L. Na+/H+ exchange in the cyanobacterium Synechococcus 6311. Biochem. Biophys. Res. Commun. 1984, 122 (1), 452–459. (9) Bohnert, H. J.; Ayoubi, P.; Borchert, C.; Bressan, R. A.; Burnap, R. L.; Cushman, J. C.; Cushman, M. A.; Deyholos, M.; Galbraith, D. W.; Hasegawa, P. M.; Jenks, M.; Kawasaki, S.; Koiwa, H.; Kore-eda, S.; Lee, B. H.; Michalowski, C. B.; Misawa, E.; Nomura, M.; Ozturk, M.; Postier, B.; Prade, R.; Song, C. P.; Tanaka, Y.; Wang, H.; Zhu, J. K. A genomics approach towards salt stress tolerance. Plant Physiol. Biochem. 2001, 39, 295–311. (10) Deshnium, P.; Los, D. A.; Hayashi, H.; Mustardy, L.; Murata, N. Transformation of Synechococcus with a gene for choline oxidase enhances tolerance to salt stress. Plant Mol. Biol. 1995, 29 (5), 897– 907. (11) Gabbay-Azaria, R.; Tel-Or, E. Mechanisms of salt tolerance in cyanobacteria; CRC Press: Boca Raton, FL, 1993; pp 692-698. (12) Gao, X.; Ren, Z.; Zhao, Y.; Zhang, H. Overexpression of SOD2 Increases Salt Tolerance of Arabidopsis. Plant Physiol. 2003, 133 (4), 1873–1881. (13) Hagemann, M.; Erdmann, N. Activation and pathway of glucosylglycerol synthesis in the cyanobacterium Synechocystis sp. PCC 6803. Microbiology 1994, 140, 1427–1431. (14) Joset, F.; Jeanjean, R.; Hagemann, M. Dynamics of response of cyanobacteria to salt stress: deciphering the molecular events. Physiol. Plant 1996, 96, 738–744. (15) Reed, R. H.; Stewart, W. D. P. The responses of cyanobacteria to salt stress; Oxford Science Publisher: Oxford, 1988; pp 217-231. (16) Pandhal, J.; Biggs, C.; Wright, P. Proteomics with a pinch of salt: A cyanobacterial perspective. Saline Syst. 2008, 4, on line. (17) Echlin, P. The blue-green algae. Sci. Am. 1966, 214 (6), 75–81. (18) Kaneko, T.; Sato, S.; Kotani, H.; Tanaka, A.; Asamizu, E.; Nakamura, Y.; Miyajima, N.; Hirosawa, M.; Sugiura, M.; Sasamoto, S.; Kimura, T.; Hosouchi, T.; Matsuno, A.; Muraki, A.; Nakazaki, N.; Naruo, K.; Okumura, S.; Shimpo, S.; Takeuchi, C.; Wada, T.; Watanabe, A.; Yamada, M.; Yasuda, M.; Tabata, S. Sequence analysis of the genome of the unicellular cyanobacterium Synechocystis sp. strain PCC6803. II. Sequence determination of the entire genome and assignment of potential protein-coding regions (supplement). DNA Res. 1996, 3 (3), 185–209. (19) Hagemann, M.; Jeanjean, R.; Fulda, S.; Havaux, M.; Joset, F.; Erdmann, N. Flavodoxin accumulation contributes to enhanced cyclic electron flow around photosystem I in salt-stressed cells of Synechocystis sp. strain PCC 6803. Physiol. Plant 1999, 105 (4), 670– 678. (20) Elanskaya, I. V.; Karandashova, I. V.; Bogachev, A. V., H. Functional analysis of the Na+/H+ antiporter encoding genes of the cyanobacterium Synechocystis PCC 6803. Biochemistry (Moscow) 2002, 67 (4), 432–440. (21) Marin, K.; Huckauf, J.; Fulda, S.; Hagemann, M. Salt-dependent expression of glucosylglycerol-phosphate synthase, involved in osmolyte synthesis in the cyanobacterium Synechocystis sp. strain PCC 6803. J. Bacteriol. 2002, 184 (11), 2870–2877. (22) Miao, X.; Wu, Q.; Wu, G.; Zhao, N. Sucrose accumulation in saltstressed cells of agp gene deletion-mutant in cyanobacterium Synechocystis sp PCC 6803. FEMS Microbiol. Lett. 2003, 218 (1), 71–7. (23) Jeanjean, R.; Matthijs, H.; Onana, B.; Havaux, M.; Joset, F. Exposure of the cyanobacterium Synechocystis PCC6803 to salt stress induces concerted changes in respiration and photosynthesis. Plant Cell. Physiol. 1993, 34, 1073–1079. (24) Reed, R. H.; Stewart, W. D. P. Osmotic adjustment and organic solute accumulation in unicellular cyanobacteria from fresh-water and marine habitats. Marine Biol. 1985, 88, 1–9. (25) Roberts, M. F. Organic compatible solutes of halotolerant and halophilic microorganisms. Saline Syst. 2005, 1, 5. (26) Galinski, E. A. Osmoadaptation in bacteria. Adv. Microbiol. Physiol. 1995, 37, 272–328. (27) Huflejt, M. E.; Tremolieres, A.; Pineau, B.; Lang, J. K.; Hatheway, J.; Packer, L. Changes in membrane lipid composition during saline growth of the fresh water cyanobacterium Synechococcus 6311. Plant Physiol. 1990, 94, 1512–21. (28) Ritter, D.; Yopp, J. H. Plasma membrane lipid composition of the halophilic cyanobacterium Aphanothece halophytica. Arch. Microbiol. 1993, 159, 435–439. (29) Riviere, M. E.; Arrio, B.; Steffan, I.; Molitor, V.; Kuntner, O.; Peschek, G. A. Changes of some physical properties of isolated and purified plasma and thylakoid membrane vesicles from the freshwater cyanobacterium Synechococcus sp. 6301 (Anacystis nidulans) during adaptation to salinity. Arch. Biochem. Biophys. 1990, 280 (1), 159–66.

Journal of Proteome Research • Vol. 8, No. 2, 2009 827

research articles (30) Allakhverdiev, S. I.; Kinoshita, M.; Inaba, M.; Suzuki, I.; Murata, N. Unsaturated fatty acids in membrane lipids protect the photosynthetic machinery against salt-induced damage in Synechococcus. Plant Physiol. 2001, 125 (4), 1842–1853. (31) Fulda, S.; Huang, F.; Nilsson, F.; Hagemann, M.; Norling, B. Proteomics of salt effects on Synechocystis sp. PCC6803: Identification of periplasmic proteins in cells grown at low and high salt concentrations. Eur. J. Biochem. 2000, 267, 5900–5907. (32) Fulda, S.; Mikkat, S.; Huang, F.; Huckauf, J.; Marin, K.; Norling, B.; Hagemann, M. Proteome analysis of salt stress response in the cyanobacterium Synechocystis sp. strain PCC 6803. Proteomics 2006, 6 (9), 2733–2745. (33) Huang, F.; Fulda, S.; Hagemann, M.; Norling, B. Proteomic screening of salt-stress-induced changes in plasma membranes of Synechocystis sp. strain PCC 6803. Proteomics 2006, 6 (3), 910– 20. (34) Sudhir, P. R.; Pogoryelov, D.; Kovacs, L.; Garab, G.; Murthy, S. D. The effects of salt stress on photosynthetic electron transport and thylakoid membrane proteins in the cyanobacterium Spirulina platensis. J. Biochem. Mol. Biol. 2005, 38 (4), 481–5. (35) Ashhuby, B. A. Biofouling studies on reverse osmosis desalination of hypersaline waters. PhD Thesis, University of Sheffield, Sheffield, 2007. (36) Taji, T.; Seki, M.; Satou, M.; Sakurai, T.; Kobayashi, M.; Ishiyama, K.; Narusaka, Y.; Narusaka, M.; Zhu, J. K.; Shinozaki, K. Comparative genomics in salt tolerance between Arabidopsis and Arabidopsis-related halophyte salt cress using Arabidopsis microarray. Plant Physiol. 2004, 135 (3), 1697–709. (37) Zhu, J. K. Plant salt tolerance. Trends Plant Sci. 2001, 6 (2), 66–71. (38) Snijders, A. P.; de Koning, B.; Wright, P. C. Relative quantification of proteins across the species boundary through the use of shared peptides. J. Proteome Res. 2007, 6 (1), 97–104. (39) Rippka, R.; Deruelles, J.; Waterbury, J. Generic assignments, strain histories and properties of pure cultures of cyanobacteria. J. Gen. Microbiol. 1979, 111 (1), 1–61. (40) Torrecilla, I.; Leganes, F.; Bonilla, I.; Fernandez-pinaz, F. Calcium transients in response to salinity and osmotic stress in the nitrogen-fixing cyanobacterium Anabaena sp.PCC7120, expressing cytosolic apoaequorin. Plant, Cell Environ. 2001, 24, 641–648. (41) Perkins, D. N.; Pappin, D. J. C.; Creasy, D. M.; Cottrell, J. S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999, 20 (18), 3551–3567. (42) Ow, S. Y.; Cardona, T.; Taton, A.; Magnuson, A.; Lindblad, P.; Stensjo, K.; Wright, P. C. Quantitative shotgun proteomics of enriched heterocysts from Nostoc sp. PCC 7120 using 8-Plex isobaric peptide tags. J. Proteome Res. 2008, 7 (4), 1615–1628. (43) Shilov, I. V.; Seymour, S. L.; Patel, A. A.; Loboda, A.; Tang, W. H.; Keating, S. P.; Hunter, C. L.; Nuwaysir, L. M.; Schaeffer, D. A. The Paragon algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra. Mol. Cell. Proteomics 2007, 6 (9), 1638–1655. (44) Oh, J.-E.; Hong, S.-W.; Lee, Y.; Koh, E.-J.; Kim, K.; Seo, Y. W.; Chung, N.; Jeong, M.; Jang, C. S.; Lee, B.; Kim, K. H.; Lee, H. Modulation of gene expressions and enzyme activities of methionine sulfoxide reductases by cold, ABA or high salt treatments in Arabidopsis. Plant Sci. 2005, 169 (6), 1030–1036. (45) Elias, J. E.; Gygi, S. P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 2007, 4 (3), 207–14. (46) Elias, J. E.; Haas, W.; Faherty, B. K.; Gygi, S. P. Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations. Nat. Methods 2005, 2 (9), 667–75. (47) Garcia-Pichel, F.; Nubel, U.; Muyzer, G. The phylogeny of unicellular, extremely halotolerant cyanobacteria. Arch. Microbiol. 1998, 169 (6), 469–82.

828

Journal of Proteome Research • Vol. 8, No. 2, 2009

Pandhal et al. (48) Snijders, A. P.; de Vos, M. G.; Wright, P. C. Novel approach for peptide quantitation and sequencing based on 15N and 13C metabolic labeling. J. Proteome Res. 2005, 4 (2), 578–85. (49) Gardy, J. L.; Laird, M. R.; Chen, F.; Rey, S.; Walsh, C. J.; Ester, M.; Brinkman, F. S. PSORTb v.2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis. Bioinformatics 2005, 21 (5), 617–23. (50) Juncker, A. S.; Willenbrock, H.; Von Heijne, G.; Brunak, S.; Nielsen, H.; Krogh, A. Prediction of lipoprotein signal peptides in Gramnegative bacteria. Protein Sci. 2003, 12 (8), 1652–62. (51) Nesvizhskii, A. I.; Aebersold, R. Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell. Proteomics 2005, 4 (10), 1419–40. (52) Nesvizhskii, A. I.; Keller, A.; Kolker, E.; Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 2003, 75 (17), 4646–58. (53) Chong, P. K.; Gan, C. S.; Pham, T. K.; Wright, P. C. Isobaric tags for relative and absolute quantitation (iTRAQ) reproducibility: Implication of multiple injections. J. Proteome Res. 2006, 5 (5), 1232–40. (54) Gibson, J. L.; Chen, J. H.; Tower, P. A.; Tabita, F. R. The form II fructose 1,6-bisphosphatase and phosphoribulokinase genes form part of a large operon in Rhodobacter sphaeroides: primary structure and insertional mutagenesis analysis. Biochemistry 1990, 29 (35), 8085–93. (55) Kneidinger, B.; Graninger, M.; Adam, G.; Puchberger, M.; Kosma, P.; Zayni, S.; Messner, P. Identification of two GDP-6-deoxy-Dlyxo-4-hexulose reductases synthesizing GDP-D-rhamnose in Aneurinibacillus thermoaerophilus L420-91T. J. Biol. Chem. 2001, 276 (8), 5577–83. (56) Zuther, E.; Schubert, H.; Hagemann, M. Mutation of a gene encoding a putative glycoprotease leads to reduced salt tolerance, altered pigmentation, and cyanophycin accumulation in the cyanobacterium Synechocystis sp. strain PCC 6803. J. Bacteriol. 1998, 180 (7), 1715–22. (57) Shoumskaya, M. A.; Paithoonrangsarid, K.; Kanesaki, Y.; Los, D. A.; Zinchenko, V. V.; Tanticharoen, M.; Suzuki, I.; Murata, N. Identical Hik-Rre systems are involved in perception and transduction of salt signals and hyperosmotic signals but regulate the expression of individual genes to different extents in synechocystis. J. Biol. Chem. 2005, 280 (22), 21531–8. (58) Schubert, H.; Fulda, S.; , M., H. Effects of adaptation to different salt concentrations on photosynthesis and pigmentation of the cyanobacterium Synechocystis sp. PCC 6803. J. Plant Physiol. 1993, 142, 291–295. (59) Fischer, G.; Schmid, F. X. The mechanism of protein folding. Implications of in vitro refolding models for de novo protein folding and translocation in the cell. Biochemistry 1990, 29 (9), 2205–2212. (60) Wu, W. W.; Wang, G.; Baek, S. J.; Shen, R. F. Comparative study of three proteomic quantitative methods, DIGE, cICAT, and iTRAQ, using 2D Gel- or LC-MALDI TOF/TOF. J. Proteome Res. 2006, 5 (3), 651–658. (61) Gan, C. S.; Reardon, K. F.; Wright, P. C. Comparison of protein and peptide prefractionation methods for the shotgun proteomic analysis of Synechocystis sp. PCC 6803. Proteomics 2005, 5 (9), 2468–78. (62) DeSouza, L.; Diehl, G.; Rodrigues, M. J.; Guo, J.; Romaschin, A. D.; Colgan, T. J.; Siu, K. W. Search for cancer markers from endometrial tissues using differentially labeled tags iTRAQ and cICAT with multidimensional liquid chromatography and tandem mass spectrometry. J. Proteome Res. 2005, 4 (2), 377–86. (63) Lespinet, O.; Wolf, Y. I.; Koonin, E. V.; Aravind, L. The role of lineage-specific gene family expansion in the evolution of eukaryotes. Genome Res. 2002, 12 (7), 1048–59. (64) Madlung, A.; Comai, L. The effect of stress on genome regulation and structure. Ann. Bot. (London) 2004, 94 (4), 481–95.

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