MS Proteomics Reveals Metabolic

Article pubs.acs.org/jpr

Quantitative iTRAQ LC−MS/MS Proteomics Reveals Metabolic Responses to Biofuel Ethanol in Cyanobacterial Synechocystis sp. PCC 6803 Jianjun Qiao,†,‡ Jiangxin Wang,†,‡ Lei Chen,†,‡ Xiaoxu Tian,†,‡ Siqiang Huang,†,‡ Xiaoyue Ren,†,‡ and Weiwen Zhang*,†,‡ †

School of Chemical Engineering & Technology, Tianjin University, Tianjin 300072, P.R. China Key Laboratory of Systems Bioengineering, Ministry of Education of China, Tianjin 300072, P.R. China

‡

S Supporting Information *

ABSTRACT: Recent progress in metabolic engineering has led to autotrophic production of ethanol in various cyanobacterial hosts. However, cyanobacteria are known to be sensitive to ethanol, which restricts further efforts to increase ethanol production levels in these renewable host systems. To understand the mechanisms of ethanol tolerance so that engineering more robust cyanobacterial hosts can be possible, in this study, the responses of model cyanobacterial Synechocystis sp. PCC 6803 to ethanol were determined using a quantitative proteomics approach with iTRAQ LC−MS/ MS technologies. The resulting high-quality proteomic data set consisted of 24 887 unique peptides corresponding to 1509 identified proteins, a coverage of approximately 42% of the predicted proteins in the Synechocystis genome. Using a cutoff of 1.5-fold change and a p-value less than 0.05, 135 and 293 unique proteins with differential abundance levels were identified between control and ethanol-treated samples at 24 and 48 h, respectively. Functional analysis showed that the Synechocystis cells employed a combination of induced common stress response, modifications of cell membrane and envelope, and induction of multiple transporters and cell mobility-related proteins as protection mechanisms against ethanol toxicity. Interestingly, our proteomic analysis revealed that proteins related to multiple aspects of photosynthesis were up-regulated in the ethanol-treated Synechocystis cells, consistent with increased chlorophyll a concentration in the cells upon ethanol exposure. The study provided the first comprehensive view of the complicated molecular mechanisms against ethanol stress and also provided a list of potential gene targets for further engineering ethanol tolerance in Synechocystis PCC 6803. KEYWORDS: ethanol, tolerance, iTRAQ proteomics, Synechocystis

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INTRODUCTION High energy prices, increasing energy imports, and greater recognition of the environmental consequences of fossil fuels have driven interest in large-scale substitution of petroleumbased fuels by renewable biofuels.1−3 Ethanol currently constitutes 90% of all biofuels in the United States. E-10 Unleaded, a blend of 10% ethanol and 90% ordinary gasoline, has been used in the U.S. for more than 25 years. More than 1.2 billion bushels of corn were converted to ethanol in the 2003− 2004 production year.4 The 3.4 billion gallons of ethanol blended into gasoline in 2004 amounted to about 2% of all gasoline sold by volume and 1.3% (2.5 × 1017 J) of its energy content.5 Fermentation production of ethanol using microbes such as yeast Saccharomyces cerevisiae and bacterium Zymomonas mobiliz has been by far the largest-scale microbial process.6 The process has seen significant progress in the past decades: inhibitor sensitivity, product tolerance, ethanol yield, and specific ethanol productivity have been improved in modern industrial strains to the degree that up to 20% (v/v) of ethanol can be produced in industrial yeast fermentation vessels from starch-derived glucose.7 © 2012 American Chemical Society

However, the increasing production of ethanol directly from agricultural crops would require diverting farmland and crops for biofuel production, competing with world food supply and causing economic and ethical problems. Also, cultivating food crops for biofuel production consumes large amounts of water, fertilizers, and pesticides, which burden the environment,8−10 and the commercial-scale production of ethanol from lignocellulosic raw materials still requires significant technologic breakthroughs to make the process economically feasible.11 Cyanobacteria are autotrophic prokaryotes which can perform oxygenic photosynthesis, similar to that performed by higher plants.12−14 As the cyanobacteria have simple growth requirements, abilities to grow to high densities, and use light, carbon dioxide, and other inorganic nutrients efficiently, they could be attractive hosts for the production of valuable industrial products.14,15 Moreover, cyanobacteria have a relatively simple genetic background and are easy to genetically modify.16 Several Received: June 6, 2012 Published: October 12, 2012 5286

dx.doi.org/10.1021/pr300504w | J. Proteome Res. 2012, 11, 5286−5300

Journal of Proteome Research

Article

dozen cyanobacterial genomes have been sequenced,17,18 which makes them accessible by various high-throughput postgenomics and synthetic biology technologies.19 By expressing a bacterial pyruvate decarboxylase (pdc) and alcohol dehydrogenase (adh) from the bacterium Z. mobiliz in the cyanobacterium Synechococcus sp. PCC 7942, the recombinant microorganism produced up to 230 mg/L of ethanol directly from CO2 within 4 weeks of growth.20 More recently, a genome-scale Synechocystis sp. PCC 6803 metabolic network model was used to improve cyanobacterial ethanol production up to 690 mg/L in a week.21 These studies clearly demonstrated that photoautotrophic cyanobacteria could potentially be engineered for a direct conversion of solar energy and CO2 into biofuel products such as ethanol. Ethanol is known to be highly toxic to their native producers (e.g., yeast and Z. mobiliz) or engineered hosts (e.g., Escherichia coli).22 Application of ethanol-tolerant strains obtained from mutant selection has been demonstrated as a very efficient way to increase the ethanol production in industry.23−25 More recently, based upon an increasing knowledge of ethanol tolerance mechanisms, successful studies employing more directed metabolic and genome engineering approaches were also conducted in yeast to improve its ethanol tolerance and production.26−28 However, the tolerance level of cyanobacteria to ethanol is very low, far below those values calculated for costand energy-efficient distillation processes for product recovery, which is minimally about 40−50 g/L for ethanol.29 As such, it is difficult to produce ethanol in the photosynthetic cyanobacterial hosts at the titers needed for economic efficiency. Currently, very little information is available on effects of ethanol on photosynthetic microbes. Although a study has been conducted using leaves of the sugar beet (Beta vulgaris var. Saccharifera (Alef) Krass.) sprayed with 40% methanol, the results indicated that a decrease in the negative impact of water deficiency in experimental plants was due to the larger stress resistance of the photosynthetic apparatus, higher rate of photosynthesis, and more effective use of water.30 Recently several proteomic studies of cyanobacterium under various environmental stresses, such as UV light, salt, and cold temperature, have been conducted, revealing the reveal metabolic strategies utilized by cyanobacteria against diverse environmental perturbations.31−34 Although some of the metabolic responses cased by environmental stress could be similar to that caused by ethanol, the unique effects on cyanobacteria caused by ethanol remain unknown. Recent genome-level studies have also showed that microbes tend to employ multiple resistance mechanisms in dealing with the stress of a single biofuel product.22,35,36 For example, in E. coil a fitness profiling was used to measure the consequences of single-locus perturbations in the context of ethanol exposure, and then a module-level computational analysis was conducted to reveal the organization of the contributing loci into cellular processes and regulatory pathways whose modifications significantly affect ethanol tolerance. The results found that a dominant component of adaptation involves metabolic rewiring that boosts intracellular ethanol degradation and assimilation.37 The results also indicated that to uncover the complicated and synergistic tolerance mechanisms that microbes, including cyanobacteria, utilize to deal with ethanol resistance globalbased approaches need to be implemented. In this respect, we applied here a quantitative proteomics approach with isobaric tag for relative and absolute quantification (iTRAQ) technique and liquid chromatography−tandem mass spectrometry (LC−MS/ MS) to fully reveal metabolic response to ethanol in model

cyanobacterial Synechocystis sp. PCC 6803.38,39 The results showed that the Synechocystis cells employed a combination of induced common stress response, modifications of cell membrane and envelope, and induction of multiple transporters as major protection mechanisms against ethanol toxicity. Interestingly, our proteomic analysis revealed that proteins related to multiple aspects of photosynthetic function were upregulated, in contrast with the slow growth of cells under ethanol exposure.

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METHODS AND MATERIALS

Bacterial Growth Conditions and Ethanol Treatment

Synechocystis sp. PCC 6803 was obtained from American Type Culture Collection (ATCC) and grown in BG11 medium (pH 7.5) under a light intensity of approximately 50 μmol photons m−2 s−1 in an illuminating incubator of 130 rpm at 30 °C (HNY211B Illuminating Shaker, Honor, China).40,41 Cell density was measured on a UV-1750 spectrophotometer (Shimadzu, Japan). For growth and ethanol treatment, 10 mL of fresh cells at OD730 of 0.5 was collected by centrifugation and then inoculated into 50 mL of BG11 liquid medium in a 250 mL flask. Ethanol of varying concentration was added at the beginning of cultivation. Culture samples (1 mL) were taken and measured (OD730) every 12 h. Morphology of Synechocystis sp. PCC6803 control and ethanoltreated samples was observed using a BX43 fluorescence microscope (Olympus, Japan). Ethanol of analytical pure was purchased from Merck (U.S.A). Growth experiments were repeated at least three times to confirm the growth patterns. Cells for proteomics analysis were collected by centrifugation at 8000g for 10 min at 4 °C. Protein Preparation and Digestion

For each sample, 10 mg of cells was frozen by liquid nitrogen immediately after centrifugation and washed with phosphate buffer (pH 7.2). The cells were broken with sonication cracking at low temperature. Cell pellets were then resuspended in a lysis buffer (8 M urea, 4% CHAPS, 40 mM Tris-HCl), with 1 mM PMSF and 2 mM ethylenediaminetetraacetic acid (EDTA) (final concentration). After 5 min of vigorous vortex, dithiothreitol (DTT) was also added to a final concentration of 10 mM. After mixing, the sample were centrifuged for 20 min at 20 000g, and the supernatant was mixed well with ice-cold acetone (1:4, v/v) with 30 mM DTT. After repeating this step twice, supernatants were combined and precipitated at −20 °C overnight and stored at −80 °C prior to sample cleanup if not for immediate use. For digestion, the protein pellet from the previous step was resuspended in digestion buffer (100 mM triethylammonium bicarbonate (TEAB), 0.05% w/v sodium dodecyl sulfate (SDS)) to a final concentration of 1 mg/mL (total protein measured by bicinchonic acid assay (Sigma, St. Louis, MO)). Equal aliquots (500 μg) from each lysate were then digested with trypsin overnight at 37 °C (Sigma; 1:40 w/w added at 0 and 2 h) and lyophilized. iTRAQ Labeling

The iTRAQ labeling of peptide samples derived from control and ethanol-treated conditions was performed using an iTRAQ Reagent 8-plex kit (Applied Biosystems, Foster City, CA) according to the manufacturer’s protocol. For each time point (i.e., 24 or 48 h), four samples (two biological replicates for control and two biological replicates for ethanol treatment, respectively) were iTRAQ labeled: 113-, 114-, 115-, and 116iTRAQ tags for control replicate 1 and 2 at 24 h and control 5287



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FTMS; Ms order, MS2; activation type, HCD; scan type, full; ionization source, nanospray; polarity mode, +. For peak filters: S/N threshold (FT-only), 0. For replacements for unrecognized properties: unrecognized charge, automatic; unrecognized mass analysis, FTMS; unrecognized MS order, MS2; unrecognized activation, HCD; unrecognized polarity Re, +. The data acquisition was performed with Analyst QS 2.0 software (Applied Biosystems/MDS SCIEX). Protein identification and quantification were performed using Mascot 2.3.02 (Matrix Science, London, United Kingdom).44 For iTRAQ quantification, the peptide for quantification was automatically selected by the algorithm to calculate the reporter peak area, error factor (EF), and p-value (default parameters in Mascot Software package). The resulting data set was auto bias-corrected to get rid of any variations imparted due to the unequal mixing during combining different labeled samples. Genome sequence and annotation information of Synechocystis sp. PCC 6803 were downloaded from NCBI and the Comprehensive Microbial Resource (CMR) of TIGR (http://www.tigr.org/CMR) (April 22, 2012).38 Proteins with 1.5-fold or more change between ethanol-treated and control samples and the p-value of statistical evaluation less than 0.05 were determined as differentially expressed proteins. The quantitation was performed at the peptide level by following the procedures described in http:// www.matrixscience.com/help/quant_statistics_help.html. The students t test was performed using the Mascot 2.3.02 software. Metabolic pathway analysis of the identified proteins was conducted according to the KEGG Pathway Database. COG (Cluster of Orthologous Groups of proteins) analysis (http:// www.geneontology.org) was conducted according to the early literature.45

replicate 1 and 2 at 48 h, respectively; and 117-, 118-, 119-, and 121-iTRAQ tags for ethanol-treated replicate 1 and 2 at 24 h and control replicate 1 and 2 at 48 h, respectively. The peptides were labeled with respective isobaric tags, incubated for 2 h, and vacuum centrifuged to dryness. The labeled control and ethanol treatment replicate samples at each time point were 1:1 pooled and generated four combinations of samples for each time point (i.e., 113 vs 117, 113 vs 118, 114 vs 117, and 114 vs 118 for 24 h; 115 vs 119, 115 vs 121, 116 vs 119, 116 vs 121 for 48 h), which were reconstituted in buffer A (10 mM KH2PO4, 25% acetonitrile, pH 2.85). The iTRAQ labeled peptides were fractionated using a PolySULFOETHYL ATM SCX column (200 × 4.6 mm, 5 μm particle size, 200 Å pore size) by an HPLC system (Shimadzu, Japan) at a flow rate of 1.0 mL/min. The 50 min HPLC gradient consisted of 100% buffer A (10 mM KH2PO4, 25% acetonitrile, pH 2.85) for 5 min; 0−20% buffer B (10 mM KH2PO4, 25% ACN, 500 mM KCL, pH 3.0) for 15 min; 20−40% buffer B for 10 min; 40−100% buffer B for 5 min followed by 100% buffer A for 10 min. The chromatograms were recorded at 218 nm. The collected fractions were desalted with Sep-Pak Vac C18 cartridges (Waters, Milford, Massachusetts), concentrated to dryness using a vacuum centrifuge, and reconstituted in 0.1% formic acid for LC−MS/MS analysis. LC−MS/MS Proteomic Analysis

The mass spectroscopy analysis was performed using an AB SCIEX TripleTOF 5600 mass spectrometer (AB SCIEX, Framingham, MA, USA), coupled with an online micro flow HPLC system (Shimadzu, JAPAN) as described before.42,43 The peptides were separated using a nanobored C18 column with a picofrit nanospray tip (75 μm ID × 15 cm, 5 μm particles) (New Objectives, Wubrun, MA). The separation was performed at a constant flow rate of 20 μL min−1, with a splitter to get an effective flow rate of 0.2 μL min−1. The mass spectrometer data were acquired in the positive ion mode, with a selected mass range of 300−2000 m/z. Peptides with +2 to +4 charge states were selected for MS/MS. The three most abundantly charged peptides above a count threshold were selected for MS/MS and dynamically excluded for 30 s with ±30 mDa mass tolerance. Smart information-dependent acquisition (IDA) was activated with automatic collision energy and automatic MS/MS accumulation. The fragment intensity multiplier was set to 20, and the maximum accumulation time was 2 s. The peak areas of the iTRAQ reporter ions reflect the relative abundance of the proteins in the samples. For peptide identification, a Triple TOF 5600 mass spectrometer used in this study has high mass accuracy (less than 2 ppm). Other identification parameters used included: fragment mass tolerance, ±0.1 Da; mass values, monoisotopic; variable modifications, Gln → pyro-Glu (N-term Q), oxidation (M), iTRAQ8plex (Y); Ppptide mass tolerance, 0.05 Da; max. missed cleavages, 1; fixed modifications, carbamidomethyl (C), iTRAQ8plex (N-term), iTRAQ8plex (K); other parameters, default.

Flow Cytometric Analysis

To reveal cell aggregation, flow cytometric analysis was performed on a FACS Calibur fluorescence-activated cell sorting (FACS) cytometer (Becton Dickinson) with the following settings: forward scatter (FCS), E00 log; side scatter, 500 V. Control and ethanol-treated cells were harvested at 0, 24, 48, and 72 h, respectively, washed twice with phosphate buffer (pH 7.2) (Sigma-Aldrich), and then resuspended in the same phosphate buffer to a final OD580 of 0.3 (approximately 1.5 × 107 cells mL−1). A total of 104 cells were used for each analysis according to the method by Marbouty et al. (2009).46 Data analysis was conducted using the CellQuest software, version 3.1 (Becton Dickinson). Quantification of Chlorophyll a

Control and ethanol-treated Synechocystis cultures (10 mL) were harvested at 24 and 48 h by centrifugation at 3500g for 10 min at 4 °C. The cells were then resuspended with 5 mL of methanol for chlorophyll a extraction according to the early literature.47 The process was conducted under dark and on ice. Cell fragments were spin down by centrifugation at 6500g for 5 min at 4 °C. The supernatants were collected and measured for absorption using a UV-1750 spectrophotometer (Shimadzu, Japan) at 665 and 750 nm. Chlorophyll a concentration was calculated according to the formula as follows

Proteomic Data Analysis

The MS data were processed using Proteome Discoverer software (Version 1.2.0.208) (Thermo Scientific) to generate a peak list. The default parameters of Proteome Discoverer software (Version 1.2.0.208) were used. In detail, they were: For precursor selection, we used the MS1 precursor. For spectrum properties filter: lower rt limit, 0; upper rt limit, 0; lowest charge state, 0; highest charge state, 0; min. precursor mass, 0; max. precursor mass, 300 Da; total intensity threshold, 10 000 Da; minimum peak count, 1. For scan event filters: mass analyzer,

[Chl](μg/mL) = 13.42(A665 − A750)Vmethanol /Vsample

where A665 and A750 represent absorption at 665 and 750 nm, respectively. Vsample is 10 mL, and Vmethanol is 5 mL in this study. 5288



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Figure 1. Effects of ethanol. (A) Growth time courses with varying concentration of ethanol. (B) Phenotype observation under microscope (40×) at 0, 24, and 48 h, respectively; scale bars of 10 μm are indicated. C represents Control and E represents Ethanol. (C) Flow cytometric analysis at 0, 24, 48, and 72 h, respectively. Cells were treated with 1.5% ethanol.

Figure 2. (A) Distribution of proteins identified among different molecular weights. (B) Coverage of proteins by the identified peptides. (C) Coverage of the identified proteins among four biological replicates. (D) COG coverage of the proteins detected.

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RESULTS AND DISCUSSION

appropriate ethanol concentration for proteomic studies (Figure 1A). The results showed that the concentration of ethanol that caused a 50% growth decrease was found to be 1.50% (v/v) at 24 h (corresponding to middle-exponential phase) and was selected

1. Ethanol Effect on PCC 6803

The growth of Synechocystis PCC 6803 supplemented with 0, 1.25, 1.50, and 2.00% ethanol was assessed to determine an 5289



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Figure 3. (I) Repeatability between biological replicates. Ethanol-treated biological replicates at 24 h (A) and 48 h (B), respectively. (II) Distribution of iTRAQ log ratios of the 1169 and 1162 proteins identified at 24 h (A) and 48 h (B) among four biological replicates, respectively. The four sets of biological replicates at 24 h were Ethanol-24h-r1 vs Control-24h-r1, Ethanol-24h-r2 vs Control-24h-r1, Ethanol-24h-r1 vs Control-24h-r2, and Ethanol24h-r2 vs Control-24h-r2, indicated by different colors. The four sets of biological replicates at 48 h were Ethanol-48h-r1 vs Control-48h-r1, Ethanol48h-r2 vs Control-48h-r1, Ethanol-48h-r1 vs Control-48h-r2, and Ethanol-48h-r2 vs Control-48h-r2, indicated by different colors.

for the analysis in this study. The tolerance level of Synechocystis PCC 6803 to ethanol was found to be significantly lower than yeast S. cerevisiae, for which 25% ethanol has been reported to cause inhibition.48 No evaporation of ethanol was observed during the testing time course. Cell morphology under ethanoltreated and control conditions was compared under microscope, and the results showed that visible aggregation of a large number of cells was found after 24 h treatment even at a concentration of 1.50%, compared with the clearly individual cells in the control (Figure 1B). Flow cytometric analysis of ethanol-treated cells also confirmed that aggregation (Figure 1C). The resulting twoparameter histograms of forward light scatter and fluorescence intensity showed very concentrated clusters for control cells, but for ethanol-treated cells, even at 24 h after treatment, cell size changes were clearly observed on the plots (Figure 1C). For proteomic analysis, two independent cultivations for both control (no ethanol) and 1.5% ethanol-treated experiments were conducted, and cells were collected by centrifugation (8000g for 10 min at 4 °C) at 24 and 48 h, resulting in two biological replicates for each time point of control and ethanol-treated samples. The time points of sampling corresponded to middleexponential and exponential-stationary transition phases of the cell growth, respectively (Figure 1A).

coverage (averages of 30−45% of the total proteins in each MW group) was obtained for a wide MW range for proteins larger than 10 kDa (Figure 2A). In addition, most of the proteins were identified with good peptide coverage, of which 64% of the proteins were with more than 10% of the sequence coverage and 42% were with 20% of the sequence coverage (Figure 2B). Among all the proteins detected, 1201 and 1197 were identified from samples of 24 and 48 h, respectively. As an index for protein identification confidence, more than 94% and 96% of the proteins were detected in all four biological samples for 24 and 48 h, respectively (Figure 2C), suggesting the analysis is reliable. Functional classification of the proteins identified showed that they were found in almost every aspect of Synechocystis metabolism. On the basis of the number of unique proteins identified in each functional category, the most frequently detected functional categories were “signal transduction mechanisms” and “general function prediction only”, each representing more than 10% of all the proteins identified (Figure 2D). Proteins involved in the signal transduction network are generally with low abundance and quick protein turnover time in bacteria.49 High identification coverage of the group of “signal transduction mechanisms” suggested the methodology we used in the study is with high sensitivity. Other most often detected functional categories included “amino acid transportation and metabolism”, “energy production and conversion”, and “cell wall/membrane/envelope biogenesis”. Comparisons between various biological replicates were shown in Figure 3 to demonstrate the analytical reproducibility. Two types of comparisons were conducted. First, we labeled and mixed two biological replicates of ethanol-treated samples directly for proteomic analysis, and the difference was plotted versus the percentage of the proteins identified. The results showed pproximately 60% of the proteins with difference less

2. Overview of Quantitative Proteomics Analysis

A total of 200 463 spectra were obtained from the iTRAQ LC− MS/MS proteomic analysis. The peptides without labeling were excluded from the data sets. After data filtering to eliminate lowscoring spectra, a total of 24 887 unique spectra were matched to 1509 proteomes, representing approximately 42% of the 3569 predicted proteins in the Synechocystis sp. PCC 6803 genome (Supporting Information, Tables 1 and 2).38 The peptides carrying the variable modification were indicated in Supporting Information Table 2. In terms of protein mass distribution, good 5290



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Table 1. Summary of Differentially Regulated Proteins 24 h Ethanol-24h-rl vs Control-24h-rl Ethanol-24h-r2 vs Control-24hrl Ethanol-24h-rl vs Control-24hr2 Ethanol-24h-r2 vs Control-24hr2 48 h Ethanol-48h-rl vs Control-48hrl Ethanol-48h-r2 vs Control-48hrl Ethanol-48h-rl vs Control-48hr2 Ethanol-48h-r2 vs Control-48hr2

downregulated

upregulated

total

5 34

17 13

22 47

7

55

62

34

25

59

total unique down-regulated at 24 h

total unique up-regulated at 24 h

total unqiue responsive proteomes at 24 h

59

76

135

downregulated

upregulated

total

total unique down-regulated at 48 h

total unique up-regulated at 48 h

total unqiue responsive proteomes at 48 h

29

68

97

104

189

293

28

75

103

51

108

159

49

128

177

3. Ethanol Induces a Common Stress Response

than delta error of 0.1 and more than 95% of the proteins with difference less than delta error of 0.5 (Figure 3(I)). Second, we labeled and mixed each pair of ethanol-treated samples and its control for proteomic analysis, and the difference between different biological pairs was plotted in Figure 3(II). The dispersion of the iTRAQ ratios of the quantified proteins (i.e., 1167 and 1174 for 24 and 48 h, respectively) was found with very similar trends between four biological replicates at either 24 h (Figure 3(II-A)) or 48 h (Figure 3(II-B)), suggesting that the biological noise was reasonably low. It should be noted that some of the proteins (∼200−300) were detected only in either control or ethanol-treated samples so that a ratio cannot be calculated, and those proteins were excluded from Figure 3 (II). Using a cutoff of 1.5-fold change and a p-value less than 0.05, we determined that 135 and 293 unique proteins were differentially regulated between control and ethanol treatment conditions at 24 and 48 h, respectively (Table 1), among which slightly more proteins were up-regulated than the proteins downregulated by the treatments. Significantly increased numbers of the differentially regulated proteins at 48 h were probably due to the longer treatment time and more serious effects. Fifty-four upregulated and 17 down-regulated proteins were shared between 24 and 48 h, respectively, while more of the responsive proteins were unique for each of the time points (Figure 4).

The toxicity mechanism of organic solvents, such as toluene, hexane, and dimethyl sulfoxide, to microorganisms has been studied in many microbes such as yeast and Z. mobiliz.50−53 As exposure to high levels of ethanol represents a natural stress to cyanobacteria, we expected that the common mechanism cells used to deal with stress will be induced by the ethanol treatment.54 The proteomic analysis identified several proteins involved in common stress response differentially expressed between ethanol-treated and control conditions (Table 2). Heat shock proteins are the most common protective proteins in response to stress stimuli in most cells and can mediate the correct folding of proteins to prevent further damage and repair intracellular injury.55 Analysis of global gene expression during ethanol stress in S. cerevisiae revealed that up to 14 Hsp members were up-regulated by 3.4- to 21.5-fold,56 and deletion of Hsp104 greatly reduced both ethanol-induced tolerance to heat and heatinduced tolerance to ethanol.57 Our study found that two heat shock proteins GroES (Slr2075) and GrpE (Sll0057) and a universal stress protein (Slr1101) were up-regulated by ethanol (Table 2). GroES (chaperonin 10) is an oligomeric molecular chaperone, which functions in protein folding and possibly in intercellular signaling, and is found on the surface of various prokaryotic cells as well as is released from cells. Analysis of GroES in Z. mobiliz showed induction of groES expression in response to ethanol.58,59 GrpE, which is typically working with DnaJ and DnaK in an ATP-dependent manner for full folding of proteins, was involved in cellular behavior in adaptation to nbutanol tolerance in C. acetobutylicum.60 Oxidative stress responses have been reported for cells under treatment of organic solvent as they induce production of highly reactive oxygen species (ROS).61 In a recent study, an oxidative stress response was also observed in E. coli treated with nbutanol.36 Our proteomics showed that the oxidative stress response was also induced in Synechocystis sp. PCC 6803 by ethanol (Table 2). Glutathione peroxidase (Slr1992) was upregulated at both 24 and 48 h after ethanol exposure in Synechocystis. The enzyme reduces lipid hydroperoxides to their corresponding alcohols and to reduce free hydrogen peroxide to water and thus protects the organism from oxidative damage, and its ability against oxidative stress has been reported in various microbes.61,62 Two bacterioferritin comigratory proteins (Slr0242 and Sll0221) were also up-regulated by ethanol in

Figure 4. Distribution of the differentially expressed proteins at 24 and 48 h. 5291



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Table 2. Important Proteins Induced by Ethanol Exposurea,b 24 h

protein ID

Ethanol24h-r1 vs Control24h-r1


48 h Ethanol24h-r1 vs Control24h-r2




NT01SS2166 Sll0135 Ssl2084 Sll1430



1.97

1.55

1.55 3.02

2.20

2.98

6.54

4.27

1.50

Sll1815 Sll0422 Slr0242

1.57

2.13 1.75 2.89

1.61

Sll0221

2.00 2.71

2.03

Sll1491 Slr0040 Slr1853

1.50

1.62 2.00

1.52

Sll0873

1.66

Slr1390 Slr2073 Sll1292 Sll0039 Slr1982 Slr0757 Slr2075 Ssl3364 Slr0899 Slr0343

1.71

1.61 1.61

1.79

1.51

1.83 2.17

2.42 2.57 1.76 1.89 3.39

3.33 2.09

2.09 1.53 2.69 1.87 8.06 1.56

1.89

Sll0258 Sll1796 Sll1583 Ssl2982

4.65

3.82

1.55

3.58 1.68

Slr0434 Slr1844 Slr1740

1.56 1.52

Sll1322 Slr1330

1.66 1.70

1.90

Slr0148 Slr1828 Sll1382 Ssl0020 Slr1205 Slr1490 Slr1761

5.46

2.20

1.99

1.72

2.17 1.71 2.58 1.66 2.16

2.84 5.13 1.61 2.48

1.95 2.08

1.92

2.21

2.62

1.56 1.89

1.73 2.41

2.31

1.80

1.59

2.19

1.73

2.20

6.94 1.89

3.15

3.69 2.71

Slr1992 Slr0879

1.79 1.70

1.53

1.93 1.75

1.67 1.62

2.65 2.30

2.42 2.64

Sll1950 Slr1090 Sll0057 Ssl3044 Sll1512

1.62 2.02 1.55

1.59

Sll0248 Slr0033

1.97

2.05

2.21

41.67

5292

description 3-isopropylmalate dehydratase small subunit 5′-methylthioadenosine phosphorylase Acyl carrier protein Adenine phosphoribosyltransferase Adenylate kinase Asparaginase Bacterioferritin comigratory protein Bacterioferritin comigratory protein Beta transducin-like protein Bicarbonate transporter Carboxymuconolactone decarboxylase Carboxynorspermidine decarboxylase Cell division protein FtsH Cell division protein sepF CheY family protein CheY family protein CheY family protein Circadian clock protein KaiB Co-chaperonin GroES CP12 polypeptide Cyanate lyase Cytochrome B6-f complex subunit IV Cytochrome C-550 Cytochrome C553 DNA ligase DNA-directed RNA polymerase subunit omega Elongation factor P Excinuclease ABC subunit A Extracellular solute-binding protein F0F1 ATP synthase subunit A F0F1 ATP synthase subunit epsilon Ferredoxin Ferredoxin Ferredoxin Ferredoxin Ferredoxin Ferrichrome-iron receptor FKBP-type peptidyl-prolyl cis− trans isomerase Flavodoxin FldA Glutamyl-tRNA (Gln) amidotransferase subunit C Glutathione peroxidase Glycine decarboxylase complex H-protein Gsc GTPase ObgE Heat shock protein GrpE Probable ferredoxin Uncharacterized HTH-type transcriptional regulator



Article

Table 2. continued 24 h

protein ID








Sll1663

1.58

Slr1622 Slr1295 Slr2143 Slr0875 Sll0721 Slr1513 Sll1394 Sll1680 Sll0689 Slr1623

1.61 1.83

2.03

1.56 1.59 2.11

2.44

1.53

1.85

3.48 3.22 1.68

4.02 2.25

1.51 1.64

1.88

Sll0521 Slr1909 Slr1783 Ssl2667

1.69 1.52

1.75

Slr0043 Sll0408

2.35

2.00

10.75

4.59

2.83 1.93 1.81

1.73 1.94 1.74 1.57

1.98

1.95

2.42

2.11

1.94 2.04

1.64 2.03 2.66

1.57

2.34 6.90

1.54

Slr1251

1.52

Slr0513 Sll0064

2.83

Slr0172 Slr0823

3.02 1.56

1.71

1.99

2.13

1.72

2.04

1.68 1.60

Ssr2831 Sll0629 Slr1645 Sll1194

1.86 1.86 4.65

Sll1398

1.58 1.63 2.23

3.34

1.71

Sll0199 Sll1703 Ssl2296

3.68 2.51

1.55 1.64 2.40

1.71

3.46

2.65

2.73

1.50

1.54

2.02

2.07 2.88 2.07 1.53 2.79 2.36 1.62

1.69 1.54

1.70 1.93

1.85

2.28

Sll1742

2.19

1.58

1.66

Sll1483 Ssl3441 Slr0974

1.54

2.14

Ssl1690

Slr0821 Slr1198 Slr0194 Sll0145 Ssr1480 Slr0193 Slr0709 Slr2089 Slr1796 Ssl2009 Slr0729 Slr1034


1.85

2.93 2.01 2.10 1.91 1.63 1.63

1.58 2.20

2.19

2.16

1.88

4.93

3.12

2.09

2.20 1.64 5293

description Phycocyanin alpha phycocyanobilin lyase related protein Inorganic pyrophosphatase Iron transport protein L-cysteine/cystine lyase Large-conductance mechanosensitive channel S-layer-RTX protein-related Membrane-associated protein Methionine sulfoxide reductase A Methionine sulfoxide reductase B Na/H+ antiporter NAD(P)H-quinone oxidoreductase subunit M NAD(P)H-quinone oxidoreductase subunit O NADH dehydrogenase subunit J NarL subfamily protein NarL subfamily protein Assembly factor for iron−sulfur culsters Nitrate transport protein NrtC Peptidyl-prolyl cis−trans isomerase Peptidyl-prolyl cis−trans isomerase Periplasmic iron-binding protein Putative polar amino acid transport system Photosystem I assembly protein Photosystem I assembly protein Ycf3 Photosystem I reaction center subunit IV Photosystem I subunit X Photosystem II 11 kD protein Photosystem II complex extrinsic protein precursor U Photosystem II reaction center protein Psb28 Plastocyanin Protease IV Pterin-4-alpha-carbinolamine dehydratase Putative sulfur carrier protein Rehydrin Ribose-5-phosphate isomerase A Ribosome recycling factor RNA-binding protein RNA-binding protein RutC family protein Squalene-hopene cyclase Thylakoid membrane protein Thylakoid membrane protein Thylakoid-associated protein Thylakoid-associated singlestranded DNA-binding protein Transcription antitermination protein NusG Transforming growth factor induced protein Translation initiation factor IF-1 Translation initiation factor IF-3



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Table 2. continued 24 h

protein ID Slr1846



1.63




2.07

1.89

2.00

Slr1101 Sll0420 Slr1256



Ethanol48h-r2 vs Control48h-r2 1.61

2.12

2.24 2.06

description Uncharacterized monothiol glutaredoxin Universal stress protein Urease subunit beta Urease subunit gamma

a Proteins with 1.5-fold change and p-value less than 0.05. bHypothetical proteins and some poorly annotated proteins were listed with full information in Supporting Information, Table 3.

Synechocystis. A recent study showed that bacterioferritin comigratory proteins, along with glutathione peroxidasereductase, were responsible for detoxification of bentazonederived peroxide in a Synechococcus elongatus PCC7942 mutant Mu2 capable of growing at 5 times higher concentration of bentazone than wild-type Mu2.63 An uncharacterized monothiol glutaredoxin (Slr1846) was up-regulated in Synechocystis. Monothiol glutaredoxins have roles in actin cytoskeleton remodeling and in cellular defenses against oxidative stress caused by ROS accumulation in S. cerevisiae and Schizosaccharomyces pombe.64,65 Apart from general oxidative damage leading to fragmentation and carbonylation of the peptide backbone, specific modifications of certain amino acid side chains are common during oxidative stress. Cysteine and methionine both contain a sulfur atom in their side chains and are among the most easily oxidized amino acids. Methionine sulfoxides can be reduced back to the methionines by a thioredoxin-dependent enzyme, peptide methionine sulfoxide reductase (MSR), providing cells with a mechanism to repair proteins damaged by reactive oxygen species rather than having them degraded and then resynthesizing them de novo.66 The induction of the methionine sulfoxide reductase in response to oxidative stress was predictable as several previous studies have shown that msr genes of anaerobic Desulfovibrio vulgaris were induced by oxidative stress,66 and E. coli and S. cerevisiae strains with disrupted msr genes were more sensitive to oxidative stress.67,68 In the study, we found that two MSRs (Sll1394 and Sll1680) were up-regulated by ethanol exposure, suggesting this pathway of antioxidative response may also be important in photosynthetic Synechocystis. In addition, antioxidant proteins flavodoxin (Sll0248) and rehydrin (Slr1198) were also upregulated (Table 2).69 However, a cyanobacterial rubredoxin (Slr2033) that functioned as an FNR-dependent peroxidase in heterocysts for H2O2 decomposition70 and a thioredoxin reductase (Slr0600) involved in redox regulation in cyanobacteria71 were found down-regulated upon ethanol exposure, suggesting that they may play minor roles against ROS generated by ethanol treatment (Supporting Information, Table 1).

porters were also involved in tolerance to many different types of stresses, such as arsenate resistance in Anabaena variabilis,75 Cu2+ resistance in Nostoc calcicola,76 salinity stress in Synechococcus PCC7942 and Synechocystis PCC 6803,77,78 and heavy metals in filamentous Oscillatoria brevis.79 Our quantitative proteomic analysis was able to identify five putative transporters with induced expression level by ethanol exposure (Table 2). Interestingly, based on the annotation, the five transporters could be involved in transporting of different substrates: Sll0064 functions as a putative polar amino acid transport system substrate-binding protein, Slr0040 and Slr0043 function as bicarbonate transporters, Sll0689 as a Na+/H+ antiporter,80 and Slr1295 as an iron transport protein. In Synechocystis sp. PCC 6803, the slr0040, slr0041, slr0043, and slr0044 genes form an operon with a putative porin gene (slr0042), encoding a highaffinity bicarbonate transporter transcribed only under CO2limited conditions.81 Involvement of the bicarbonate transporter in solvent stress has never been reported before. Slr1295 is a periplasm-located component of an iron transporter and has a function in protecting photosystem PSII.82 Its up-regulation may be in parallel with the up-regulation of photosystems under ethanol conditions (which is discussed below) (Table 2). The discovery that transporters with a wide range of substrate specificity were involved in ethanol resistance, especially bicarbonate transporters, suggested that photosynthetic Synechocystis PCC 6803 may employ a different resistance mechanism for ethanol toxicity from yeasts where hexose and amino acid transport seems predominant.72 Moreover, recent genome-wide studies have led to a suggestion that microbes tend to use multiple and synergistic resistance mechanisms in dealing with single biofuel stress,22 and the five transporters with different substrate specificity were up-regulated around the same treatment time, indicating the utilization of multiple resistance mechanisms. However, their possible synergistic relationship is yet to be elucidated. 5. Ethanol Induces Modifications of Cell Membrane and Envelope

The studies with yeast and Z. mobiliz showed the cell membrane and envelope as the most affected targets of organic solvents, and the composition of the cell membrane and wall can influence ethanol tolerance.55,83 One well-documented change is the shift from cis to trans unsaturated fatty acids to decrease membrane fluidity.55 Squalene-hopene cyclase encoded by slr2089 was upregulated at both 24 and 48 h (Table 2). Hopanoids, a group of cyclic triterpenoic compounds, can integrate in biological membranes and increase structural order at temperatures above the phase transition temperature of phospholipid membranes. Due to the rigid ring structures, hopanoids have a condensing effect on phospholipid layers and reduce perme-

4. Ethanol Induces Transporters

Cross-membrane transporters for small molecules have been suggested as one important mechanism against ethanol toxicity in the early studies with yeast.55,72 For example, studies showed that the FPS1 gene, encoding the plasma membrane aquaglycerolporin which can facilitate transmembrane transport of small uncharged molecules like polyols and urea, was upregulated in response to ethanol exposure in yeast.73,74 Similarly, a recent functional genomics study with E. coli under exogenous n-butanol stress also found that transporters were among the most regulated functional categories.36 In cyanobacteria, trans5294



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ability of membranes. Hopanoids therefore have stabilizing functions in bacterial membranes comparable to sterols in eukaryotes.83,84 Hopanoids can be synthesized from squalene by squalene-hopene cyclase (Shc). Increased hopanoid concentration in membranes of Z. mobiliz at elevated ethanol concentrations has been reported.83 A recent study also found that the deletion of shc gene resulted in a severe growth defect when cells were grown under acidic and alkaline conditions in Rhodopseudomonas palustris TIE-1.85 Our result provided further evidence that squalene-hopene cyclase and hopanoids were important for stress tolerance. In addition to the changes with membrane lipid contents, we have also observed changes with other components of the cell membrane and cell envelope upon ethanol exposure. First, three peptidyl-prolyl cis−trans isomerase proteins were found upregulated (Sll0408, Slr1251, Slr1761). Prolyl isomerase catalyzes the cis−trans isomerization of Xaa−Pro bonds of peptides, which accelerates slow steps of protein folding and thus shortens the lifetime of intermediates and has been considered as a typical antistress response occurring in the cell envelope in bacteria.86 A large-conductance mechanosensitive channel (MscL) (Slr0875) was also up-regulated upon ethanol exposure. Prokaryotic microbes typically harbor a large-conductance mechanosensitive channel that plays a critical role in transducing physical stresses at the cell membrane into an electrochemical response.87 Other changes at cell membrane and cell wall levels included a putative S-layer-RTX protein (Sll0721), a periplasmic iron-binding protein (Slr0513), and an extracellular solute-binding protein (Slr1740). Although biochemical function for these proteins has not yet been established, their up-regulation by ethanol exposure could contribute to strengthening the cell membrane and extracellular matrix and is worth further investigation.

be the first functional clue to start the investigation. Three histidine kinases of unknown functions (Sll1555, Sll1871, and Sll1888) were down-regulated by ethanol exposure (Supporting Information, Table 1). Sll1555 is a hybrid-type histidine kinase with both the kinase and receiver domains in a single HK molecule. Early analysis of HK domain architecture from a number of prokaryotic and eukaryotic genomes showed that most eukaryotic HKs are of the hybrid type, while prokaryotes generally use a simple two-component phosphor-transfer scheme. This observation has led to a speculation that hybridtype HKs found in bacteria could be involved in signal transduction required for cell−cell communication or differentiation.96 In addition, a LysR transcriptional regulator (Sll0998) was down-regulated. The sll0998 gene homologous to proteobacterial Calvin cycle regulators (cbbR) has been found crucial to Calvin cycle and cell viability under autotrophic conditions.97 The stringent response is important for bacterial survival under stressful conditions (e.g., amino acid shortage) and is characterized by the accumulation of guanosine pentaphosphate and tetraphosphate ((p)ppGpp and ppGpp). The response caused inhibition of RNA synthesis and decreased translation, along with up-regulation of genes involved in amino acid uptake and biosynthesis.98 ObgE is an essential conserved GTPase, and several observations have implicated the protein in the control of the stringent response.98 A ObgE GTPase (Slr1090) was found up-regulated by ethanol in Synechocystis. Although the relationship between stringent response and ethanol tolerance in Synechocystis is still unclear, a recent study showed that the (p)ppGpp synthetase RelA, a key enzyme in the stringent response, contributes to stress adaptation and virulence in Enterococcus faecalis V583.99

6. Ethanol Affect on Cell Mobility and Regulatory Systems

7. Ethanol Induces Proteins Related to Photosystem and Circadian Rhythms

In E. coli, there are six sigma factors associated with stress response; among them, σ28 (σF) controls genes related to flagella synthesis and chemotaxis to adapt bacteria in stress environments.88 Recently, several genome-wide analyses showed that genes involved in cell mobility and chemotaxis were up-regulated by ethanol or butanol in C. acetobutylicum.89,90 Our proteomic analysis showed that three CheY family proteins (Sll0039, Sll1292, Slr1982), all signal proteins involved in chemotaxis, were up-regulated at 48 h. Sll0039 was even annotated as a positive phototaxis protein according to Cyanobase (http:// genome.kazusa.or.jp/cyanobase). A previous study showed that they were also up-regulated by salt stress in Synechocystis.91 Three regulatory proteins were found with increased expression, including two response regulators of the NarL subfamily two-component system (Slr1783, Slr1909) and an uncharacterized HTH-type transcriptional regulator (Sll1512). Slr1783 was also annotated as Rre1, which has been found to be involved in salt stress92 and regulation of an alcohol dehydrogenase (Slr1192) that was enhanced upon the exposure of cells to different environmental stresses.93 HTH-type transcriptional regulators use a structurally well-defined DNA-binding HTH motif to recognize the target DNA sequences, and other similar HTH-type transcriptional regulators included a tetracycline repressor (TetR) regulating resistance mechanism against the antibiotic tetracycline in grain-negative bacteria94 and a QacR repressing multidrug transporter gene in Staphylococcus aureus.95 Currently no information is available regarding the possible physiological functions of these regulatory proteins in Synechocystis, and their involvement in ethanol response could

Cyanobacteria has two types of photosystems: photosystem I (plastocyanin: ferredoxin oxidoreductase, PSI) and photosystem II (water−plastoquinone oxidoreductase, PSII).100 Early studies showed that some natural stresses, such as salt and sulfur starvation, decreased expression level of genes for phycobilisome, photosystems I and II, cytochrome b6/f, and ATP synthase, indicating overall reduced light-harvesting and photosynthetic activity upon stress.101,102 Early researches have suggested that the photosystems are the main sites of ROS production and the primary sites of damage of abiotic stresses, as various abiotic stresses (i.e., cold, light, drought, salt, and metal) can impair function of the photosystems and lead to the production of ROS due to the overexcitation of the photosystem.103−107 Interestingly, our proteomics analysis showed that a set of proteins involved in photosynthetic activity was up-regulated by the ethanol treatment (Table 2). The responses included: (i) upregulation of four proteins from photosystem I (Slr0172, photosystem I assembly protein; Slr0823, photosystem I assembly protein Ycf3; Ssr2831, photosystem I reaction center subunit IV; Sll0629, photosystem I subunit X) and three proteins from photosystem II (Slr1645, photosystem II 11 kD protein; Sll1194, photosystem II complex extrinsic protein precursor U; Sll1398, photosystem II reaction center protein Psb28); (ii) upregulation of six ferredoxin-like proteins (Slr0148, Slr1828, Sll1382, Ssl0020, Slr1205, and Ssl3044) and one flavodoxin FldA (Sll0248). Ferredoxins are the main electron shuttles, accepting electrons from photosystem I and delivering them to essential oxido-reductive pathways. Ferredoxin levels were previously 5295



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Figure 5. Schematic representation of the photosynthetic network regulated by ethanol. The photosynthetic electron transport chain is composed of the two photosystems (PSI and PSII) and cytochrome b6 f (B6f). Electrons are transferred from PSII to cytochrome b6 f via plastoquinol molecules. Plastocyanin (PC) transfers electrons from cytochrome b6 f to PSI. Electrons from PSI are transferred to ferredoxin (Fd) and Ferredoxin-NADP reductase (FNR). Reactive Oxygen Species (ROS) generated during enhanced electron transfer reactions were indicated by red explosion symbols. The relevant up-regulated proteins associated with each of the systems were indicted and highlighted in green.

found decreased under adverse environmental conditions in photosynthetic microbes.108 In cyanobacteria, this decrease is compensated for by induction of flavodoxin, an isofunctional flavoprotein that can replace ferredoxin in many reactions;109 (iii) up-regulation of a light-harvesting related phycocyanin alpha phycocyanobilin lyase (Sll0199) and a plastocyanin (Sll1663);110,111 (iv) up-regulation of multiple cytochromes, such as cytochrome B6-f complex subunit IV (Slr0343), cytochrome C-550 (Sll0258), and cytochrome C553 (Sll1796); (v) up-regulation of two F0F1 ATP synthase subunits (Sll1322, Slr1330); (vi) up-regulation of L-cysteine/cystine lyase (Slr2143) involved in ferredoxin Fe−S cluster formation;112 and (vii) up-regulation of four thylakoid membrane-associated proteins (Slr0729, Slr1796, Slr1034, Ssl2009) involved in ATP formation during photosynthesis.113 The profound up-regulation of proteins associated with multiple aspects of photosynthesis, along with up-regulation of Slr1295 (a periplasm-located component of an iron transporter) which has a function in protecting photosystem PSII,77 provided strong evidence that cyanobacterial photosynthesis was up-regulated by ethanol exposure (Figure 5). To confirm the possible enhancement of photosynthesis under ethanol treatment conditions, we performed measurements of chlorophyll a concentration in cells. As a control for methodology, we also measured the chlorophyll a concentration in salt-treated Synechocystis sp. PCC 6803 cells as early literature has showed that salt treatment caused down-regulation of photosynthesis and chlorophyll a concentration.101,102,114 Chlorophyll a was extracted from all samples, and their fluorescence was measured and chlorophyll a concentration calculated. The results showed that while 4.0% NaCl treatment decreased the chlorophyll a concentration, which is consistent with the early studies,101,102,114 ethanol treatment increased the chlorophyll a concentration in the Synechocystis cells by approximately 100% at 24 h and 30% at 48 h, respectively (Figure 6). The results provided important cellular-level support to the up-regulated of photosynthesis-related proteins revealed by the proteomics analysis, suggesting the enhanced photosynthesis related activities were related to ethanol tolerance in Synechocystis.

Figure 6. Chlorophyll a concentration in control and ethanol-treated cells. Ratio of Chl A absorption was normalized by cell density (OD730). Each bar represents the mean of three independent biological replicates, and the error bars denote standard deviation (SD) of all biological replicates. We used 4.0% of NaCl for salt treatment according to the literature.

Cyanobacteria are the only prokaryotes known thus far possessing regulation of physiological functions with approximate daily periodicity, or circadian rhythms that are controlled by a cluster of three genes, kaiA, kaiB, and kaiC.115 Our proteomic analysis showed that one of the key circadian clock proteins, KaiB (Slr0757), was induced, suggested that the circadian rhythms may also be affected by ethanol treatment (Table 2). 8. Ethanol Affects Other Central Metabolic Process

Although our proteomic analysis was able to identify most of the proteins involved in the CO2 fixation pathway, such as ribulose bisphosphate carboxylase (Slr0009, Slr0012) and carbon dioxide concentrating mechanism proteins (Sll1028, Sll1029, Slr1839, Slr1838, Slr0436, Sll1030, Sll1031), no protein of this functional category was found with differential expression (Supporting Information, Table 1). Similarly, although we have identified a large number of proteins involved in fatty acid metabolism, few of them were differentially regulated by ethanol treatment under the testing conditions. 5296



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Notes

Meanwhile, we found that a number of ribosomal proteins involved in protein biosynthesis, and proteins involved in core carbohydrate metabolism, such as pyruvate dehydrogenase, succinate dehydrogenase, 6-phosphofructokinase, fructose-1,6bisphosphatase, and phosphoenolpyruvate carboxylase, were down-regulated (Supporting Information, Table 1), which may partially explain the slow growth of the Synechocystis cells under ethanol exposure (Figure 1A).

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The research was supported by grants from the National Basic Research Program of China (National “973” program, project No. 2011CBA00803 and No. 2012CB721101). The authors would also like to thank Tianjin University and the “985 Project” of Ministry of Education for their generous support in establishing the research laboratory.

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CONCLUSIONS Comparative proteomic analysis showed that the Synechocystis sp. PCC 6803 cells employed a combination of induced common stress response, modifications of cell membrane and envelope, and induction of multiple transporters and cell mobility as major protection mechanisms against ethanol toxicity. Utilization of these mechanisms is very similar to what has been reported for yeast and Z. mobiliz,50−52 although the exact proteins involved in each of these responsive modules may be different between Synechocystis and yeast or Z. mobiliz. The results implicated the presence of a possible core response to ethanol among various species.116 The putative core response to ethanol, once confirmed by further studies, could be an important target for engineering ethanol tolerance across a spectrum of producing hosts. Interestingly, our proteomics analysis provided strong evidence that proteins involved in multiple aspects of photosynthesis (i.e., photosystem I and II, cytochrome, ferredoxin) were up-regulated in ethanol-treated Synechocystis, which is inconsistent with some early studies showing that the photosynthetic activity was generally decreased upon environmental stresses, such as salt.94 On the basis of our results, we proposed that ethanol treatment might enhance photosynthesis in Synechocystis or impair photosynthesis and thus cause up-regulation of related proteins as compensation, with generation of ROS, and trigger oxidative stress response in the end (Figure 5). As a unique biochemical property for photosynthetic organisms, it remains unclear how the increased expression of photosynthesis-related proteins will help combat the ethanol toxicity. Further functional investigation of photosystems II and I could provide more insight. In addition, a large number of hypothetical proteins were identified as responsive to ethanol exposure (Supporting Information, Table 1), and they may represent a huge information resource for discovering targets related to ethanol tolerance and worth further efforts to decipher their potential functions. Finally, it has been reported that ratio distortion owing to protein quantification interference is a common effect in iTRAQ proteomic analysis,117,118 and further validation of the results from this proteomics analysis will be necessary. Overall, the study offers important insights into the dynamic responses to ethanol at a global level in Synechocystis, which will help build an initial foundation for better understanding of ethanol tolerance and for manipulating the cyanobacterial cells for robust highethanol-tolerant producing systems.

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ASSOCIATED CONTENT

S Supporting Information *

Supplementary tables. This material is available free of charge via the Internet at http://pubs.acs.org.

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REFERENCES

AUTHOR INFORMATION

Corresponding Author

*Tel.: 0086-22-2740-6394. Fax: 0086-22-27406364. E-mail: [email protected]. 5297



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