Dynamic Proteome Analysis of

ARTICLE pubs.acs.org/jpr

Dynamic Proteome Analysis of Cyanothece sp. ATCC 51142 under Constant Light Uma K. Aryal,† Jana St€ockel,‡ Eric A. Welsh,‡ Marina A. Gritsenko,† Carrie D. Nicora,† David W. Koppenaal,† Richard D. Smith,† Himadri B. Pakrasi,‡ and Jon M. Jacobs*,† † ‡

Pacific Northwest National Laboratory, Richland, Washington 99352, United States Department of Biology, Washington University, St. Louis, Missouri 63130, United States

bS Supporting Information ABSTRACT: Understanding the dynamic nature of protein abundances provides insights into protein turnover not readily apparent from conventional, static mass spectrometry measurements. This level of data is particularly informative when surveying protein abundances in biological systems subjected to large perturbations or alterations in environment such as cyanobacteria. Our current analysis expands upon conventional proteomic approaches in cyanobacteria by measuring dynamic changes of the proteome using a 13C15N-L-leucine metabolic labeling in Cyanothece ATCC51142. Metabolically labeled Cyanothece ATCC51142 cells grown under nitrogen-sufficient conditions in continuous light were monitored longitudinally for isotope incorporation over a 48 h period, revealing 414 proteins with dynamic changes in abundances. In particular, proteins involved in carbon fixation, pentose phosphate pathway, cellular protection, redox regulation, protein folding, assembly, and degradation showed higher levels of isotope incorporation, suggesting that these biochemical pathways are important for growth under continuous light. Calculation of relative isotope abundances (RIA) values allowed the measurement of actual active protein synthesis over time for different biochemical pathways under high light exposure. Overall results demonstrated the utility of “nonsteady state” pulsed metabolic labeling for systems-wide dynamic quantification of the proteome in Cyanothece ATCC51142 that can also be applied to other cyanobacteria. KEYWORDS: cyanobateria, Cyanothece ATCC51142, dynamic proteome, metabolic labeling, mass spectrometry, N2-fixation, photosynthesis

1. INTRODUCTION Cyanobacteria are oxygenic photosynthetic prokaryotes, and photosynthetic assimilation of CO2 by these microbes plays an important role in the global carbon cycle.1,2 In addition, different unicellular species such as Crocosphaera and Cyanothece are capable of biological N2-fixation (BNF) and have been recognized for their significant role in the marine nitrogen cycle.3 Recent functional genomics analyses of Cyanothece ATCC51142 and other unicellular diazotrophic cyanobacteria revealed a strong transcriptional regulation of genes involved in the biochemically incompatible cellular processes of oxygenic photosynthesis and oxygen-sensitive N2-fixation.48 To date, proteome level investigations have generally focused on the determination of steady state protein abundances9,10 and, as such, have not captured the dynamics of the proteome.11 Accordingly, such studies are inefficient in providing detailed information concerning adaptive capabilities. In contrast, the ability to quantitatively measure dynamic changes of the proteome under different conditions has the potential to unravel previously unknown cellular mechanisms and to enhance our systems level understanding of regulatory mechanisms that r 2011 American Chemical Society

control diverse metabolic pathways in Cyanothece and other cyanobacteria. Metabolic labeling of proteins using stable isotope labeling with amino acids in cell culture (SILAC)12 is a powerful tool for quantitative, mass spectrometry (MS)-based proteomics.13 Relative protein expression changes can be accurately measured by comparing the natural form of a peptide with its stable isotope analog.14 The proteome of any organism is a dynamic system controlled by the relative rates of protein synthesis and degradation.15 The focus of many proteomic studies is to determine the relative changes in the levels of individual proteins,16 and recent studies have used stable isotopes as molecular tracers to study dynamic protein turnover in vivo.17 Although increasingly applied to different model organisms,11,15,16,1824 metabolic labeling to investigate the proteome dynamics in Cyanothece ATCC51142 and other cyanobacteria remains unexplored. Therefore, this study aims to evaluate the efficiency of “dynamic metabolic labeling” for quantitative measurement of the proteome Received: June 6, 2011 Published: November 07, 2011 609

dx.doi.org/10.1021/pr200959x | J. Proteome Res. 2012, 11, 609–619

Journal of Proteome Research

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loaded onto a 2.1 200 mm (5 μM, 300 Å) Polysulfethyl A LC column (PolyLC, Columbia, MD) and preceded by a 2.1 10 mm guard column. After loading peptides to the LC column, the mobile phase consisted of 100% buffer A for 10 min, a 40-min linear gradient from 0 to 50% B (25% acetonitrile, 500 mM ammonium formate, pH 6.8), a 10-min linear gradient from 50 to 100% B, and then 100% B for 20 min. Using an automated fraction collector, 24 fractions were collected for each sample, pooled into 9 fractions, and lyophilized prior to LCMS/MS analysis.

dynamics in Cyanothece ATCC51142. We measured the partial label incorporation of Cyanothece 51142 cells growing in heavy leucine (13C15N-Leu) containing medium over a time course of 48 h under continuous light at a light intensity of 150 μmol photons m2 s1. Using this technique in combination with LCMS and MS/MS analysis, we quantitatively measured expression profiles of 414 proteins with different levels of isotope incorporation. Results highlight the importance of the current technique to obtain an added dimension of insight into the overall strategy of Cyanothece ATCC51142 to adapt to environmental conditions by regulating major metabolic processes through changes in protein abundances.

Reversed Phase Liquid Chromatography (LC) Separation and Tandem MS (MS/MS) Analysis

Trypsin digested proteome samples were analyzed using high throughput hybrid LCMS/MS as described previously.10 Six μL of the tryptic peptides from each SCX fraction out of a total of 60 μL per fraction were analyzed using a custom-built automated four column capillary LC (150 μm i.d. 360 μm o.d. 65 cm capillary; Polymicro Technologies Inc., Phoenix, AZ) under constant pressure of 5000 psi coupled with a Finnigan LTQFT Iontrap Mass Spectrometer (ThermoFinnigan, San Jose, CA) and an electrospray ionization source manufactured in-house that has been extensively reported.31 The LC column was prepared by slurry-packing 3-μm Jupiter C18 bonded particles (Phenomenex, Torrence, CA). After loading 3 μg of peptides onto the column, peptides were separated for a 100 min run using the following settings: 100% mobile phase solvent A (0.2% formic acid in water) for 20 min, followed by a linear gradient from 0 to 70% of solvent B (0.2% formic acid in 100% acetonitrile) over 80 min before reverting to 100% solvent A. Each full MS scan (m/z 4002000) was followed by collisioninduced MS/MS spectra (normalized collision energy setting of 35%) for the 5 most abundant ions per duty cycle selected for fragmentation. The dynamic exclusion window was set to 30 s, the heated capillary was maintained at 200 °C, and the ESI voltage was held at 2.2 kV.

2. EXPERIMENTAL PROCEDURES Culture Conditions and Metabolic Labeling

Cyanothece ATCC51142 cells were grown for 7 days in a ASP2 medium25 supplemented with 17.6 mM NaNO3 at 30 °C and constant illumination with white light at an intensity of 30 μmol photons m2 s1. Initially, flasks containing the culture medium were inoculated with 0.1 volume of culture grown under the same conditions. For the labeling experiment, cultures were transferred into high light (150 μmol photons m2 s1) and 40 mg/L of 13 15 L-leucine (U C6, 98%; N7, 98%; Cambridge Isotope Laboratories) was added to the culture medium. The cells were grown for an additional 48 h and samples were collected at 2, 12, 24 and 48 h after addition of the heavy L-leucine. The cells were harvested by centrifugation at 5000 g for 5 min and cell pellets were frozen and stored at 80 °C until used for protein extraction. Chlorophyll Measurement

For chlorophyll a measurements, cultures were harvested at 0, 2, 12, 24 and 48 h of labeling. One mL cell culture was centrifuged for 1 min at 12000 g and the cell pellet was resuspended in 10 μL ASP2 media. 990 μL of methanol was added to the cell suspension, mixed and centrifuged again for 1 min. Total chlorophyll a was quantified spectrophotometrically using an Olis DW2000 spectrophotometer (Online Instrument Systems) at 652 and 665 nm.26,27 Chlorophyll a concentrations were calculated using the following formula: Chl a [μg/mL] = (16.29 A665) (8.54 A652).

Data Analysis

The hybrid LCMS data sets were analyzed using in-house developed ICR2LS and VIPER software.3234 ICR2LS performs mass transformation and deisotoping on the raw LCMS data to generate a set of monoisotopic masses and the corresponding intensities for all detected species in each mass spectrum. LCMS feature identification was performed using VIPER33,34 by matching accurately measured masses and normalized LC elution time (NET) of each detected feature to the Cyanothece ATCC51142 AMT tag database.10 A robust LCMS alignment algorithm has been incorporated into VIPER for correcting any variations in mass and elution time.35 The Cyanothece AMT tag database was previously generated using LCMS/MS analysis of highly fractionated samples to provide broad peptide level coverage.10 The Cyanthece AMT tag database was designed with an overall false discovery rate at the peptide identification level of 0.5 after 48 h include glycogen synthesis and metabolism, chaperones, RNA synthesis, protein degradation, PP pathways, and chlorophyll biosynthesis, which is nearing a full 1.0 RIA value. As evident from the heat map (Figure 3A) and data presented in Table S1 and S2 (Supporting Information), it is clear that there were substantial differences in the timing of protein expression among different functional groups. While proteins involved in protein and RNA degradation and biosynthesis of cofactors showed faster turnover, others such as phycobiliproteins, photosystem I, tRNA synthesis, CO2 fixation and detoxification proteins showed relatively slower rates of turnover. By viewing RIA values in this perspective, insightful observations can be made concerning the activity of any particular cellular function for Cyanothece ATCC51142 under continuous light growth.

Figure 2. Determination and quantification of labeled proteins. (A) Venn diagram showing the number of isotopically labeled proteins (inner green circle) and the total number of observed proteins (outer yellow circle). (B) Distribution of labeled peptides/proteins at different time points over the 48 h time course. (C) Heavy L-leucine incorporation levels measured over time as RIA values for 24 ribosomal proteins.

of the L-leucine amino acid (Figure 2A, Supplementary Table S1 and S2, Supporting Information). For further analysis, we generated peptide and protein level RIA values,11,15,19 which helps quantify the fraction of labeled proteins to the total (labeled + unlabeled) protein pool, and represents an overall measure of protein synthesis and/or protein stability. Accordingly, lower RIA values over time indicate either more stable proteins with a lower translation rate compared to higher RIA values likely representing proteins with a higher translational and/or turnover rate as a function of environmental perturbations or conditions. For example, the calculated RIA of 0.63 for glycogen synthase (GlgA1) at 48 h (see Table 1) suggests that at least 63% of this protein in the total proteome pool had been newly synthesized at that time point. Again, for a given time point, individual proteins within the same functional category exhibited variations in the rates of new protein synthesis (Table 1). A good example is PetA (cce_2959), PetB (cce_1383) and PetD (cce_1384) of cytochrome b6f complex.

Analysis of Changes in the Cyanothece 51142 Proteome

Looking specifically at the contributions of individual proteins, Figure 4A and 4B show changes in the relative RIA abundances of selected proteins within each functional category. The complete list of labeled proteins and peptides with their corresponding RIA values are summarized in Supplemental Table S1 and S2 (Supporting Information). The data obtained in this study reflect how cells modulate their metabolism during growth under constant light. As a reflection of this, proteins involved in 612

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Table 1. List of Selected Groups of Proteins Belonging to Different Cellular Functions That Showed Relatively Higher Level of Isotope Incorporation As Measured by RIAa RIA ORF

proteins

2h

12 h

24 h

48 h

Glycogen synthesis and metabolism cce_3396

Glycogen synthase (GlgA1)

0.27

0.63

cce_2391

Glucosylglycerol-phosphate synthase (GgpS)

0.05

0.27

0.74

cce_1629

Glycogen phosphorylase (GlgP1)

0.14

0.40

0.86

cce_1357

Fructose-1,6-bisphosphate aldolase

0.03

cce_0835

S-Adenosyl-L-homocysteine hydrolase (AhcY)

0.03

cce_4760

NAD+-glycerol-3-phosphate dehydrogenase (GpsA)

0.11

0.40

cce_0666

Glucose-6-phosphate isomerase (Pgi1)

0.14

0.40

cce_4219

Phosphoglycerate kinase (Pgk)

0.16

0.40

cce_2156

Enolase (Eno)

cce_2750

Pyruvate dehydrogenase E2 (PdhC)

cce_0606

Phosphoglucomutase (Pgm)

cce_2225

Phosphoketolase

cce-3746

6-Phosphogluconate dehydrogenase (Gnd)

0.30

0.91

cce_4627

Transketolase (TktA)

0.02

0.08

0.22

0.66

cce_0798

Ribulose-phosphate 3-epimerase (Rpe)

0.04

0.28

0.92

0.51

cce_0995

Phosphoribulokinase (Prk)

cce_4304

Triosephosphate isomerase (Tpi)

cce_3366

Carboxysome formation protein (CcmA)

cce_3166

Ribulose bisphosphate carboxylase (RbcL)

cce_4280

Energy metabolism 0.20

0.01 0.1

0.48

0.83

0.94

0.38

0.12

0.32

0.32

0.85

0.06

0.30

0.54

0.02

0.20

CO2 fixation 0.03

0.12

0.35

0.76

0.05

0.32

0.48

0.72 0.015

0.034

0.074

0.14

CO2-concentrataing mechanism protein (CcmM)

0.06

0.13

0.30

cce_4282

CO2-concentrating mechanism protein (CcmK1)

0.03

0.10

0.20

cce_1049

NDH-1S subunit, CO2 uptake small protein (CupS)

0.74

0.21

cce_2959

Apocytochrome f (PetA)

0.27

0.44

cce_1384

Cytochrome b6f subunit 4 (PetD)

0.62

0.86

cce_1383

Cytochrome b6 (PetB)

0.25

0.57

Cytochrome b6f complex

Soluble electron carrier cce_0589

Cytochrome c family protein

0.04

0.15

0.49

cce_0994

Ferredoxin-NADP oxidoreductase (PetH)

0.02

0.16

0.47

cce_0990

PSI P700 chlorophyll a subunit Ib (PsaB)

0.06

0.52

cce_1290

PSI reaction center subunit III (PsaF)

0.24

0.34

cce_1747

PSI ironsulfur center subunit VII (PsaC)

cce_1973

PSI biogenesis protein (BtpA)

cce_1837

PSII CP47 protein (PsbB)

cce_2955

PSII extrinsic protein cyt c550

cce_2625

PSII 12 kD extrinsic protein (PsbU)

cce_1659

Delta-aminolevulinic acid dehydratase (HemB2)

0.45

cce_2966

Uroprophyrinogen decarboxylase (HemE)

0.02

cce_3201

Coproporphyrinogen III Oxidase, aerobic (HemF)

0.91

cce_2813

ATP synthase F1, beta subunit (AtpB1)

0.10

0.15

0.54

cce_4488

ATP syntahse F1, alpha subunit (AtpA1)

0.17

0.14

0.36

cce_4489

ATP synthase gamma subunit (AtpC1)

0.12

0.23

0.56

PSI and PSII 0.04

0.62 0.07 0.18 0.03

0.66

0.56 0.10

0.14

0.20

0.56

Pigment biosynthesis 0.98 0.4

ATP synthase 0.006

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Table 1. Continued RIA ORF

proteins

2h

12 h

24 h

48 h

Protein degradation cce_1941

ATP-dependent Clp protease (ClpB1)

cce_1911

ATP-dependent Clp protease (ClpP3)

0.55

0.33

0.94

cce_0641

ATP-dependent Clp protease (ClpP1)

0.12

0.58

cce_4247

ATP dependent Clp protease (ClpC1)

0.47

0.77

0.94

cce_3488

RNA polymerase beta subunit (RpoB)

0.03

0.59

0.98

cce_3838

RNA polymerase gamma sununit (RpoC1)

0.06

0.29

0.70

cce_1343

Chaperonin (GroES)

0.28

0.33

0.64

cce_1344 cce_3330

Chaperonin 1 (GroEL1) Chaperonin 2 (GroEL2)

0.11 0.05

0.20 0.14

0.32 0.32

cce_2925

Heat shock protein Hsp90 (HtpG)

0.38

0.93

0.85

cce_2003

Heat shock protein 70 (Hsp70)

0.05

0.35

0.65

cce_0174

Glutathione S-transferase (Gst1)

0.14

0.68

cce_3988

Thioredoxin (TrxA2)

0.19

0.36

0.31

0.89

RNA synthesis

Chaperons and heat shock

0.02 Detoxification

a

The RIA was calculated as the ratio of the heavy peptide intensity (H) to the sum of heavy and light peptides intensity (IH + IL); (RIA = [IH/(IH + IL)]. The complete list of all the labeled proteins with RIA is provided in Supplementary Table S1 and complete list of all the labeled peptides is in Supplementary Table S2 (Supporting Information).

Interestingly, glucosylglycerol phosphate synthase (GgpS), an enzyme involved in conversion of G3P into glucosylglycerol (GG), also showed increased synthesis. Although the regulation of GgpS and GG synthesis in protection against salt stress is known,42 the reason for increased synthesis of GgPS in the current study is unclear. In addition, expression of thioredoxin (TrxA2), glutathione S-transferase (Gst), and glutathione reductase (Gor) (Figure 4A) indicate stress response mechanisms to protect the cells against photooxidative damage.43 Additionally, higher RIA values of Gnd (RIA ≈ 0.91) suggest an increased demand of NADPH for TrxA2 and Gor functioning to reduce oxidized thioredoxin or glutathione (GSSG),44 contribution of Gnd in protecting cells from oxidative damage. Cyanobacterial carbon fixation requires proper assembly and functioning of the carboxysome.1 Interestingly, chromosome partitioning, ParA family protein showed active synthesis at 48 h (Supplementary Table S1). Recently, the deletion of parA like gene in Synechococcus elongatus PCC 7942 disrupted carboxysome organization.1 Therefore, the expression of the ParA family protein (cce_5257) at 48 h together with CcmA, CcmM, and CcmK1 might suggest an important role in organization and functioning of carboxysome in Cyanothece ATCC51142. Rubisco (ribulose-1,5-bisphosphate carboxylase oxygenase) is the key enzyme of the Calvin cycle that catalyzes the first major step of carbon fixation.45 We observed only limited active expression of both the large subunit (RbcL) (≈14% at 48 h) and small subunit (RbcS) (≈6% at 24 h) of Rubisco likely reflecting a slow turnover of both subunits. PSI catalyzes the light driven transport of electrons from reduced plastocyanin or Cytochrome b6f to soluble ferredoxin or flavodoxin46 (Figure 5). While several proteins of photosynthetic electron transfer including PetA, PetB, PetD and PetH showed increasing label accumulation, only a few proteins of PSI and PSII

Table 2. Measurement of Cell Growth and Chlorophyll Concentrations at Different Time Pointsa 652 nm

665 nm

μg Chl/mL

0.1123 0.1149

8

1.60 10 1.64 108

0.0869 0.1215

0.1853 0.2453

2.28 2.96

12 h

0.1392

1.99 108

0.1094

0.2457

3.07

24 h

0.1640

2.34 108

0.1349

0.3065

3.84

48 h

0.2460

3.51 108

0.2282

0.5086

6.34

time

OD730

cells/mL

0h 2h

a

First sample was taken at the time of heavy isotope addition to the medium (0 h). Data represent the average of 3 replicate measurements.

nitrogen fixation are not expressed.8 Specifically, we observed considerable synthesis of GlgA1 and GlgB1, enzymes involved in the synthesis of glycogen (Figure 4A, B and Supplementary Table S1, Supporting Information) which serves as an energy reservoir when sufficient cellular carbohydrates are available.25,41 In addition, glycogen phosphorylase GlgP1, which is involved in degradation of glycogen to supply the cell with energy and carbon building blocks, also reveals an increase in the RIA value during the time course. So, both synthesis and metabolism of glycogen seems to occur at the same time under the experimental conditions. Similarly, continuous active synthesis of proteins involved in the oxidative branch of PP pathway including 6-phosphoglucomutase (Pgm), transketolase (TktA), phosphoketolase (cce_2225) and 6-phosphogluconate dehydrogenase (Gnd) was observed. This pathway has two major functions, the production of ribulose 5-phosphate, which is required for the nucleotide synthesis, and the generation of NADPH, which provides the major reducing power. Thus, under nitrogensufficient condition and higher light intensities, a continuous activation of the PP pathway in Cyanothece was observed. 614



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Figure 3. Dynamic changes in the abundances of labeled proteins over time. (A) Heat map showing hierarchical clustering of 414 labeled proteins. Gray areas denote no data (pairs) present. (B) Overview of selected functional categories according to KEGG and their corresponding averaged protein RIA values. The numbers in the parentheses indicate the number of proteins used to calculate the averaged RIA values.

complex showed increase in synthesis and peaked at different time points (Table 1 and Figure 4B). However, at given time points, PSII proteins showed relatively faster turnover than the PSI proteins (Figure 3B). Presumably, the cellular strategy under high light exposure seems not to increase protein amounts dramatically, but to sustain photosynthetic rates by regulating multiple pathways to ensure a continuous supply of ATP and NADPH for efficient carbon fixation. In addition, only a modest increase in the expression of PBS proteins was observed which is likely because these proteins would be less necessary after increasing light intensity at the start of the labeling experiment. The proteomic profiles of several biosynthetic enzymes observed at 24 and 48 h suggest that they undergo some targeted degradation as the number of labeled proteins decreased at 48 h. In Bacillus subtilis, expression of RNA polymerase sigma factor provides general stress response with multiple stress resistance strategies in anticipation of future stresses.47 In Cyanothece 51142, we observed increased active synthesis of RNA polymerase beta prime (RpoC2), beta (RpoB) and gamma subunit (RpoC1) at 48 h, and their expression might be a cellular strategy adopted by the Cyanothece to deal with multiple stresses. Besides these general stress marker proteins, the proteases ClpC1, ClpP1and ClpP also showed higher accumulation at 48 h. This is in good agreement with the potential for increased protein degradation at 48 h (Figure 3A and Supplementary Table S2, Supporting Information).

To investigate how proteome data corresponds to physiological data, we also measured chlorophyll concentrations at the time of each sample collection. Table 2 provides information not only on chlorophyll concentrations but also on colony forming units and OD730 values, which are indicative of culture growth. From this table it is apparent that the number of cells more than slightly doubles over the 48 h interval of the experiment. Thus, one would except that at least 50% of an average protein would be newly synthesized, even if there was no degradation of existing proteins. Exceptions might be proteins involved in light harvesting (e.g., phycobiliproteins), which would be less necessary after increasing light intensity at the start of the experiment, and possibly some proteins involved with oxidative stress as a result of the increased light intensity. No large differences were observed in chlorophyll concentration of cells from 0 to 24 h time points but the concentration did increased relatively higher by 48 h (3.84 μg/mL at 24 h vs 6.34 μg/mL at 48 h) (Table 2). This was in agreement with the increase in abundance of pigment biosynthesis proteins HemB2 and HemF at 48 h, and HemE at 24 h (Table 1). On the basis of insights gained using the presented dynamic quantitative data and available information in the literature, we provided a summary of the key cellular adaptation in Cyanothece 51142 during growth under nitrogen-sufficient conditions and high light illumination (Figure 5). We propose that under high light, cellular function relies not on effective photosynthesis but in the avoidance from photo-oxidative 615



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Figure 4. Comparison of dynamic changes in abundances of individual proteins across functional categories. (A) Heat map showing high light induced changes in the abundances of selected proteins over time. The gene symbols and ORF’s of the identified proteins are shown on the left and the functional categories on the right. (B) Examples of selected proteins from different functional categories that show dynamic changes in their protein abundances in response to high light. The error bars ((standard deviation) show the variation in peptides RIAs of the same protein.

damage. There are at least two modes of electron flow during photosynthesis, linear flow from PSII to PSI, which allows the production of ATP, and the reducing power in the form of reduced ferredoxin and NADPH. This mode is functional when light energy captured by PBS is transferred to PSII. But, under

continuous high light illumination, PBS might also directly transfer absorbed light energy to PSI to avoid some oxidative damage and activating cyclic electron flow driven solely by PSI, limiting the production of NADPH. To overcome the limited production of NADPH, our data suggest that Cyanothece 616



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Figure 5. Overview of the cellular metabolism in Cyanothece 51142. The biochemical pathways were divided based on the observed dynamic changes in protein abundances over time and the available information from the literature.4,48,5254 Proteins are represented by gene symbols according to CyanoBase (http://genome.kazusa.or.jp/cyanobase/CCE), and most of them with corresponding ORF are also shown in Figure 4A. Proteins that showed dynamic changes in their abundances over time are shown. The values shown in red and parentheses indicate the RIA’s of the labeled proteins for the 48 h time point. * Indicate RIA for the 24 h time point when values for the 48 h time point are missing or are lower in abundance than at time point 24 h. FBA, fructose-1,6-bisphosphate aldolase (cce_1357).

51142 activates metabolic pathways involved in carbohydrate metabolism including OPP pathways (Figure 5). Activation of OPP pathway at the transcript levels for the generation of NADPH and C skeletons under PSI light treatment has been reported for Synechocystis 6803. 48

leucine, can help create a more robust experimental study, to gain more quantitative pair information for analysis. Additionally, as seen in Figure 4B, some protein RIA values had relatively wide peptide RIA ranges. This could be either biologically based, that is, variations in specific Leu label incorporation, or process based, that is, variations in peak pair detection, identification, and protein quantification. The answer likely lies in both fields, but interestingly, peptide level RIA values show a consistent rate of incorporation across the time course at the individual peptide level (Table S2, Supporting Information), pointing to the intrinsic individuality of each peptide/Leu incorporation as a key driving factor. We acknowledge, however, that since our approach is attempting to quantify often diverse peptide peak pairs, relatively small RIA values with minimal “heavy” peak incorporation, this likely represents the potential for higher than normal variability in the quantification of these lower intensity peaks. Even though cyanobacteria are considered as slow growing organisms, we were able to measure a considerable amount of heavy leucine incorporation over a 48 h time course. Each cell and condition, however, requires a specific time frame that optimizes for the correct window of detection and tracking the heavy peptide peak and hence the incorporation rate of that amino acid over time. On the basis of our results, it appears that this approach is applicable to numerous cyanobacterial strains at a minimum and does not need to be limited to eukaryotic-based essential amino acid models; rather, it can be envisioned as practical for any prokaryotic organism, depending upon the rate of amino acid uptake. This study also led to the identification of proteins important for adaptation and growth of cyanobacteria under high light intensities. We found that Cyanothece utilizes the glutathione/Trx system in combination with multiple other stress response systems for cellular protection against high light. Additional protection is provided by the activity of chaperones and HSPs in agreement with previous transcriptional analysis in Synechocystis 6803.48

4. CONCLUSIONS We report the use of a dynamic metabolic labeling approach to study the proteome dynamics of cyanobacteria using Cyanothece ATCC51142 as a model organism. The availability of numerous cyanobacterial genome sequences together with increasing information about transcriptional regulation of various cellular processes4,5,7,4852 has stimulated interest in how regulated transcripts are translated into proteins. As a consequence, the pulsed metabolic labeling approach applied in this study for Cyanothece ATCC51142 provides a more functional tie to transcript activation, and will be useful for broader applications to understand dynamic state of the cells under different environmental conditions. Tracking actual active protein synthesis provides benefits particularly in instances where static protein abundance measurements do not accurately reflect protein regulation. Additionally, it was readily apparent that even though entire pathways and cellular functions can be regulated as a whole based upon environmental cues and energy requirements, there remains a strong protein-specific turnover component, whether structurally or environmentally driven. This results in an enlightened view of the complex underlying nature of any particular protein abundance driven by its intrinsic stability, or lack thereof, and its regulation. Even though Cyanothece is able to synthesize L-leucine for new protein synthesis, heavy L-leucine is gradually taken up and can be used for quantitative MS labeling, as confirmed in this study. We expect that further optimization of the experimental design, particularly altering the media concentration of heavy labeled 617


Journal of Proteome Research Additionally, we observed increased synthesis of proteins involved in the reductive and oxidative PP pathways as well as glycogen synthesis and metabolism suggesting their importance to meet higher energy demand of cells to continue growth in continuous light. Overall, Cyanothece ATCC51142 appears to manifest both short-term and long-term regulatory mechanisms under high light and utilizes multiple stress resistance strategies in anticipation of future stresses. Thus, we were able to add both qualitative and quantitative information at the proteome level to obtain insights concerning cellular metabolism and provide a robust dynamic metabolic labeling strategy for quantitative measurement of the cellular proteome in Cyanothece ATCC51142 that can also be applied to other cyanobacterial systems.

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Ru-5P, ribulose-5-phosphate; SCX, strong cation exchange chromatography; SPE, solid phase extraction; SILAC, stable isotope labeling by amino acid in cell culture

’ REFERENCES (1) Savage, D. F.; Afonso, B.; Chen, A. H.; Silver, P. A. Spatially ordered dynamics of the bacterial carbon fixation machinery. Science 2010, 327 (5970), 1258–61. (2) Flores, E.; Herrero, A. Compartmentalized function through cell differentiation in filamentous cyanobacteria. Nat. Rev. Microbiol. 2010, 8 (1), 39–50. (3) Zehr, J. P.; Waterbury, J. B.; Turner, P. J.; Montoya, J. P.; Omoregie, E.; Steward, G. F.; Hansen, A.; Karl, D. M. Unicellular cyanobacteria fix N2 in the subtropical North Pacific Ocean. Nature 2001, 412 (6847), 635–8. (4) Stockel, J.; Welsh, E. A.; Liberton, M.; Kunnvakkam, R.; Aurora, R.; Pakrasi, H. B. Global transcriptomic analysis of Cyanothece 51142 reveals robust diurnal oscillation of central metabolic processes. Proc. Natl. Acad. Sci. U.S.A. 2008, 105 (16), 6156–61. (5) Toepel, J.; Welsh, E.; Summerfield, T. C.; Pakrasi, H. B.; Sherman, L. A. Differential transcriptional analysis of the cyanobacterium Cyanothece sp. strain ATCC 51142 during light-dark and continuous-light growth. J. Bacteriol. 2008, 190 (11), 3904–13. (6) Zinser, E. R.; Lindell, D.; Johnson, Z. I.; Futschik, M. E.; Steglich, C.; Coleman, M. L.; Wright, M. A.; Rector, T.; Steen, R.; McNulty, N.; Thompson, L. R.; Chisholm, S. W. Choreography of the transcriptome, photophysiology, and cell cycle of a minimal photoautotroph, prochlorococcus. PloS One 2009, 4 (4), e5135. (7) Shi, T.; Ilikchyan, I.; Rabouille, S.; Zehr, J. P. Genome-wide analysis of diel gene expression in the unicellular N(2)-fixing cyanobacterium Crocosphaera watsonii WH 8501. ISME J. 2010, 4 (5), 621–32. (8) Bandyopadhyay, A.; Stockel, J.; Min, H.; Sherman, L. A.; Pakrasi, H. B. High rates of photobiological H2 production by a cyanobacterium under aerobic conditions. Nat. Commun. 2010, 1, 139. (9) Wegener, K. M.; Singh, A. K.; Jacobs, J. M.; Elvitigala, T.; Welsh, E. A.; Keren, N.; Gritsenko, M. A.; Ghosh, B. K.; Camp, D. G., 2nd; Smith, R. D.; Pakrasi, H. B. Global proteomics reveal an atypical strategy for carbon/nitrogen assimilation by a cyanobacterium under diverse environmental perturbations. Mol. Cell. Proteomics 2010, 9 (12), 2678–89. (10) Stockel, J.; Jacobs, J. M.; Elvitigala, T. R.; Liberton, M.; Welsh, E. A.; Polpitiya, A. D.; Gritsenko, M. A.; Nicora, C. D.; Koppenaal, D. W.; Smith, R. D.; Pakrasi, H. B. Diurnal rhythms result in significant changes in the cellular protein complement in the cyanobacterium Cyanothece 51142. PloS One 2011, 6 (2), e16680. (11) Pratt, J. M.; Petty, J.; Riba-Garcia, I.; Robertson, D. H.; Gaskell, S. J.; Oliver, S. G.; Beynon, R. J. Dynamics of protein turnover, a missing dimension in proteomics. Mol. Cell. Proteomics 2002, 1 (8), 579–91. (12) Ong, S. E.; Blagoev, B.; Kratchmarova, I.; Kristensen, D. B.; Steen, H.; Pandey, A.; Mann, M. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 2002, 1 (5), 376–86. (13) Kruger, M.; Moser, M.; Ussar, S.; Thievessen, I.; Luber, C. A.; Forner, F.; Schmidt, S.; Zanivan, S.; Fassler, R.; Mann, M. SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell 2008, 134 (2), 353–64. (14) Ong, S. E.; Mann, M. Mass spectrometry-based proteomics turns quantitative. Nat. Chem. Biol. 2005, 1 (5), 252–62. (15) Doherty, M. K.; Hammond, D. E.; Clague, M. J.; Gaskell, S. J.; Beynon, R. J. Turnover of the human proteome: determination of protein intracellular stability by dynamic SILAC. J. Proteome Res. 2009, 8 (1), 104–12. (16) Beynon, R. J.; Pratt, J. M. Metabolic labeling of proteins for proteomics. Mol. Cell. Proteomics 2005, 4 (7), 857–72. (17) Rachdaoui, N.; Austin, L.; Kramer, E.; Previs, M. J.; Anderson, V. E.; Kasumov, T.; Previs, S. F. Measuring proteome dynamics in vivo: as easy as adding water? Mol. Cell. Proteomics 2009, 8 (12), 2653–63. (18) Doherty, M. K.; Beynon, R. J. Protein turnover on the scale of the proteome. Exp. Rev. Proteomics 2006, 3 (1), 97–110.

’ ASSOCIATED CONTENT

bS

Supporting Information Table S1. List of all 414 labeled proteins with their gene identification, ORF, protein product name, functional category, and RIA value over time. Table S2. List of all 1110 labeled peptides with their ORF, protein product name, and RIA value over time. LTQ-FT data files are available for download at http://omics.pnl.gov/. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Jon M. Jacobs. Tel: 509-420-4262. Fax: 509-371-6555. E-mail: [email protected].

’ ACKNOWLEDGMENT We thank all of the members of the Smith and Pakrasi Laboratories for their collegial discussions. This work is part of a Membrane Biology Scientific Grand Challenge (MBGC) project at the W.R. Wiley Environmental Molecular Science Laboratory, a national scientific user facility sponsored by the U.S. Department of Energy’s Office of Biological and Environmental Research Program and located at Pacific Northwest National Laboratory (PNNL), Richland, WA. Battelle operates PNNL for the DOE under Contract DE-ACO5-76RLO 1830. ’ ABBREVIATIONS ACN, acetonitrile; ALA, 5-aminolevulinnate; BCA, bicinchoninic acid; BNF, biological nitrogen fixation; CHAPS, 3-[(3-cholamidopropyl)dimethylammonio]-2-hydroxy-1-propanesulfonate; CID, collision induced dissociation; Cytb, cytochrome-b6f-complex; FA, formic acid; Fd, ferredoxin; FDR, false discovery rate; FNR, ferredoxin NADH oxidoreductase; F-6P, fructose-6-phosphate; G-3P, glycerol-3-phosphate; G-6P, glucose-6-phosphate; GOGAT, ferredoxin dependent glutamate synthase; GO, gene ontology; GS, glutamine synthetase; iTRAQ, isobaric tag for relative and absolute quantification; KEGG, Kyoto Encyclopedia of Genes and Genomes; LC, liquid chromatography; MS, mass spectrometry; MS/MS, tandem mass spectrometry; NCBI, National Center for Biotechnology Information; OEC, oxygen evolving complex; 2-OG, 2-oxoglurarate; PBS, phycobilisome; PC, plastocyanine; 3-PG, 3-phosphoglycerate; 6-PG, 6-phosphogluconate; PP pathway, pentose phosphate pathway; PQ, plastoquinone; PSI, photosystem I; PSII, photosystem II; RIA, relative isotope abundance; Rubisco, ribulose 1,5-bisphosphate carboxylase-oxygenase; 618



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(37) Qian, W. J.; Liu, T.; Petyuk, V. A.; Gritsenko, M. A.; Petritis, B. O.; Polpitiya, A. D.; Kaushal, A.; Xiao, W.; Finnerty, C. C.; Jeschke, M. G.; Jaitly, N.; Monroe, M. E.; Moore, R. J.; Moldawer, L. L.; Davis, R. W.; Tompkins, R. G.; Herndon, D. N.; Camp, D. G.; Smith, R. D. Large-scale multiplexed quantitative discovery proteomics enabled by the use of an (18)O-labeled 00 universal00 reference sample. J. Proteome Res. 2009, 8 (1), 290–9. (38) Polpitiya, A. D.; Qian, W. J.; Jaitly, N.; Petyuk, V. A.; Adkins, J. N.; Camp, D. G., 2nd; Anderson, G. A.; Smith, R. D. DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics 2008, 24 (13), 1556–8. (39) Kolodny, N. H.; Bauer, D.; Bryce, K.; Klucevsek, K.; Lane, A.; Medeiros, L.; Mercer, W.; Moin, S.; Park, D.; Petersen, J.; Wright, J.; Yuen, C.; Wolfson, A. J.; Allen, M. M. Effect of nitrogen source on cyanophycin synthesis in Synechocystis sp. strain PCC 6308. J. Bacteriol. 2006, 188 (3), 934–40. (40) Kanehisa, M.; Goto, S.; Kawashima, S.; Nakaya, A. The KEGG databases at GenomeNet. Nucleic Acids Res. 2002, 30 (1), 42–6. (41) Berman-Frank, I.; Lundgren, P.; Chen, Y. B.; Kupper, H.; Kolber, Z.; Bergman, B.; Falkowski, P. Segregation of nitrogen fixation and oxygenic photosynthesis in the marine cyanobacterium Trichodesmium. Science 2001, 294 (5546), 1534–7. (42) Ferjani, A.; Mustardy, L.; Sulpice, R.; Marin, K.; Suzuki, I.; Hagemann, M.; Murata, N. Glucosylglycerol, a compatible solute, sustains cell division under salt stress. Plant Physiol. 2003, 131 (4), 1628–37. (43) Dietz, K. J. Redox control, redox signaling, and redox homeostasis in plant cells. Int. Rev. Cytol. 2003, 228, 141–93. (44) Carmel-Harel, O.; Storz, G. Roles of the glutathione- and thioredoxin-dependent reduction systems in the Escherichia coli and saccharomyces cerevisiae responses to oxidative stress. Annu. Rev. Microbiol. 2000, 54, 439–61. (45) Wildman, S. G. Along the trail from Fraction I protein to Rubisco (ribulose bisphosphate carboxylase-oxygenase). Photosynth. Res. 2002, 73 (13), 243–50. (46) Grotjohann, I.; Fromme, P. Structure of cyanobacterial photosystem I. Photosynth. Res. 2005, 85 (1), 51–72. (47) Otto, A.; Bernhardt, J.; Meyer, H.; Schaffer, M.; Herbst, F. A.; Siebourg, J.; Mader, U.; Lalk, M.; Hecker, M.; Becher, D. Systems-wide temporal proteomic profiling in glucose-starved Bacillus subtilis. Nat. Commun. 2010, 1, 137. (48) Singh, A. K.; Bhattacharyya-Pakrasi, M.; Elvitigala, T.; Ghosh, B.; Aurora, R.; Pakrasi, H. B. A systems-level analysis of the effects of light quality on the metabolism of a cyanobacterium. Plant Physiol. 2009, 151 (3), 1596–608. (49) Colon-Lopez, M. S.; Sherman, L. A. Transcriptional and translational regulation of photosystem I and II genes in lightdark- and continuous-light-grown cultures of the unicellular cyanobacterium Cyanothece sp. strain ATCC 51142. J. Bacteriol. 1998, 180 (3), 519–26. (50) Schneegurt, M. A.; Sherman, D. M.; Nayar, S.; Sherman, L. A. Oscillating behavior of carbohydrate granule formation and dinitrogen fixation in the cyanobacterium Cyanothece sp. strain ATCC 51142. J. Bacteriol. 1994, 176 (6), 1586–97. (51) Roose, J. L.; Kashino, Y.; Pakrasi, H. B. The PsbQ protein defines cyanobacterial Photosystem II complexes with highest activity and stability. Proc. Natl. Acad. Sci. U.S.A. 2007, 104 (7), 2548–53. (52) Welsh, E. A.; Liberton, M.; Stockel, J.; Loh, T.; Elvitigala, T.; Wang, C.; Wollam, A.; Fulton, R. S.; Clifton, S. W.; Jacobs, J. M.; Aurora, R.; Ghosh, B. K.; Sherman, L. A.; Smith, R. D.; Wilson, R. K.; Pakrasi, H. B. The genome of Cyanothece 51142, a unicellular diazotrophic cyanobacterium important in the marine nitrogen cycle. Proc. Natl. Acad. Sci. U.S.A. 2008, 105 (39), 15094–9. (53) Bernard, S. M.; Habash, D. Z. The importance of cytosolic glutamine synthetase in nitrogen assimilation and recycling. New Phytol. 2009, 182 (3), 608–20. (54) Miflin, B. J.; Habash, D. Z. The role of glutamine synthetase and glutamate dehydrogenase in nitrogen assimilation and possibilities for improvement in the nitrogen utilization of crops. J. Exp. Bot. 2002, 53 (370), 979–87.

(19) Doherty, M. K.; Whitehead, C.; McCormack, H.; Gaskell, S. J.; Beynon, R. J. Proteome dynamics in complex organisms: using stable isotopes to monitor individual protein turnover rates. Proteomics 2005, 5 (2), 522–33. (20) Andersen, J. S.; Lam, Y. W.; Leung, A. K.; Ong, S. E.; Lyon, C. E.; Lamond, A. I.; Mann, M. Nucleolar proteome dynamics. Nature 2005, 433 (7021), 77–83. (21) Cargile, B. J.; Bundy, J. L.; Grunden, A. M.; Stephenson, J. L., Jr. Synthesis/degradation ratio mass spectrometry for measuring relative dynamic protein turnover. Anal. Chem. 2004, 76 (1), 86–97. (22) Rao, P. K.; Roxas, B. A.; Li, Q. Determination of global protein turnover in stressed mycobacterium cells using hybrid-linear ion trapfourier transform mass spectrometry. Anal. Chem. 2008, 80 (2), 396–406. (23) Lampert, F. M.; Matt, P.; Grapow, M.; Lefkovits, I.; Zerkowski, H. R.; Grussenmeyer, T. “Turnover proteome” of human atrial trabeculae. J. Proteome Res. 2007, 6 (11), 4458–68. (24) Jayapal, K. P.; Sui, S.; Philp, R. J.; Kok, Y. J.; Yap, M. G.; Griffin, T. J.; Hu, W. S. Multitagging proteomic strategy to estimate protein turnover rates in dynamic systems. J. Proteome Res. 2010, 9 (5), 2087–97. (25) Reddy, K. J.; Haskell, J. B.; Sherman, D. M.; Sherman, L. A. Unicellular, aerobic nitrogen-fixing cyanobacteria of the genus Cyanothece. J. Bacteriol. 1993, 175 (5), 1284–92. (26) Roose, J. L.; Pakrasi, H. B. Evidence that D1 processing is required for manganese binding and extrinsic protein assembly into photosystem II. J. Biol. Chem. 2004, 279 (44), 45417–22. (27) Feng, X.; Bandyopadhyay, A.; Berla, B.; Page, L.; Wu, B.; Pakrasi, H. B.; Tang, Y. J. Mixotrophic and photoheterotrophic metabolism in Cyanothece sp. ATCC 51142 under continuous light. Microbiology 2010, 156 (Pt 8), 2566–74. (28) Callister, S. J.; Dominguez, M. A.; Nicora, C. D.; Zeng, X.; Tavano, C. L.; Kaplan, S.; Donohue, T. J.; Smith, R. D.; Lipton, M. S. Application of the accurate mass and time tag approach to the proteome analysis of subcellular fractions obtained from Rhodobacter sphaeroides 2.4.1. Aerobic and photosynthetic cell cultures. J. Proteome Res. 2006, 5 (8), 1940–7. (29) Jacobs, J. M.; Diamond, D. L.; Chan, E. Y.; Gritsenko, M. A.; Qian, W.; Stastna, M.; Baas, T.; Camp, D. G., 2nd; Carithers, R. L., Jr.; Smith, R. D.; Katze, M. G. Proteome analysis of liver cells expressing a full-length hepatitis C virus (HCV) replicon and biopsy specimens of posttransplantation liver from HCV-infected patients. J. Virol. 2005, 79 (12), 7558–69. (30) Jacobs, J. M.; Mottaz, H. M.; Yu, L. R.; Anderson, D. J.; Moore, R. J.; Chen, W. N.; Auberry, K. J.; Strittmatter, E. F.; Monroe, M. E.; Thrall, B. D.; Camp, D. G., 2nd; Smith, R. D. Multidimensional proteome analysis of human mammary epithelial cells. J. Proteome Res. 2004, 3 (1), 68–75. (31) Shen, Y.; Jacobs, J. M.; Camp, D. G., 2nd; Fang, R.; Moore, R. J.; Smith, R. D.; Xiao, W.; Davis, R. W.; Tompkins, R. G. Ultrahigh-efficiency strong cation exchange LC/RPLC/MS/MS for high dynamic range characterization of the human plasma proteome. Anal. Chem. 2004, 76 (4), 1134–44. (32) Smith, R. D. Advanced mass spectrometric methods for the rapid and quantitative characterization of proteomes. Comp. Funct. Genomics 2002, 3 (2), 143–50. (33) Zimmer, J. S.; Monroe, M. E.; Qian, W. J.; Smith, R. D. Advances in proteomics data analysis and display using an accurate mass and time tag approach. Mass Spectrom. Rev. 2006, 25 (3), 450–82. (34) Monroe, M. E.; Tolic, N.; Jaitly, N.; Shaw, J. L.; Adkins, J. N.; Smith, R. D. VIPER: an advanced software package to support high-throughput LC-MS peptide identification. Bioinformatics 2007, 23 (15), 2021–3. (35) Jaitly, N.; Monroe, M. E.; Petyuk, V. A.; Clauss, T. R.; Adkins, J. N.; Smith, R. D. Robust algorithm for alignment of liquid chromatography-mass spectrometry analyses in an accurate mass and time tag data analysis pipeline. Anal. Chem. 2006, 78 (21), 7397–409. (36) Qian, W. J.; Monroe, M. E.; Liu, T.; Jacobs, J. M.; Anderson, G. A.; Shen, Y.; Moore, R. J.; Anderson, D. J.; Zhang, R.; Calvano, S. E.; Lowry, S. F.; Xiao, W.; Moldawer, L. L.; Davis, R. W.; Tompkins, R. G.; Camp, D. G., 2nd; Smith, R. D. Quantitative proteome analysis of human plasma following in vivo lipopolysaccharide administration using 16O/18O labeling and the accurate mass and time tag approach. Mol. Cell. Proteomics 2005, 4 (5), 700–9. 619


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