Proteome Turnover in the Green Alga Ostreococcus tauri by Time

ARTICLE pubs.acs.org/jpr

Proteome Turnover in the Green Alga Ostreococcus tauri by Time Course 15N Metabolic Labeling Mass Spectrometry Sarah F. Martin, Vijaya S. Munagapati, Eliane Salvo-Chirnside, Lorraine E. Kerr, and Thierry Le Bihan* Centre for Systems Biology at Edinburgh (CSBE), University of Edinburgh, CH Waddington Building, The Kings Buildings, Mayfield Road, EH9 3JD, United Kingdom

bS Supporting Information ABSTRACT: Protein synthesis and degradation determine the cellular levels of proteins, and their control hence enables organisms to respond to environmental change. Experimentally, these are little known proteome parameters; however, recently, SILAC-based mass spectrometry studies have begun to quantify turnover in the proteomes of cell lines, yeast, and animals. Here, we present a proteomescale method to quantify turnover and calculate synthesis and degradation rate constants of individual proteins in autotrophic organisms such as algae and plants. The workflow is based on the automated analysis of partial stable isotope incorporation with 15N. We applied it in a study of the unicellular pico-alga Ostreococcus tauri and observed high relative turnover in chloroplast-encoded ATPases (0.42
0.58% h
1), core photosystem II proteins (0.34
0.51% h
1), and RbcL (0.47% h
1), while nuclear-encoded RbcS2 is more stable (0.23% h
1). Mitochondrial targeted ATPases (0.14
0.16% h
1), photosystem antennae (0.09
0.14% h
1), and histones (0.07
0.1% h
1) were comparatively stable. The calculation of degradation and synthesis rate constants kdeg and ksyn confirms RbcL as the bulk contributor to overall protein turnover. This study performed over 144 h of incorporation reveals dynamics of protein complex subunits as well as isoforms targeted to different organelles. KEYWORDS: mass spectrometry, protein turnover, 15N, stable isotope labeling, unicellular alga, proteomics, Ostreococcus tauri

’ INTRODUCTION Protein turnover is an important aspect of biological processes, which has recently been the subject of large scale analyses in human cells,1,2 animals3 and plants.4 Biosynthesis and degradation rates determine the rate of protein turnover.5 In systemwide studies, experimental data for transcription are commonly used as substitutes for practical reasons, however it has been shown that RNA and protein measurements can poorly correlate in experiments of abundance and stability.3,6 The quantification of protein turnover has been a key research drive in mass spectrometry-based proteomics to generate proteome-scale results. In cell culture and animal models, pulsed7 and dynamic2 stable isotope labeling with amino acids in culture (SILAC) methods have been developed to this end, while in microbial8,9 and photoautotrophic organisms10
13 stable isotope labeling with 13C and 2H atoms has been applied. Metabolic labeling with stable isotopes such as 15N, 13C and 2 H at near 100% is routinely applied as a strategy to compare two experimental conditions.14,15 Labeling with 15N to distinguish between conditions is widely used in plant proteomics16
20 and has been recently applied to studies on algae.21,22 15N labeling carries the benefit over 13C that it can be experimentally achieved with the substitution of inorganic nitrogen without requiring atmospheric control (except for plant species undergoing symbiotic nitrogen fixation). Unlike 2H, labeling with 15N has been shown r 2011 American Chemical Society

not to affect plant growth.13,19 Commercial software is available for the analysis of differentially labeled, pooled samples at a fixed 15 N incorporation efficiency.23 Partial metabolic labeling of proteins with stable isotopes was long avoided due to the complexity of the resulting combinatorial distribution of isotopomers.24 The simulation of data resulting from partial labeling with 15N has been pioneered for the more accurate quantification of differential proteomic studies, as reagent purity and experimental conditions preclude the complete substitution of all nitrogen atoms with 15N, which has obvious implications for the accuracy of quantification.22,24,25 Several theoretical approaches predicting isotope patterns26
28 and studing the implications of the presence of labeled isotopic peaks on peptide identification have been reported.10,25,29,30 Algorithms designed to analyze the time-domain integrals of the 14N and 15N monoisotope peaks have been developed31 and further modified to accommodate partial labeling.32 Analyses of LC-FTICRMS20,24 and MALDI33 data have demonstrated the feasibility of utilizing partial labeling with 15N as an automated quantification strategy. Partial labeling with 15N has been studied elegantly in Sulfolobus solfataricus by analyzing MS-peak distributions and Special Issue: Microbial and Plant Proteomics Received: September 14, 2011 Published: November 14, 2011 476

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calculating the labeling efficiency and incorporation efficiency of five proteins.34 As defined in this study, the incorporation efficiency depends on the availability of 14N and 15N and enables an estimate of recycling of unlabeled amino acids. As these are supplied by the mechanism of protein degradation, protein turnover could be estimated from these data. We have developed an automated approach to quantify turnover, synthesis and degradation rates of individual proteins and performed a proteomic analysis of the picophytoplankton and key plant model Ostreococcus tauri (O. tauri). Due to its simplicity, O. tauri is a model of choice for circadian orchestration35,36 and photosynthetic evolution37,38 studies. The compact O. tauri genome (8200 genes in 12.5 Mb) has been fully sequenced,39,40 furthermore facilitating its analysis by mass spectrometry-based proteomics. A global study using comparative 15N labeling revealed the proteome response to diurnal cycles and low nitrogen conditions.22 Algae, like plants, rely on endogenous signaling, mediated by changes in protein turnover rates, to initiate rapid responses13 and adapt to given environmental conditions and the long-term availability of energy sources.41 To apply a systems biology approach to the study of plant behavior, it is necessary to develop large-scale strategies to measure protein turnover. The measurement of rates to supplement total abundance levels is crucial, as the balance of synthesis and degradation can not be inferred from changes in overall abundance, which is yet still erroneously referred to as protein dynamics.9,42 We report here the first turnover proteomic study based on the automated analysis of 15N incorporation in a unicellular alga and present a broadly applicable analysis platform for MS-peak quantification.

Figure 1. Schematic overview of the analysis workflow. Peptide identification and retention time information is computed by MaxQuant in conjunction with Mascot as a combined label-free analysis of all .RAW files. Files are converted with ReAdW and extracted with tailored mass calculation and averaging scripts. The resulting mass/intensity spectra are fitted in a further analysis script.

sampled by centrifuging 30 mL culture (as above) and washing pellets with 1 mL PBS before centrifuging again (5 min, 12000 g). Pipetting up and down with 200 μL 2 M urea enabled full pellet resuspension and cell lysis. To reproduce these methods for absolute quantification, full protein denaturation should be ensured with a higher urea concentration. Samples were stored at
20 °C before digestion.

’ MATERIALS AND METHODS

Culture Density Monitoring

Culture densities were monitored throughout the time course. 1 mL of culture was concentrated to 100 μL by centrifugation (5 min, 12000 g) and resuspension. Optical densities were measured by absorbance at 600 nm (Nanodrop ND1000, LabTech, U.K.). Diluted cell suspensions (1:100 and 1:500) were monitored by FACS (Fluorescence Activated Cell Sorting) to determine cell density and purity.

Ostreococcus tauri Media and Culture

Ostreococcus tauri OTTH059543 were cultured as previously described.22 Cultures were split weekly to 1 part in 50 to ensure continuous growth. In preparation for labeled experiments, cultures were passaged twice 1:50 into media containing either 14 N sodium nitrate and 14N ammonium chloride for light (L) cultures (both from Sigma Aldrich, U.K.) or 15N sodium nitrate (98% pure) and 15N ammonium chloride (99% pure) for heavy (H) cultures (both 15N stable isotope compounds from Cambridge Isotope Laboratories). Triplicates of each L and H culture were incubated (Sanyo MLR-350) in vented tissue culture 175 cm2 flasks (Sarstedt, U.K.) at 20 °C in 12 h light (100 μmol photon m
2 s
1, filtered with 724 Ocean Blue, Lee Filters)/12 h dark cycles for 8 days, by which point cultures had grown to an optical density of 0.1 mm
1 at 600 nm equivalent to approximately ∼10k cells per μL, or 700 μg protein per 100 mL. To assess final 15N nitrogen incorporation, 15 mL was removed from each of the original cultures and equal volumes of L and H mixed before digesting as described below.

Digestion and Peptide Cleaning

Samples were reduced with 12.5 μL each of dithiothreithol 200 mM and ammonium bicarbonate 1 M for 30 min at RT. 12.5 μL iodoacetamide 500 mM and 5 μg sequencing grade porcine trypsin (Roche, UK) were added for alkylation and digestion overnight. Ten μL digest were diluted in 20 μL buffer A (95.5% HPLC grade water, 2.5% HPLC grade acetonitrile, both Fisher, U.K., 2% formic acid, Suprapure Merck, Germany), cleaned on Stagetips,44 eluted in 10 μL buffer B (90% acetonitrile, 0.1% formic acid, 0.025% trifluoroacetic acid, sequencing grade, Sigma, U.K.), vacuum-dried (RC 10
10, Thermo, U.K.) and stored at
20 °C.

Pulsed Experiments and Harvest

LC
MS Analysis

Dried samples were resuspended in 11 μL buffer A (90% water, 10% acetonitrile, 0.1% formic acid) and analyzed on a capillary-HPLC
MS/MS system (1200 binary HPLC, Agilent, U.K., coupled to a hybrid LTQ-Orbitrap XL mass spectrometer, controlled by Xcalibur version 2.0.7, Thermo-Fisher, U.K.) in 140 min gradients including column conditioning as described previously.45 Data-dependent acquisition was performed with one profile-mode FT-mode survey scan at 60k resolution followed by 5 MS/MS scans in IT mode.

To exchange culture media, 280 mL of each culture was centrifuged (3200 g, 10 min). Supernatants were decanted and filtered at 0.22 μm. 14N cell pellets were resuspended in 15N supernatants and vice versa and replaced in tissue culture flasks and incubated. Filtered supernatants were tested for residual extracellular protein content by concentrating 1:10, and separating by SDS-PAGE. No protein bands were observed by Coomassie staining. Incorporation was monitored by sampling at initially 12 h and then 24 h intervals for six days. Whole cell lysate was 477

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N Labeling Analysis: Intensity Estimation and Peak Fitting. In a simplified form of the theoretical calculation described by Kubinyi,27 intensities of isotopic peak values were calculated based on the atmospheric probability distributions28 of up to two atoms of 2H, 33S, 34S and 18O, up to five atoms of 13C and unrestricted atoms of 15N. The latter allows for the peptidedependent variable input of 15N up to 99%. The first 5 isotopic peaks of each peptide were fitted with a natural nitrogen distribution, and the envelope intensity (light L) noted. The sixth to last isotopic peaks were iteratively fitted with 15 N % ranging from 30% to 99% in 0.5% steps, and the incorporation percentage and envelope intensity of the best leastsquares fit (heavy H) noted.

Data Analysis

Protein Identification and Label-free Analysis. An overview of the analysis workflow is presented as a schematic in Figure 1. Raw files were searched using MaxQuant (version 1.0.13.8 in singlet mode)46 in conjunction with MASCOT (version 2.3, Matrix Science) and the O. tauri subset of the NCBI protein database (March 2008, 9442 sequences) combined with the default contaminant list and reversed sequences. The search parameters were: 7 ppm MS-tolerance, 0.4 Da MS/MS-tolerance, 0.01 FDR, fixed carbamidomethylation, variable n-terminal acetylation, oxidation (M), phospho (ST, Y) using a maximum missed cut of 2. Proteins were ranked in descending intensity defined at time point 1, and the top 40 proteins were taken forward. For each protein, the two to three most intense peptides were selected for the labeling analysis and their sequence, retention time and intensity at time point t1 were noted. Six of the 40 examined proteins lacked identifying annotation in our protein database. A BlastP (NCBI) search revealed high similarity of 3 of the 6 proteins with annotated proteins, which was used to identify these unnamed protein products. The protein annotated as gi|116054806| returned the chlorophyll a/bbinding protein CP26 precursor Lhc b5 (gb|AAY27549.1|, e-value of 8e-177). The protein annotated as gi|116060183| returned the glycine cleavage system t-protein (gb|ACO65076.1|, evalue of 2  10
156) and the protein annotated as gi| 112806897| matched the ATPase subunit 1 (YP_717281.1, evalue of 0.0). Protein localization was determined by two methods. Where available, it was extracted from GO annotation for O. tauri. These results were supplemented by BlastP (NCBI) searching against the A.thaliana subset of NCBI and retrieving localization information from TAIR (www.arabidopsis.org). Localization as well as Ot and At gene locus tags are presented in Supporting Information Table ST1. 15

N Labeling Analysis: Estimation of Isotopomer Masses and Data Extraction. Raw files were viewed in Xcalibur (version 2.0.7, Thermo-Fisher). For quantification, raw files were converted to centroid mzXML format using ReAdW (version 4.3.1 from tools.proteomecenter.org/ReAdW.php). A Perl script (peptideextraction.pl) was developed using the “unpack” function to translate the binary code into x/y table format. For each of the 2232 examined peptide spectra, data were extracted for a 3 min window around the retention time. The peak masses were estimated for each peak in the range from the 14N monoisotope peak to the 15N monoisotope peak plus 5 heavier isotope peaks. For the first five peaks, weighted averages of the dominant 15Nand 13C- contributions to each isotopic peak were calculated based on natural isotope distributions (p[12C] = 0.98893, p[13C] = 0.01107, p[14N] = 0.99634, p[15N] = 0.00366).28 For each subsequent peak (number n), the masses were calculated by a weighted average of the contribution of three peaks A = [15N*n], B = [15N*(n
1) + 13C*1] and C = [15N*(n
2) + 13 C*2], so the mass of the sixth isotopic peak for example is approximated by averaging the masses of the peaks A = 15N*6, B = 15N*5 + 13C*1 and C = 15N*4 + 13C*2 . The weighting ratio of A:B:C of 20:10:1 was empirically derived as an average over the incorporation range of interest, peptide length and composition. Experimental mass/intensity pairs were written to Excel files, and an intensity-weighted average (weightedaverage.pl) was used to group data extracted from all MS scans in the retention time window by mass. Standard deviations on the averaged data were also calculated, providing an estimate of intensity variation.

Turnover calculation

Triplicates of up to 3 peptides per protein were extracted and analyzed as above. The ratio of the heavy (H) species intensity over total protein intensity (H+L) was chosen as a measure of turnover.47 Often in proteomic turnover studies the H/L is quoted as the synthesis/degradation ratio, however this does not take into account the degradation of the H species and hence underestimates the actual synthesis/degradation ratio ksyn/kdeg. Plotting results as H/(H+L) enables the estimation of the initial linear time-dependence by linear regression, where the slope (divided by 100) returns the initial turnover rate in % per hour. Nonlinear behavior indicates either nonconstant rates, or dominant synthesis (resulting in a rise to plateau) or degradation (resulting in an exponential rise). Averaged peptide H/(H+L) values for each protein were plotted as a function of time and linear slopes and R2 values determined by linear regression.

’ RESULTS AND DISCUSSION To quantify turnover of individual O. tauri proteins, stable isotope labeling mass spectrometry experiments were performed. MS spectra were analyzed by adapting existing theoretical work24,27 and developing a novel suite of Perl scripts named Protein TurnStILE (Turnover by Stable Isotope Labeling Experiments). These automatically extract, average and fit spectral data specifically for peptides incorporating 15N across the full range of potential isotopomers. A schematic diagram of the analysis workflow is presented in Figure 1. Protein TurnStILE Perl scripts are available on request from the authors. Automated Data Extraction

Peak intensities were extracted from mzXML files for the full labeling range for each peptide. The 14N monoisotope peak mass of a peptide is the sum of C, H, O, S and N atoms of each amino acid (accounting for carbamidomethylated cysteines) plus the masses of terminating H and OH groups and the ionizing protons. The mass estimation of all further peaks is based on a simple empirical model of 15N and 13C contributions and achieves sufficient accuracy to enable the extraction of intensity values within a (7 ppm narrow window for all peptides in this study. Figure 2A illustrates the extraction of a peptide in a 1:1 mixture of natural (L) and labeled-to-completion with 15N (H) cultures, highlighting the distinction between peptide peaks and background ions. The precision of this (7 ppm narrow window of extraction adds the benefit of substantial noise reduction, as typically >99% or the mass range
and hence interfering ions
is excluded at the data extraction stage. By contrast, the default setting for a base peak comparison in Xcalibur is in the order of 1 part per thousand. For research focusing specifically on long

478

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Figure 2. (A) Mass spectrum of a 1:1 pooled natural (14N): fully labeled sample (15N) of peptide NDVGDTIK, 2+, exhibiting labeling at 96% after 2 passages. Isotope peaks corresponding to peptide NDVGTIK, 2+ charged, are shown in black, distinct from background ions at masses out with (7 ppm (ppm) in gray. Diamonds indicate the automatically extracted and averaged peaks following the peptide-specific mass calculation. (B) Histogram of the labeling distribution in O. tauri, based on 200 peptides. An incorporation of 95.5 ( 2% was observed, indicative of the reagent purity.22

peptides, the ratio A:B:C of 20:10:1 (as defined in Materials and Methods) can be readily adapted in the scripts to reflect an increased 13C contribution, by increasing the values for B and C. In order to allow for any variability in retention time and to detect full peaks from 24 sequentially LC
MS analyzed samples (8 triplicate time points), a relatively broad elution window of (90 s was used. To validate this approach, the retention times reported by MaxQuant were compared for peptides identified in several runs. Typical standard deviations in retention time were between 10 to 40 s. The values obtained by our peak extraction were compared to those reported by MaxQuant for the light L species of 40 proteins. Peptide intensities of 2
3 peptides were summed and plotted against MaxQuant intensities (sums of all identified peptides). The Pearsons R2 coefficient was 0.92, demonstrating a good correlation between the two different extraction and summation approaches (see Supplementary Figure S1, Supporting Information). Automated Fit of 15N Incorporation

The time-averaged peaks were fitted with simulated isotopomer distributions to determine 15N incorporation and the intensities of L and H species. The least-squares fit was performed in two stages. First it returned an L intensity based on a fit of the first 5 peaks with a natural distribution (0.3663% 15N). This fit assumes no low-level incorporation (e.g., 5%), which was confirmed by extensive visual inspection of the data. Indeed, lowlevel incorporation would alter the peak masses sufficiently to impede extraction at 7 ppm, as the contribution of the 15N peak shifts the peak mass toward higher value. This shift is due to the fact that the mass difference between isotopes is not equal to exactly one, due to subtle differences in the binding energies of neutrons. In fact, ΔC = 13C
12C = 1.0034, while ΔN = 15N
14 N = 0.997.28 In composite peaks, this difference can lead to substantial shifts, depending on the relative intensities of the constituents. A second fit returns the heavy labeled species intensity (H) as well as the percentage of nitrogen atoms substituted with 15N, based on a least-squares fit of the highest peak in the H range (2 neighboring peaks. Figure 2B shows a histogram of the percentage of substitution of 15N for 14N atoms in the 200 most intense peptides (referred to

Figure 3. Schematic overview of experimental design and size. Cultures passaged in 15N for 14N containing seawater were centrifuged, supernatants removed, filtered and exchanged. Cells were sampled in 8 time points during 144 h of further incubation.

as % 15N incorporation) from an O. tauri culture grown under 2 successive passages of 15N media (1:50 dilution) which corresponds to a theoretical maximum incorporation of 97.96% using reagent purities stated by manufacturers. Final incorporation was estimated at 95.5 ( 2% which is close to the theoretical limit. However, the width of the distribution even at completion labeling suggests caution must be used when making fixed incorporation assumptions.24 The difference in a fitted intensity at a fixed 93 or 97% can be up to 2-fold, an error source avoided in a fit that enables peptide-specific variable incorporation as described in this work. 479

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Protein Turnover Experiments

Having developed the automated analysis workflow, we applied it to the study of turnover in the unicellular alga O. tauri. In previous proteomic experiments including circadian regulation and nutrient signaling, O. tauri cells were harvested at 7
10 days after a 1:50 to 1:100 cell passage into new media.22 Quantification within this time scale is most relevant to other research conducted on O. tauri and this experimental design hence enables us to relate these findings on synthesis and degradation to other data sets that have been sampled at similar culture densities and nutrient levels. Figure 3 illustrates the experimental design. Following the exchange of filtered media, cells were sampled at four 12 h intervals beginning at the first light-to-dark transition to capture translation at day and night. A further four samples were taken at 24 h intervals thereafter to cover 6 days (144 h) of protein synthesis and degradation. This study was specifically designed to simulate unperturbed conditions (aside from the unavoidable media exchange) 7 days after passage, hence the exchange of used seawater from the two cultures rather than the addition of new media. Culture density was monitored throughout and slow linear growth observed, corresponding to a doubling time of 3.95 days. Crucially, the seawater exchange did not impact on cell growth, with 14N, 15N and both swapped cultures exhibiting identical growth (data not shown). 15

Figure 4. Extracted spectral peaks (points) and fitted simulations (lines) for three peptides over the duration of the time course. Peptides are constituents of the proteins (A) AtpB, (B) RbcS2 and (C) Histone H3 which exhibit markedly different turnover properties. The inset in (A) shows the normalized (to a maximum value of 1) experimental data in heavy H range, displaying gradual incorporation of the 15N label, rising from 15N:14N = 56:44 at t = 24 h to 15N:14N = 68:32 by the end of the time course.

N Incorporation over Time

Over 2200 peptide spectra were analyzed using our automated workflow. The list of peptides is detailed in Supplementary Table ST2 (Supporting Information). All spectra and fits were plotted and verified by visual inspection. The fitting process was found to be robust with the rare exception of low intensity spectra in which peaks on either side of the heavy range were the most intense (peaks 6 or 15N+4 or 15N+5). This arose in the absence of a heavy H species (e.g., shortly after exchanging media) or in the presence of interfering ions. Eight extracted spectra for each of three peptides are shown in Figure 4. They are constituents of the proteins AtpB, RbcS2 and Histone H3 respectively, and exemplify distinct patterns of 15N incorporation and turnover rates. Incorporation of 15N results in a shift of the H peaks to the higher mass range, while turnover determines the relative intensities of the L and H peaks. The peptide with the sequence VIDTANLANSK is a medium-length peptide (C48 N14 H85 O18, 573.3117 amu at 2+ charge) with the H species accounting for 60% of the total pool within 144 h. This de novo synthesis is accompanied by a marked decrease of the L component, reflecting a measure of the degradation of the L species). At the 24 h time point a low-intensity H species is observed approximately halfway between the light (14N) species, and where we would expect a fully labeled heavy species to appear (in which all the atoms were 15N). Fitting returns that protein synthesis occurs at an incorporation ratio of 15N:14N = 56:44, making over half the nitrogen atoms in the de novo synthesized heavy species from the new 15N sources. This demonstrates substantial reuse of the existing pool of 14N as well as incorporation from new 15N sources. The incorporation ratio rises and plateaus at 15N:14N = 68:32, which is illustrated by the normalized H peak in the inset in Figure 4A. Initially, the H peak exhibits a broad range of 15N incorporation, reflective of an inhomogeneous labeling distribution. The tail of this distribution narrows as the final incorporation level is reached. The automated

workflow fits data as a single labeled species, leading to a potential underestimate of the total H population. The peptide associated with the sequence MFETFSFLPPLSDAEIAK (C96 H144 N19 O28 S1, 1022.0108 amu at 2+ charge) is a relatively large peptide with a correspondingly broader isotopomer range. Moreover, peptide fingerprint distortion due to a large number of carbon atoms is clearly visible as intense second and third isotopomers in figure 4B. Again we observed a slow initial incorporation of 15N:14N = 60:40 at 24 h, rising to 15N:14N = 73:27 at 144 h. The turnover however, which is a function of the relative intensities, is slower, with the H species making up less than 30% of total protein by the end of the time course. The peptide associated with the sequence STELLIR (C36 H67 N10 O12 at 416.2504 as a 2+) is a short peptide illustrated in Figure 4C. The very low intensity of the H species indicates turnover of 0.97 for all other examined proteins) enables the distinction of proteins that should not be quantified by this method. Figure 5 shows the relative protein turnover of 39 proteins categorized by subcellular organization. While turnover of the 4 nuclear proteins is uniformly low, turnover in the other categories varies significantly, showing no further localization-based trend. An examination of the suborganelle location and function of individual proteins is required to understand the significance of these numbers. Turnover Rates of Individual Proteins

An interesting feature of O. tauri as a model organism for protein turnover is its compact small genome. In higher model organisms including plants, gene redundancy and protein 482

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Figure 6. Time courses of selected proteins. Intensity of the heavy species over the total intensity (H/(H+L)) versus time for the 20 most intense proteins are shown in (A) rank 1
10 in intensity and (B) rank 11
20 in intensity. Rank 21
40 in intensity are presented in Supplementary Figure S5 (Supporting Information). Further proteins in the top 40 most intense proteins are contrasted in (C) Rubisco and ATP synthase subunits, (D) Chloroplast proteins glyceraldehyde-3-phosphate dehydrogenase subunits A and B, Photo system II oxygen-evolving enhancers 1 and 3, as well as central PSII proteins PsbB and PsbC. Error bars denote one standard deviation based on the combination of errors from up to 9 spectra.

eighth), potentially pointing to a distinct turnover-based control mechanism of proteins translated in the chloroplast. A further high-turnover photosynthesis-related protein was RuBisCO activase (0.42% per hour, ranked at sixth), which is required for the release of RuBP from the catalytic site of RuBisCO.52 Seven photosystem II (PSII) proteins were analyzed: PsbB, PsbC, PsbH, OEE1, OEE3, CP26/Lhcb5 and CP29/Lhcb4. The light-dependent repair cycle, involving disassembly, replacement and reassembly of the PSII complex, has been studied extensively, focusing in particular on the turnover of the D1 core protein (PsbA) by radiolabeling.53,54 PSII is specifically disassembled in order to ensure a rapid turnover of PsbA in photodamaging conditions, with complete decay reported within 5
10 h in A. thaliana.55 We identified two core-surrounding proteins, PsbB and C, which are also targeted for degradation during the repair cycle, calculated at 0.34% and 0.51% per hour respectively (ranking 10th and second) but consistently at a lower rate than values reported for PsbA, which may reach 30% per hour in extreme conditions.55 The light harvesting antennae CP26 and the CP29like protein have a slow turnover at 0.12% and 0.09% per hour respectively, ranking 31st and 37th out of 40. The photosystem II associated oxygen evolving enhancer proteins OEE1 and 3 also exhibited marked slow turnover at 0.15% and 0.13% per hour respectively (ranking 27th and 29th). In this study, the histone family were the proteins with the slowest turnover rate, with H2A and H2B at 0.10% per hour (33rd and 34th) ahead of H3 at 0.08% (38th) and H4 at 0.07% (39th). This result is in agreement with SILAC-based measurements

in HeLa cells, which found histones to be generally stable, with H2A displaying a turnover approximately 10% faster than H3.56 Protein turnover rates in HeLa cells were however consistently higher, providing only a crude comparison to the dynamics observed in O. tauri. This could be explained by the higher cell division rate of HeLa cells.57 Figure 6 shows the time course traces of 15N incorporation for a number of proteins. The first four time point samples were taken at 12 h intervals at dusk and dawn, enabling the direct comparison of illumination-dependency on protein synthesis. In this study of O. tauri, most proteins ceased or sharply decreased de novo synthesis during the dark period. In particular, PSII proteins exhibit a complete halt with light withdrawal, as may be expected in a repair cycle with activity directly linked to the intensity of illumination.53 Estimates of protein translation in day and dark have been reported in A. thaliana and correlated with the switch from photosynthetic to fixed carbon energy sources.4 Here the rate of synthesis was reported 50
100% increased in light period compared to dark periods. The observed relative turnover in O. tauri, as illustrated in Figure 6, is considerably lower in darkness, with little change in H/(H+L) signal in the two dark intervals 12
24 and 36
48 h. This corresponds to a sharp decrease in the de novo synthesis rate constant, as displayed for chloroplast ATPases in Figure 7C. Comparing protein-for-protein, O. tauri turnover patterns exhibit some similarity with A. thaliana, based on a comprehensive translation study of ribosome activity estimates for metabolism enzymes.4 RuBisCO was ranked as one of the highest turnover proteins in both cases. Further rapidly synthesized 483

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Figure 7. L and H intensities plotted against time for chloroplast (A, B) and mitochondrial (D, E) AtpA and AtpB isoforms. This data was used to calculate the rate constant of synthesis of all four isoforms (C). The two mitochondrial isoforms are displayed separately in (F). The presented data was based on absolute L and H peak intensities of peptides averaged over 3 LC
MS runs. Error bars hence reflect the label-free intensity variability between runs (in contrast to the relative intensities in Figure 6).

enzymes identified in both organisms included Calvin cycle glyceraldehyde 3-phosphate dehydrogenase (GapA, B) and pyruvate kinase (PPDK). Nitrogen assimilation and photorespiration have been identified as protein functions associated with high de novo synthesis rates,4 clearly joined by ATP synthesis in chloroplast membranes and photosynthesis in the PSII core, while peripheral PSII proteins are comparatively stable (OEE1, OEE3, Ferredoxin).

and dH ¼ ksyn
kdeg H dt

Degradation rates can hence be calculated from the decrease in the L peak, where kdeg ¼


Rate Constants for Degradation and Synthesis

Using a simple model of the contributions of degradation and synthesis to the L and H peaks we observe, the rate constants for degradation (kdeg, in units of h
1) and the rate constant for de novo synthesis (ksyn, in intensity h
1) can be estimated. One assumption is that addition and loss of a peptide isoform are due to synthesis and degradation only, not peptide modification. In conditions where synthesis and degradation rates are constants, the change in the abundance of a protein P can be written as:58 dP ¼ ksyn
kdeg P dt

Lmþ1
Lm 1 tmþ1
tm Lm

ð4Þ

for each time point tm. The measurement of kdeg is extracted by comparing sequential runs (i.e., similar to an intensity-based label-free LC
MS quantitation) of the natural isotope distribution between time point samples. It is hence subject to variability when calculated from a set of experiments designed for a comparison of labeled states without the normalization of sample concentrations. As the H species rises in abundance, it is also increasingly subjected to degradation. Synthesis rates can be separated from the simultaneously occurring degradation in the H peak and expressed as:

ð1Þ

ksyn ¼


Hmþ1
Hm þ kdeg H tmþ1
tm

ð5Þ

for each time point tm. As both H and L peak measurements are used to calculate ksyn, the relative measurements are comparatively low in experimental variability. Degradation and synthesis rates were calculated for each time point interval (tn+1
tn). Averages of four 24 h intervals (48
144 h) are presented in Table 1. With a 15-fold higher than average synthesis rate, RbcL was the largest contributor to absolute protein turnover, in line with experiments in A. thaliana.4 The second highest absolute synthesis rate was chloroplast AtpA

giving P = (ksyn/kdeg) during linear growth, where kgrowth = dP/dt is contained in ksyn. The light peak intensity (L) is subject to degradation only, while the heavy peak intensity (H) is a composite of synthesis and degradation. dL ¼
kdeg L dt

ð3Þ

ð2Þ 484

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Journal of Proteome Research at 4-fold the average. Combined with an absolute quantification strategy (e.g., with spiked peptides), this method could be used to calculate absolute synthesis rates in copy numbers per hour and a detailed analysis of energy balance and trade-offs in protein production under limiting conditions. Plots of L and H intensities as well as ksyn rates for four ATPase isoforms are shown in Figure 7. Chloroplast isoforms were approximately five times more abundant than the mitochondrial ones. They also exhibited significantly higher turnover rates, with chloroplast synthesis on average 30 times higher than its mitochondrial counterpart. In the chloroplast isoforms we observed 3
4 fold reduced protein synthesis rates in dark periods compared to under illumination (see Figure 7C). These results highlight the influence of diurnal rhythms, cellular localization and proximity to sources oxidative damage.

ARTICLE

’ REFERENCES (1) Pratt, J. M.; Petty, J.; Riba-Garcia, I.; Robertson, D. H. L.; Gaskell, S. J.; Oliver, S. G.; Beynon, R. J. Dynamics of protein turnover, a missing dimension in proteomics. Mol. Cell. Proteomics 2002, 1, 579–591. (2) 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, 104–112. (3) Doherty, M. K.; Beynon, R. J. Protein turnover on the scale of the proteome. Expert Rev. Proteomics 2006, 3, 97–110. (4) Piques, M.; Schulze, W. X.; H€ ohne, M.; Usadel, B.; Gibon, Y.; Rohwer, J.; Stitt, M. Ribosome and transcript copy numbers, polysome occupancy and enzyme dynamics in Arabidopsis. Mol. Syst. Biol. 2009, 5, 314. (5) Doherty, M. K.; Whitfield, P. D. Proteomics moves from expression to turnover: update and future perspective. Expert Rev. Proteomics 2011, 8, 325–334. (6) Schwanh€ausser, B.; Busse, D.; Li, N.; Dittmar, G.; Schuchhardt, J.; Wolf, J.; Chen, W.; Selbach, M. Global quantification of mammalian gene expression control. Nature 2011, 473, 337–342. (7) Schwanh€ausser, B.; Gossen, M.; Dittmar, G.; Selbach, M. Global analysis of cellular protein translation by pulsed SILAC. Proteomics 2009, 9, 205–209. (8) Taubert, M.; Jehmlich, N.; Vogt, C.; Richnow, H. H.; Schmidt, F.; von Bergen, M.; Seifert, J. Time resolved protein-based stable isotope probing (Protein-SIP) analysis allows quantification of induced proteins in substrate shift experiments. Proteomics 2011, 11, 2265–2274. (9) Cargile, B. J.; Bundy, J. L.; Grunden, A. M.; Stephenson, J. L. Synthesis/degradation ratio mass spectrometry for measuring relative dynamic protein turnover. Anal. Chem. 2004, 76, 86–97. (10) Whitelegge, J. P.; Katz, J. E.; Pihakari, K. A.; Hale, R.; Aguilera, R.; Gomez, S. M.; Faull, K. F.; Vavilin, D.; Vermaas, W. Subtle modification of isotope ratio proteomics; an integrated strategy for expression proteomics. Phytochemistry 2004, 65, 1507–1515. (11) Shastri, A. A.; Morgan, J. A. A transient isotopic labeling methodology for 13C metabolic flux analysis of photoautotrophic microorganisms. Phytochemistry 2007, 68, 2302–2312. (12) Allen, D. K.; Shachar-Hill, Y.; Ohlrogge, J. B. Compartmentspecific labeling information in 13C metabolic flux analysis of plants. Phytochemistry 2007, 68, 2197–2210. (13) Yang, X.-Y.; Chen, W.-P.; Rendahl, A. K.; Hegeman, A. D.; Gray, W. M.; Cohen, J. D. Measuring the turnover rates of Arabidopsis proteins using deuterium oxide: an auxin signaling case study. Plant J. 2010, 63, 680–695. (14) Beynon, R. J.; Pratt, J. M. Metabolic labeling of proteins for proteomics. Mol. Cell. Proteomics 2005, 4, 857–872. (15) Palmblad, M.; Bindschedler, L. V.; Cramer, R. Quantitative proteomics using uniform (15)N-labeling, MASCOT, and the transproteomic pipeline. Proteomics 2007, 7, 3462–3469. (16) Dunkley, T. P. J.; Hester, S.; Shadforth, I. P.; Runions, J.; Weimar, T.; Hanton, S. L.; Griffin, J. L.; Bessant, C.; Brandizzi, F.; Hawes, C.; Watson, R. B.; Dupree, P.; Lilley, K. S. Mapping the Arabidopsis organelle proteome. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 6518–6523. (17) Engelsberger, W. R.; Erban, A.; Kopka, J.; Schulze, W. X. Metabolic labeling of plant cell cultures with K15NO3 as a tool for quantitative analysis of proteins and metabolites. Plant Methods 2006, 2, 14. (18) Lanquar, V.; Kuhn, L.; Lelievre, F.; Khafif, M.; Espagne, C.; Bruley, C.; Barbier-Brygoo, H.; Garin, J.; Thomine, S. 15N-metabolic labeling for comparative plasma membrane proteomics in Arabidopsis cells. Proteomics 2007, 7, 750–754. (19) Bindschedler, L. V.; Palmblad, M.; Cramer, R. Hydroponic isotope labelling of entire plants (HILEP) for quantitative plant proteomics; an oxidative stress case study. Phytochemistry 2008, 69, 1962–1972. (20) Palmblad, M.; Mills, D. J.; Bindschedler, L. V. Heat-shock response in Arabidopsis thaliana explored by multiplexed quantitative

’ CONCLUDING REMARKS In this study of protein turnover in O. tauri over 6 days, we have demonstrated the automated analysis of partial 15N-labeled MS-level peptide spectra, and the suitability of in vivo stable isotope labeling incorporation for the quantification of protein turnover in algae. This complements recent efforts with labeled amino acids in cell lines and animals. The analysis of isotopomer distributions at MS level is a powerful method to measure in parallel the nutrient uptake and flux into the proteome by means of the substitution of 15N atoms, as well as synthesis and degradation turnover kinetics of individual proteins. Automated approaches that center on time domain integration of monoisotope peaks have been reported, however these do not capture the time-dependent incorporation of different levels of 15N from new sources. The presented method lends itself to a combined study of proteome with existing metabolomics methods to study, for example, nutrient limiting conditions. In a single experiment, protein turnover as well as the flux of nitrogen through the proteome and metabolome could be monitored in parallel. A better understanding of the control of inorganic nitrogen assimilation and recycling from proteins during plant growth may enable genetic improvement and result in agronomic applications. ’ ASSOCIATED CONTENT

bS

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

’ AUTHOR INFORMATION Corresponding Author

*Thierry Le Bihan, e-mail: [email protected]. Tel: +44 (0)131 651 9073. Fax: +44 (0)131 651 9068.

’ ACKNOWLEDGMENT S.F.M., E.S.C., L.E.K. and T.L.B. are funded by the Centre for Systems Biology at Edinburgh (CSBE) which is a Centre for Integrative Systems Biology (CISB) funded by the BBSRC and EPSRC; reference BB/D019621/1. We thank Prof. Anthony Trewavas, Prof. Andrew Millar, Dr. Natasha Savage and Dr. Mark Pogson for valuable discussions. 485

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