Integrated Quantitative Analysis of Nitrogen Stress Response in

Article pubs.acs.org/jpr

Integrated Quantitative Analysis of Nitrogen Stress Response in Chlamydomonas reinhardtii Using Metabolite and Protein Profiling Nishikant Wase,† Paul N. Black,† Bruce A. Stanley,‡ and Concetta C. DiRusso*,† †

Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, Nebraska 68588, United States Section of Research Resources, Pennsylvania State University College of Medicine, Hershey, Pennsylvania 17033, United States

‡

S Supporting Information *

ABSTRACT: Nitrogen starvation induces a global stress response in microalgae that results in the accumulation of lipids as a potential source of biofuel. Using GC-MS-based metabolite and iTRAQ-labeled protein profiling, we examined and correlated the metabolic and proteomic response of Chlamydomonas reinhardtii under nitrogen stress. Key amino acids and metabolites involved in nitrogen sparing pathways, methyl group transfer reactions, and energy production were decreased in abundance, whereas certain fatty acids, citric acid, methionine, citramalic acid, triethanolamine, nicotianamine, trehalose, and sorbitol were increased in abundance. Proteins involved in nitrogen assimilation, amino acid metabolism, oxidative phosphorylation, glycolysis, TCA cycle, starch, and lipid metabolism were elevated compared with nonstressed cultures. In contrast, the enzymes of the glyoxylate cycle, one carbon metabolism, pentose phosphate pathway, the Calvin cycle, photosynthetic and light harvesting complex, and ribosomes were reduced. A noteworthy observation was that citrate accumulated during nitrogen stress coordinate with alterations in the enzymes that produce or utilize this metabolite, demonstrating the value of comparing protein and metabolite profiles to understand complex patterns of metabolic flow. Thus, the current study provides unique insight into the global metabolic adjustments leading to lipid storage during N starvation for application toward advanced biofuel production technologies. KEYWORDS: iTRAQ, quantitative proteomics, GC-MS, metabolite profiling, Chlamydomonas, nitrogen stress

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INTRODUCTION The microalgae are a very large and diverse group of photosynthetic organisms that have attracted global attention as a renewable energy feedstock. The use of algae as an energy source started in the late 1950s when it was suggested that carbohydrate fractions of algal cells could be used for the production of methane gas via anaerobic digestion.1,2 Various studies during the 1950s and 60s indicated that starving algae or diatoms for key nutrients such as N or Si, respectively, resulted in the accumulation of lipid droplets that could be used for the production of biofuels. This concept gained serious attention during the 1970s oil embargo and led to the development of the U.S. Department of Energy (DOE) Aquatic Species Program (ASP). Under the umbrella of the ASP, researchers collected more than 3000 strains of microalgae from various niche environments.3 After screening for lipid production, the algae collection was narrowed down to the 300 © 2014 American Chemical Society

most promising strains, containing primarily Chlorophyceae (green algae) and Bacillariophyceae (diatoms). Since then, many reports have confirmed that nitrogen starvation induces significant accumulation of neutral lipids in several algal species including the model organism Chlamydomonas reinhardtii.4−7 Although Chlamydomonas is not considered to be a potential biofuel feedstock organism, analysis of its physiology at the molecular level is expected to provide important insights into the mechanisms that promote lipid and starch accumulation and how nutrient stress might be applied to other algal species considered as potential feedstocks but for which molecular genomic and genetic tools are more limited.8,9 The genome of Chlamydomonas has been sequenced and its annotation is currently at version 5.3 (http://www.phytozome. Received: September 18, 2013 Published: February 14, 2014 1373

dx.doi.org/10.1021/pr400952z | J. Proteome Res. 2014, 13, 1373−1396

Journal of Proteome Research

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net/).10 The availability of a sequenced genome with adequate annotation and metabolic pathway information facilitates highthroughput analyses of transcriptional, proteomic, and metabolomic profiling to understand changes in carbon, nitrogen, and lipid metabolism.10 Similar approaches have been successfully used in the past to dissect key steps in the control of nitrogen metabolism in crop plants such as maize11 and cadmium exposure in Arabidopsis, Oryza sativa, and Glycine max.12 In fact, iTRAQ peptide labeling has been used as a quantitative proteomics tool in studies of nitrogen fixing in cyanobacteria,13 salt stress in cyanobacteria,14 nitrogen deprivation in Chlamydomonas,15 salt stress response in Arabidopsis,16 boron deficiency in Citrus sinensis,17 and many other studies including human and mouse models. When Chlamydomonas is stressed for nutrients, especially nitrogen, growth is retarded coincident with significant reprogramming of metabolism,18−21 including the accumulation of starch and neutral lipids, particularly triglycerides (TAG).20−24 Nitrogen stress also leads to developmental changes including gametogenesis and structural changes.25−27 Recently, Boyle et al. showed that during nitrogen starvation three acyltransferase genes (DGAT1, DGTT1, and PDAT1) and a nitrogen response regulator (NRR1) were induced.28 Their results also showed that most of the transcripts encoding de novo fatty acid and membrane lipid synthesis enzymes (e.g., acetyl-CoA biotin carboxyl carrier, acyl carrier protein, malonyl-CoA ACP transacylase, and acetyl carboxyl transferase, SQD1, SQD2, MGD1, and BTA1) were down-regulated. There is some evidence that during nitrogen starvation, starch accumulates first followed by TAG accumulation.21 When nitrogen is added back to the media, nearly 70% of the starch is degraded within 20 h, although TAG breakdown occurs more slowly (20 to 24 h). Cells became green within 24 h of nitrogen addition, which had led to the proposal that there is competition between starch and TAG synthesis for the carbon precursor. These findings led to the hypothesis that preventing starch biosynthesis might increase oil accumulation.29−32 Recent studies have raised question about the competition hypothesis by showing a complemented derivative of a strain of Chlamydomonas starchless mutant had both high oil and starch content.24 In Chlamydomonas during nutrient deprivation, metabolism is tightly controlled by regulatory networks to increase the probability of survival.20,33,34 To date, transcriptomic studies that have been used to interrogate changes in the response to environmental perturbations are relatively comprehensive, whereas proteomic and metabolomic analysis for the microalgae have been much more limited.15,35−38 Additionally, microarray-based transcriptomics and RT-PCR have provided information useful to understand the cellular response to stress conditions at the gene expression level,20,23,39 but the information does not necessarily translate into changes in protein abundance or activity. Further, alterations in metabolite levels under stress conditions may also modulate protein activities and influence the cellular phenotype. In order to give a more comprehensive system level view of the stress response, a combination of protein and metabolite measurements is required. In the present study, we report the results of detailed quantitative proteomics and metabolite profiling of algal cells deprived of nitrogen compared with those cultured with nitrogen. The data were used to correlate alterations in the metabolome and proteome with alterations in growth pattern, pigment levels, starch content, fatty acid profiles, and TAG

accumulation during nitrogen deprivation. For the metabolome analysis, extracted metabolites were analyzed using gas chromatography−mass spectrometry (GC-MS), which allowed a partial least-squares−discriminate analysis (PLS-DA) model to be generated. Important metabolites contributing to the model were identified using a variable importance of projections (VIP) plot. Protein samples were proteolytically cleaved and peptides labeled with iTRAQ tags for analysis using MALDI-MS/MS analysis, which identified important changes in the proteome during nitrogen stress. The results of these experiments advance our understanding of the metabolic adjustments that occur during nitrogen limitation commensurate with TAG accumulation.

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MATERIAL AND METHODS

Strains and Culture Conditions

Chlamydomonas strain CC125 was obtained from the Chlamydomonas Genetics Center (http://www.chlamy.org/). Cells were cultivated at 25 °C under 50 μE m−2 s−1 continuous white light in 250 mL flasks with shaking (120 rpm) using 100 mL of Tris acetate phosphate (TAP) medium.40 Freshly inoculated cultures were grown for 4 to 5 days under light. Cells were harvested and then transferred to new flasks containing nitrogen-replete TAP (N+) media or nitrogendeficient TAP (N−) media from which NH4Cl was omitted. For control conditions, cells were grown in N+ media for 24 h and harvested. For nitrogen stress sampling, cells were cultured in N− media, and aliquots harvested by centrifugation at 24, 48, and 144 h were immediately flash frozen in liquid nitrogen and stored at −80 °C prior to protein or metabolite extraction. Chlorophyll and Total Carotenoid Measurement

Chlorophyll levels were determined using ethanol extraction as detailed elsewhere.24 Briefly, cells from late log phase in N+ and N− media were harvested and resuspended to ∼1.0 × 107 cells/mL in fresh media. A 1 mL aliquot of each was centrifuged at 22 °C and the cell pellet resuspended in 95% ethanol. The samples were vortexed, and the tubes were left in the dark at 4 °C for pigment extraction. Cellular debris was removed by centrifugation at 18 000g for 5 min, and the absorption of the supernatant containing the chlorophyll was read at 665, 649, and 470 nm using a UV−vis spectrophotometer (BioMate 6; Thermo Scientific). Calculations for chlorophyll a, b and total carotenoid content (μg/mL) were performed as described previously.40 Starch Analysis

Quantification of starch levels was performed using the Sigma Starch Assay kit (product code: SA-20) according to the manufacturer’s instructions. Briefly, 2.0 × 107 cells from each triplicate culture were collected by centrifugation and then resuspended in 2 mL of 100% ethanol to extract chlorophyll, and the colorless pellet was further processed as per the manufacturer’s instructions. The absorbance of the final reaction mixture was measured at 340 nm (maximum absorbance for NADH). Assessment of Lipid Accumulation by Fluorescent Microscopy and Spectrometry

The effect of nitrogen deprivation on lipid accumulation was visually assessed using confocal laser scanning microscopy. Cells from a 2 mL culture were collected by centrifugation (2500g for 5 min) and then resuspended in 900 μL of fresh N+ or N− media. Nile Red, dissolved in DMSO, was added to a 1374



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(100%, 300 μL , −50 °C) was added to the samples which were heated in a water bath at 70 °C for 30 min. The samples were cooled to room temperature (RT), 300 μL chloroform was added, then they were incubated with shaking at RT for 30 min followed by addition of 400 μL of Milli-Q water. The samples were vortexed and centrifuged at 3000 rpm for 5 min, and the upper polar phase was collected. A second extraction was performed in similar manner, and the polar phases were combined. The resultant samples were split into equal aliquots, one was dried, flushed with nitrogen and stored at −80 °C, and the other was processed for GC-MS analysis.

final concentration of 30 μM, and the samples were incubated in the dark for 30 min. Images (100×; oil immersion) were acquired using an Olympus IX-81 inverted confocal laser scanning microscope using FloView v5.0 software. To detect Nile Red fluorescence, laser excitation was at 543 nm, and the emission/barrier filter was set at 560−610 nm. For consistency, images for cultures with the highest level of fluorescence (i.e., 144 h N−) were acquired first and all subsequent images were acquired using the same settings. For quantification of Nile Red staining, cells were prepared in the same manner in wells of a microtiter plate and fluorescence measured in arbitrary units using filter sets for 485/20 excitation and 590/35 emission using a BioTek Synergy plate reader.

TMS Derivatization of Metabolites

The trimethylsilyl (TMS) derivatization was performed as previously detailed,41 with 2 μL of ribitol (0.2 mg/mL) added as internal standard.42 One aliquot of the extracted metabolite mixture prepared as detailed above was dried using a vacuum concentrator. Five microliters of 40 mg/mL methoxyamine hydrochloride in pyridine was added to the dried pellet, and samples were shaken for 90 min at 30 °C. The samples were subsequently derivatized using trimethylsilylation of acidic protons by the addition of 50 μL MSTFA (N-methyl-Ntrimethylsilyltrifluoroacetamide) and incubation at 37 °C for 30 min with shaking.

Assessment of Cellular Lipids by Analysis of Fatty Acid Methyl Esters (FAMES)

All glassware was either new or rinsed with hexane prior to use to remove detergents. Following growth as detailed above, cells from a 50 mL culture were collected by centrifugation (2500g, 5 min) and dried overnight in an oven at 60 °C. Cellular lipids were extracted using 2 mL per 10 mg dried biomass using chloroform/methanol (2:1 v/v) containing 0.05% butylated hydroxytoluene (BHT)). One hundred micrograms of C15:0 was added as an internal standard. Following extraction, 1/4 volume of water was added to allow phase separation. The organic phase containing the lipids was collected and then dried under a stream of nitrogen. Fatty acid methyl esters (FAME) were generated in sealed tubes under nitrogen at 50 °C for 12− 16 h in 0.5 mL 1% sulfuric acid in methanol and 0.25 mL of toluene. Following incubation, 1.25 mL of 5% NaCl was added, and the samples were extracted twice with 1.25 mL hexane. The hexane phases were combined and washed with 1 mL of 2% potassium bicarbonate, and the remaining moisture was removed from the hexane extract by passing the sample through anhydrous sodium sulfate column. The FAME samples were dried under a stream of nitrogen, resuspended in 1 mL of methyl acetate, and either analyzed immediately by GC-MS or stored under nitrogen at −20 °C until analysis. For GC-MS analysis, the samples were transferred to glass, crimp-capped GC vials, and 1 μL was injected in splitless mode into an Agilent 7890 A GC-MS. An Agilent Select FAME (200 m × 275 μm × 0.25 μm) column was used to separate the different FAME species. The injection port was set at 250 °C, and the MSD transfer line was maintained at 280 °C. The GC was operated at a constant flow of He at 1 mL/min. The GC run conditions were 10 min at 130 °C, followed by a 10 °C/ min temperature ramp to 160 °C, which was held for 7 min; a second ramp of 10 °C/min to 190 °C, which was held for 7 min; a third 10 °C/min temperature ramp to 220 °C, which was held for 22 min; and a fourth 10 °C/min temperature ramp to 250 °C, which was held for an 17 min. Data acquisition was performed on an Agilent 5973 mass selective detector with a scan range from 50 to 550 atomic mass units (amu).

GC-MS Analysis of Metabolites

One microliter of the TMS-derivatized metabolite sample was injected in splitless mode into an Agilent 6890 GC-MS equipped with an Agilent J and W DB-5 ms UI (30 m × 0.25 mm × 0.25 μm) column. The injection port was set at 230 °C, and the MSD transfer line was maintained at 280 °C. The GC was operated at a constant flow of 1 mL/min He. The temperature program was started at 80 °C for 2 min and ramping at 6 °C/min to 300 °C, which was held for 5 min. Data acquisition was performed on a Agilent 5973 mass selective detector with a scan range from 50 to 800 amu. The acquired spectra were deconvoluted using AMDIS (Automated Mass spectral Deconvolution and Identification System). Resolution, sensitivity, and shape requirements were set at medium, and component width was set at 10 as suggested elsewhere.43 For metabolite identification, the match factor was 70. The deconvoluted spectral data were then compared with the NIST mass spectral search system, the NIST main library (Scientific Instrument Services, Inc., NJ), RI index library, and the Golm metabolome database (http://csbdb.mpimp-golm. mpg.de/csbdb/gmd/home/gmd_sm.html). Compounds identified in at least two biological replicates and 2/3 of the technical replicates were considered true hits. Data for retention time and m/z values used for quantification and peak area were collected manually. Missing values were imputed using the probabilistic principal components analysis (PPCA) method.44 The metabolomics data output is generally complex, and thus preprocessing is necessary to eliminate noise and artifacts. The data for multivariate statistical analysis was set as an X-matrix consisting of 36 rows from both N+ and N− samples, whereas the Y-matrix represented the 113 metabolites identified (Supplemental Table S2). Mean normalization was used where each value was divided by each variable average so that variable average became unity using a Multibase excel plugin (http://www.numericaldynamics.com/). A PLS-DA model was then created to evaluate the distinction of the metabolic level and to identify the relationships between metabolism (matrix X) and changes in the intracellular metabolites (matrix Y).

Extraction of Cellular Metabolites

For extraction of intracellular metabolite, cells were grown as detailed above. Cell density was normalized to 2.0 × 107 cells/ mL and sampled in three biological replicates and three technical triplicates for each treatment condition. Metabolic reactions were quenched by injecting 1 mL of cells into 5 mL of 70% methanol (−50 °C). After 5 min, the cells were collected by centrifugation (3300g, −9 °C for 1 min), flash-frozen in liquid nitrogen, and either processed immediately for metabolite extraction or stored at −80 °C. Pure methanol 1375



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iodoacetamide was added, and tubes were incubated in the dark for 30 min. Sequencing grade trypsin (Promega) was reconstituted in 50 mM acetic acid and added at 1:20 mass ratio to the samples, which were then incubated at 48 °C overnight.46 Following trypsin digestion, iTRAQ labeling of the peptides was performed according to the manufacturer’s instructions (AB Sciex, Foster City, CA) at 22 °C for 2 h. The labeling reaction was quenched with the addition of 100 μL of Milli-Q water, and the samples were incubated further at 22 °C for 30 min. All iTRAQ-labeled samples were pooled and dried. Final samples were reconstituted in 500 μL of SCX buffer containing 10 mM ammonium formate in 20% acetonitrile (ACN) at pH 2.7. SCX separations and second dimension nano RPLC analysis, followed by MALDI-TOF MS/MS analysis, was performed as recommended by the Penn State Hershey Proteomics/Mass Spectrometry Shared Resource facility (http://www. pennstatehershey.org/web/core/proteinsmassspectometry/ protocols/data-analysis). First dimension SCX separation was performed using a 4.6 × 250 mm PolySULFOETHYL Aspartamide column (PolyLC, Columbia, MD) at a flow rate of 1 mL/min installed on a on a Waters 600E HPLC system. A low ionic strength transfer Buffer A containing 10 mM ammonium formate in 20% ACN, pH 2.7 and a high strength elution Buffer B containing 666 mM ammonium formate in 20% ACN at pH 2.7 were used for SCX separation of peptides. The separation gradient was 100% Buffer A for 22 min, then Buffer B was added from 0 to 40% over 33 min; isocratic 100% Buffer B was applied for an additional 7 min, and then at 56 min the solvent was switched back to 100% Buffer A. The first 26 mL of eluent (containing all flow-through fractions) was combined into one fraction, and then 14 additional fractions were collected separately every 2 min. All 15 fractions were dried down using a SpeedVac, resuspended in 9 μL of 2% (v/v) ACN, 0.1% (v/v) TFA and then filtered prior to reverse phase C18 nanoflow-LC separation.

General purpose adjustment was done by creating a pooled average sample from the 24N+ group. Autoscaling (meancentered and divided by the standard deviation of each variable) was performed to make features more comparable. Metabolites were first analyzed by principal component analysis (PCA) to visualize the overall distribution of metabolites, and from these data, a partial least-squares discriminant analysis (PLS-DA) model was set up to evaluate the effect of nitrogen stress on the intracellular metabolite profile. Using the variable importance of projection (VIP) plots, biochemical metabolites were identified that might have a significant role in metabolism during nitrogen stress. Evaluation of Citrate Supplementation

To study the effect of citrate on lipid accumulation, Chlamydomonas cells were pregrown to midlog phase, harvested, washed once with fresh TAP media, and then dispensed into 96-well plates to a cell density of ∼0.1 OD550. The negative control for lipid accumulation was cells without citrate, and the positive control was cultures without nitrogen (TAP N−). Citrate buffered with Tris base to pH 7.0 was added to final concentrations ranging from 1 mM to 10 mM as indicated in the results. Growth was monitored every 12 h by measuring the optical density at 550 nm (OD550). To quantify relative amounts of lipid accumulation, 2 μL of 1 mg/mL Nile Red dissolved in DMSO was added to each well. The plate was incubated at 37 °C for 30 min, and fluorescence was measured at 485/20 (Ex) and 590/35 (Em) using a Biotek Synergy Plate reader. Protein Extraction, Trypsin Digestion, iTRAQ Labeling, and Strong Cation Exchange Chromatography

For analysis of the proteome under N+ and N− conditions, cells were grown as detailed above, harvested by centrifugation (control cells (24h in N+) or nitrogen-stressed cells (24, 48h, or 144 h in N−)), washed once with ice cold water, and resuspended in 500 μL of chilled protein extraction buffer (Tris buffered phenol, 100 mM Tris-HCl pH 8.8, 10 mM EDTA, 0.4% 2-mercaptoethanol, and 0.9 M sucrose). The number of replicates chosen for comparison was determined according to Gan et al. 2007.45 Two biological replicates were viewed as sufficient to estimate error rates between samples and to allow us to compare four growth conditions in duplicate using eight isobaric tags. Cells were broken using a Branson sonicator probe at an intensity of 50 for 30 s on, 60 s off, on ice for 5 min with agitation for 30 min at 4 °C. Proteins were precipitated with 100 mM methanolic ammonium acetate overnight at −20 °C and then collected by centrifugation (12 000 rpm; 30 min; 4 °C). The protein pellet was further washed twice with 600 μL of methanolic ammonium acetate, twice with 80% acetone, and once with cold 70% ethanol. The dried pellets were dissolved in 200−400 μL of resuspension buffer (8 M urea, 2 M thiourea, 2% CHAPS, and 2% Triton X-100) and incubated at 25 °C for 1 h. Insoluble material was removed by centrifugation (12 000 rpm; 30 min at 4 °C). Protein concentrations were determined using Bio-Rad DC protein assay kit using BSA as a standard. For in-solution trypsin digestion, 100 μg of protein was precipitated overnight using chilled acetone (−20 °C), and then the precipitate was dissolved in 20 μL of 0.5 M trimethylammonium bicarbonate (TEAB) pH 8.5 containing 1 μL of 2% SDS. Once the proteins were dissolved completely, tris-(2-carboxyethyl) phosphine (TCEP) was added to a final concentration of 5 mM, and the samples were incubated at 60 °C for 1 h. One microliter of freshly prepared 84 mM

Second Dimension Nano-Reverse-Phase LC Separation and MALDI-TOF/TOF Analysis of iTRAQ-Labeled Peptides

A detailed method for the second dimension RPLC separation is given elsewhere (http://www.pennstatehershey.org/web/ core/proteinsmassspectometry/protocols/data-analysis). Briefly, for second dimension RPLC separation, using a 5 μL loop, each SCX fraction was transferred onto a Chromolith CapRod column (150 × 0.1 mm, Merck) on a Tempo LC MALDI Spotting system (AB Sciex). In this protocol, 2% acetonitrile, 0.1% trifluoroacetic acid was used as Buffer C, and 98% acetonitrile, 0.1% trifluoroacetic acid was used as Buffer D. The elution gradient was 95% Buffer C and 5% Buffer D at 2 μL/min for the first 3 min and then 2.5 μL/min from 3 to 8.1 min. Buffer D was initially ramped to 38% (38−40 min) and allowed to reach 80% (41−44 min). Buffer D was further bought down to 0% from 44 to 49 min (initial conditions). The flow rate was maintained at 2.5 μL/min during the gradient with an equal flow of MALDI matrix solution containing 2 mg/ mL (NH4)3PO4, 0.1% TFA, 80% ACN, and 7 mg/mL recrystallized α-cyano-hydroxycinnamic acid (CHCA). The mixture was automatically spotted onto a stainless steel MALDI target plate every 6 s (0.6 μL per spot), for a total of 370 spots per original SCX fraction. After drying the MALDI plates, 13 calibrant spots (ABI 4700 LC/MS Peptide/Protein Mass Standards Kit) were added to each plate manually, and 15 1376



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Figure 1. Lipid accumulation in Chlamydomonas CC125 under nitrogen stress as assessed by Nile Red staining and FAMES analysis. Cells were cultured in N+ and N− media for the indicated duration as labeled. (A) Confocal laser scanning microscopy of nonpolar lipid accumulation in Chlamydomonas during nitrogen stress (100×). Lipid bodies become pronounced over time after the removal of nitrogen. The upper panel shows fluorescent images, and the lower panel shows the corresponding bright field image merged with the corresponding fluorescent image. (B) Fatty acid levels (mol % ± SD; n = 3) of Chlamydomonas CC125 growing in nitrogen-replete or nitrogen-depleted media. Significant differences were assessed using ANOVA (JMP v9.0). The p-values indicate the level of significance for differences between N+ and N− culture conditions; different letters indicate significant differences between growth conditions at each time point evaluated for each fatty acid species.

rate (FDR) not higher than 5%, as calculated by the PSEP (Proteomics System Performance Evaluation Pipeline) algorithm.47 For statistical analysis of quantitative differences between samples, the ProteinPilot Descriptive Statistics Template (PDST) (v 3.0) was used. All proteins that were identified as differentially expressed with a p-value ≥0.05 in Supplemental Table S3 were extracted and transferred to a new excel sheet, Table S3E. Then the log ratios were converted to the linear scale, replicates were averaged, and a second filter was applied to identify proteins with a difference in abundance ratios that were ≥1.5 or ≤0.6. This gave rise to the final data set of 173 proteins whose abundance was significantly changed over time in the absence of nitrogen.

MALDI target plates were analyzed in a data-dependent manner on an ABI 5800 MALDI-TOF/TOF. To maximize mass accuracy the instrument performed both a plate calibration/MS calibration and then updated the MS/MS default calibration. Using a laser intensity of 3200, 500 laser shots were collected from each spot and averaged to produce an MS spectrum. For each unique m/z value observed across the entire target plate, the spot from which the largest peak of that m/z value was observed was then used to obtain the subsequent MS/MS analysis of that observed m/z value. For each MS/MS acquisition, 2500 laser shots at power 4200 were fired. Protein Identification and Quantification

The Paragon algorithm as implemented in ProteinPilot 4.0 software (AB Sciex) was used to identify and quantify proteins from the MS/MS data. A stringent >95% confidence interval was applied (equivalent to ProteinPilot Unused score >1.3). Spectra were searched against the Chlamydomonas protein database v5.3 (19 529 Protein Sequences downloaded from Phytozome (2012)) plus 389 common lab contaminants, concatenated with a reversed “decoy” version of the “forward” database. After searching, we accepted protein IDs that had a ProteinPilot Unused Score of at least 1.3 (corresponding to a 95% Confidence Interval) and an estimated local false discovery

Integrated Biochemical Map of Global Changes in the Metabolome and Proteome in Chlamydomonas under N stress

Employing the metabolite and protein data sets acquired as detailed above, integrated biochemical maps were constructed to illustrate the global changes in primary metabolism that occur over time during nitrogen deprivation using VANTED (Visualization and Analysis of Network containing Experimental Data).48 The basic framework was constructed based on KEGG pathways. 1377



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Figure 2. Modeling of metabolite shifts during nitrogen depletion. (A) PLS-DA model illustrates differences in metabolite levels between treatments. To prevent the model from overfitting, cross validation was performed and yielded the parameters of Q2 = 0.854 and R2 = 0.98. For these results, a four-component model was best-suited. (B) A screenshot showing a PLS-DA loading plot of the first two components. The solid lines indicate the direction of separation as identified in the corresponding score plot. (C) Variable plot of samples tested. Metabolites were listed from highest to lowest contribution due to nitrogen stress. Variable importance in the projection of Component 1 (VIP) ranks the contribution of each variable to lipid content. Components were selected as significant signals according to variables with VIP >1.

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RESULTS

nitrogen deficient (N−) as compared with nitrogen-replete (N+) growth conditions. The approach employed combines GC-MS-based metabolic profiling and iTRAQ-labeled peptide analysis to provide insight into metabolic pathway fluctuations commensurate with triglyceride accumulation during nitrogen

The objective of the current study was to estimate and compare alterations in metabolite and protein profiles to assess the overall changes in metabolic pathway flow of the green algae Chlamydomonas over an extended period of time under 1378



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Figure 3. Metabolites that have altered concentrations during nitrogen limitation. To determine the statistical variation and significance of difference between the groups based on treatment, Dunnett’s method was applied using the N+ group as the control (alpha = 0.05). Raw data were visualized as box and whisker plots where the x-axis represents sample groups and y-axis represents the raw intensity measured using GC-MS. (A) Indicated metabolites are significantly increased by nitrogen starvation. (B) Indicated metabolites are significantly decreased.

days.20 In that work, transcript and protein abundance for selected ribosomal, photosynthetic, and lipid pathway genes were reported to be stable in N+ conditions but changed

starvation. We chose to model our studies on those of Msanne et al., who compared Chlamydomonas cultured in nitrogenreplete media with similar cultures without nitrogen for up to 6 1379



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is more favorable for biofuel production, because the number of double bonds is decreased, resulting in a more reduced hydrocarbon profile.

progressively over time when nitrogen was removed. This information informed our experimental design to assess metabolite and protein profiles for cells grown without nitrogen for 24, 48, or 144 h by comparison with cells grown for 24 h in nitrogen-replete conditions facilitating analysis of the four experimental sampling conditions in multiple biological replicates.

Effect of Nitrogen Stress on Intracellular Metabolite Content in Chlamydomonas

To acquire data that was reflective of the levels of intracellular metabolites, we used a multistep procedure for GC-MS data acquisition and processing of the analytical signals obtained, as detailed in the Material and Methods section. Data were accumulated for nitrogen-replete cultures and those without nitrogen sampled after 24, 48, or 144 h. Employing these data sets, partial least squares discriminant analysis (PLS-DA) was performed to investigate the correlations between metabolite levels and nitrogen stress, and to identify potential key metabolites involved in lipid accumulation. In the PLS score plot, (Figure 2A,B), the data for the N+ sample was located in the right quadrant due to the relatively higher carbon and nitrogen content, whereas the data for the 24 h N− sample was located in the lower left quadrant of the plot. As the nitrogen stress progressed over time, the concentrations of a significant portion of the metabolites were shifted toward the left upper quadrants, showing clear demarcation of cells experiencing nitrogen stress (Figure 2A). Figure 2B shows the corresponding loading plots for the PLS-DA model to identify metabolites that are responsible for the observed separation. This separation also reveals that metabolites present in the same vicinity of the samples in the PLS-DA plot are present in high concentration in those samples. For example, trehalose and homocysteine are present in high concentration in the 144 h N− samples, whereas glycerol, glucose, galactose, and oxalic acid are present in high concentration in the N-replete cultures. PLS analysis supports a strong connection between limited nitrogen-induced metabolic stress and lipid accumulation over time as reported above. The variable importance plot (VIP) plot (Figure 2C) provides additional information regarding the contribution of each metabolite to the model. A total of 18 identified metabolites correlated with the first component (VIP value >1). The differential expression levels of some of the key metabolites among the nitrogen-stressed and nitrogen-replete cultures are illustrated in Figure 3A,B. Among these were octadecanoic acid, triethanolamine, citric acid, citramalic acid, methionine, nicotianamine, trehalose, and sorbitol, which were increased in abundance during nitrogen stress. Similar trends for ethanolamine and hydroxycitric acid were recently reported in a study where levels of metabolites and lipids were compared in three different cyanobacteria (Synechocystis sp. PCC 6803, Anabaena sp. PCC 7120, and Scenedesmus obliquus).49 Excess citrate may be used as a carrier of acetyl units for fatty acid synthesis, and therefore, the accumulation of citrate may partially explain the increase in lipid storage as discussed below.50 The accumulation of both methionine and nicotianamine may reflect differences in one carbon metabolism involving s-adenosylmethionine in the nitrogen-stressed conditions. We also observed the accumulation of trehalose, a nonreducing disacchride that is generally believed to be strongly induced in stressed cells to stabilize cellular membranes and proteins exposed to damaging conditions in vitro to help retain cellular integrity.51−53 Significant reductions in the levels of Phe, Trp, Asp, putrescine, glycerol, and uracil were also observed in the nitrogen-stressed cultures when compared with the nitrogen

Alterations in Growth, Starch, Pigment, and Lipid Accumulation of Chlamydomonas During Nitrogen Stress

To determine the effect of nitrogen depletion on growth of Chlamydomonas cultured under photoautotrophic conditions, wild-type CC125 cells were pregrown to midlog phase in N+ media. The cells were then resuspended in either N+ or N− media at a cell density of 1.65 × 106 cells/mL and cultured for up to 144 h. In N+ media, the cell numbers increased 369% after 24 h and up to 702, 833, and 1321% after 48, 96, and 144 h, respectively. In contrast, after 24 h in N− media, there was only a 40% increase in cell number, and the density increased slightly over time to 131 and 162% of the starting culture after 96 and 144 h, respectively (Supplemental Figure S1). Starch is a major storage compound in algae and often accumulates during stress to prepare the cells for adverse growth conditions. We found approximately 41% more starch had accumulated after 24 h in stressed cells as compared with the unstressed cells. Starch content then slowly accumulated to approximately double after 144 h of N starvation compared to cells cultured in N-replete media (Supplemental Figure S2A). After 144 h in N− media, cell cultures were yellow in appearance compared to cells growing in N+ media, which were a deep green color. This was attributed to a decrease in chlorophyll a and b content. In cells incubated in N− media, chlorophyll a was reduced 53% after 24 h compared with N+ controls and was reduced further to 72 and 79% of control values after 48 and 144 h, respectively (Supplemental Figure S2B). After 24 h of N stress, the chlorophyll b content was also reduced by 60% and declined further to 80% of control values by 144 h (Supplemental Figure S2C). A similar decrease in total carotenoid levels was observed with a 51−67% reduction after 24 to 144 h without nitrogen (Supplemental Figure S2D). These results indicate there was a progressive loss of photosynthetic pigments in Chlamydomonas during N stress that paralleled decreases in the proteome of the photosynthetic apparatus as presented below and reported previously.20 During N stress, there was an accumulation of lipid bodies that were easily visible when cells were stained with the lipophilic dye Nile Red (Figure 1A). Analysis of cellular fatty acids extracted from cells cultured in the same manner verified this increased lipid accumulation resulting from nitrogen deprivation (Supplemental Table S1). Total fatty acid levels increased by about 50% after 24 h of nitrogen starvation from 106 ± 6 μg/mg dry cell weight (DCW) to 165 ± 12 μg/mg DCW and increased further to 2-fold (230 ± 16 μg/mg DCW) and 3.8-fold (404 ± 16 μg/mg DCW), respectively, of control values after 48 and 144 h. Most notably, there was an increase in the relative percentage of the saturated fatty acid palmitate (C16:0) and the monounsaturated fatty acid oleate (C18:1Δ9), as compared with the polyunsaturated fatty acids C16:4, C18:3 (Δ5,9,12) and C18:3 (Δ9,12,15) in nitrogen-starved cells compared to nitrogen replete (Figure 1B). The reduction in polyunsaturated fatty acids and increase in saturated and monounsaturated fatty acids agree with previous studies reported for Chlamydomonas.20,21 This fatty acid composition 1380



Article

the metabolomics finding and the PLS-DA model proposed in this study.

replete samples (Figure 3B). Each compound may be catabolized to provide energy by feeding substrates into the TCA cycle for energy. The amino acid, putrescine, and uracil catabolic pathways would also make nitrogen available through formation of urea and ammonia, which may help to adjust metabolic homeostasis of cells cultured in the nitrogen limiting culture conditions.54

Alterations in the Proteome During Nitrogen Deprivation

In the current investigation, an iTRAQ peptide labeling experiment was conducted using Chlamydomonas CC125 grown in N+ and N− media and sampled at specific time points, as detailed in the experimental workflow (Figure 5). Two biological replicates were used to verify observed changes.45 To ensure that erroneous quantification reports on complex proteome responses were statistically minimized, a stringent 5% local false discovery rate (FDR) estimation was applied.55 By these criteria, 794 distinct proteins were identified, and of these, 67% were identified by ≥2 distinct peptides. In this data set, one decoy hit was identified and removed to generate a final list of 793 proteins (Supplemental Table S3A). This set of proteins was classified using different KEGG metabolic pathway modules (Supplemental Table S3D) and then compared using results for cells grown in N-replete versus N-depleted conditions across the four experimental time points. Protein Pilot Descriptive Statistics Template V 3.0 was used for statistical analysis of the protein abundance data comparing N+ and N− conditions (Supplemental Table S3). From these data, a volcano plot was created, and 260 distinct proteins were identified as significantly altered in abundance with p < 0.05. For better visualization of these data sets, a heat map was generated with hierarchical clustering using Pearson correlation and average linkage method on the list obtained (Supplemental Figure S3). A second filter, requiring a change in abundance equivalent to expression ratio ≥1.5 or ≤0.6 for increased or decreased levels of proteins, respectively, was applied to the filtered list of 260 proteins (having p ≤ 0.05), which resulted in final lists of 96 proteins with increased abundance and 77 proteins with decreased abundance when N stressed cultures were compared to nonstressed, N-replete cultures (Tables 1 and 2, respectively). In the absence of a nitrogen source, 55 proteins were significantly increased in abundance at 24 h, 64 proteins at 48 h, and 96 proteins at 144 h (Table 1). We also observed that 38 proteins were decreased in abundance after 24 h of N stress, 50 proteins at 48 h, and 58 proteins at 144 h (Table 2). The Venn diagrams in Figure 6A,B illustrate the concordance and differences for proteins that were significantly increased or decreased (p < 0.05 and expression ratio ≥1.5 or ≤0.6) over the 3 time periods assessed compared with controls. As illustrated in Figure 6, 37 proteins were increased in abundance over 144 h at all time points evaluated, and 19 were decreased.

Effect of Citrate Supplementation on Lipid Accumulation

To investigate whether citrate might function to increase the total accumulation of lipid in vivo as suggested by our metabolite analysis, tris-buffered citrate was added at different concentrations to TAP N+ media. As indicated in Figure 4A,

Figure 4. Effect of exogenous citrate on growth and lipid accumulation. (A) Cells were grown in N+ media containing the indicated concentration of tris-buffered citrate for up to 84 h as indicated. Each data point is an average of three experiments, and error bars represent ± SD. Apparent differences in growth at various times and concentrations of citrate were not statistically significantly. (B) Effect of exogenous citrate on the lipid accumulation assessed using the nile red fluorescence plate reader (mean ± SD; n = 2). Cells grown in TAP media without nitrogen (N−) were used as a positive control for lipid accumulation. To determine the statistical significance, ANOVA was performed with Dunnet’s multiple comparison test (alpha = 0.05) using GraphPad Prism V6.0. Statistically significant lipid accumulation for treatment with citrate as compared to cells growing in TAP N+ media are indicated by asterisks.

Pathway Analysis of Proteins with Altered Abundance During Nitrogen Stress

KEGG classification of all proteins identified in this study is given in Supplemental Table S3. Employing iTRAQ-facilitated measurements of relative protein abundance, we identified 16 proteins that mapped to the GO categories glycolytic and gluconeogenesis pathway, and 10 of these were differentially abundant. Several key enzymes of the glycolytic pathway were increased including mitochondrial glyceraldehyde 3-phosphate dehydrogenase, phosphoglycerate mutase, and enolase (Supplemental Figure S4A and Figure 8). In contrast, other key enzymes of gluconeogenesis including phosphoenolpyruvate carboxykinase (pckA) and fructose-1,6-bisphosphatase I (FBP) were significantly decreased, as was a second chloroplast localized glyceraldehyde 3-phosphate dehydrogenase. This

cultures supplied with exogenous citrate achieved the same or slightly higher final cell density than those without added citrate, although differences were not statistically significant. However, above 6 mM, cells appeared stressed, as indicated by the loss of green coloration. Lipid accumulation, as assessed using Nile Red staining, increased with citrate up to a 6 mM final concentration (Figure 4B). These results suggest that addition of citrate stimulates fatty acid synthesis, corroborating 1381



Article

Figure 5. Workflow for quantitative analysis of proteins during nitrogen stress. Samples were obtained from two independent cultures. Proteins were extracted, and proteolytic digestion was performed using trypsin as shown. Tryptic peptide fragments were labeled using iTRAQ reagents and then subjected to LC-SCX fractionation prior to MALDI-TOF/TOF-MS. Numbers indicate the molecular mass of the iTRAQ labels. All procedures as well as bioinformatics and statistical analysis are detailed in the Material and Methods section.

fructose-1,6-bisphosphatase I (FBP) were all significantly decreased. This suggests that the pentose phosphate pathway is down-regulated upon nitrogen depletion, and intermediate metabolites were instead directed toward the synthesis of 6phosphogluconate, which was significantly elevated in nitrogen starvation as detailed above. This metabolite is most likely directed toward the glycolytic pathway. Our observations correspond with a previous transcriptomics study reported elsewhere, in which [U−13C] acetate was also used to confirm that under nitrogen starvation the glyoxylate and gluconeogenic pathways are down-regulated.23 At least six proteins classified as participating in the Calvin cycle and pentose phosphate pathway were decreased in amount (Table 2) with nitrogen deprivation. These differences were coordinate with decreases in abundance of seven proteins of photosystem I and six proteins of photosystem II. This confirms previous reports indicating nitrogen starvation induces a disruption in the photosynthetic apparatus.15,23,38 At least four enzymes involved in porphyrin and chlorophyll metabolism were also reduced compared with N+ controls. Thus, there was a global alteration in plastid components that was coincident with the reduction of chlorophyll a and b levels as noted above. Many ribosomal proteins were reduced 40% or more during nitrogen stress indicative of an overall reduction in protein synthesis, as is expected when nitrogen becomes limiting. The largest reduction occurred by 48 h (Table 2). This global decrease in the protein synthetic machinery is a major contributing factor to the cessation of cell growth and replication during nitrogen deprivation. Our data is in line with most studies of ribosomal proteins and RNA expression during nitrogen deprivation.15,23,57−60 It has also been reported that as nitrogen starvation proceeds, there is degradation of

occurred coordinately with other plastid proteins. These data reflect the fact that in Chlamydomonas enzymes of the initial glycolytic pathway leading to glyceraldehyde-3-phosphate are primarily in the chloroplast and those leading from glyceraldehyde-3-phosphate to pyruvate (i.e., phosphoglycerate mutase, and enolase) are primarily cytosolic.56 Thus, we observed that the chloroplast isozymes were decreased in abundance, whereas the cytosolic enzymes were increased (Supplemental Figure S4A). Our analysis of the proteome also identified 44 proteins involved in oxidative phosphorylation, of which 23 proteins were significantly increased and two proteins were decreased in abundance compared with N+ controls (Tables 1 and 2; Figure 7A,B). All five respiratory complexes were identified, including all nine subunits of complex I (NADH dehydrogenase), and each was strongly up-regulated. Within complex II, all three succinate dehydrogenase subunits (SDH1, SDH2, and SDH3) were identified and quantified. Of these, SDH2 (Cre06.g264200.t1.2) was significantly increased following 24 and 144 h of nitrogen stress. In complexes III and IV, several cytochrome c oxidase subunits and components of the ubiquinol−cytochrome c complex were increased, as was complex V, including the alpha, beta, and epsilon subunits of the mitochondrial ATP synthase (Supplemental Table S3). The enzymatic components of the pentose phosphate cycle (PPP) are localized to the plastid and provide NADPH for anabolic pathways.23 Among these, glucose-6-phosphate dehydrogenase was found to increase in abundance during N stress, but the difference compared with nitrogen replete conditions was not statistically significant (2.62 mean fold change at 144 h; p = 0.072) (Supplemental Table S3 and Figure S4D). The levels of the enzymes 6-phosphogluconate dehydrogenase, transketolase (tktA), transaldolase (talA), and 1382


Cre10.g461050.t1.2 Cre02.g076350.t1.2 g12869.t1 g1098.t1 Cre06.g304350.t1.2 Cre09.g405850.t1.1 Cre10.g422600.t1.1 Cre10.g434450.t1.2 Cre10.g459200.t1.2 Cre02.g100200.t1.2 g11598.t1 Cre12.g555250.t1.2 Cre10.g461050.t1.2 Cre15.g638500.t1.2 Cre16.g664600.t1.2 Cre17.g698000.t1.2 g11697.t1 g18128.t4 Cre12.g523850.t1.2 g2416.t1 Cre06.g310950.t1.2 g11946.t2 Cre07.g338050.t1.2 Cre02.g113200.t1.3 Cre06.g293850.t1.2 Cre04.g223100.t1.2 Cre13.g569850.t1.2 Cre12.g514050.t1.2 Cre13.g592200.t1.2

Cre12.g485150.t1.2 g16134.t1 g18056.t1 Cre01.g044800.t1.2 Cre06.g264200.t1.2 Cre12.g514750.t1.2 Cre12.g526800.t1.2 Cre01.g051900.t1.2

Cre06.g272050.t1.2 Cre07.g337650.t1.2

accession no.b

nitrogen and amino acid metabolism

pentose phosphate pathway oxidative phosphorylation

pyruvate metabolism citrate cycle (TCA cycle)

glycolysis/gluconeogenesis

pathwaysc phosphoglycerate mutase, 2,3-bisphosphoglycerate-independent mitochondrial pyruvate dehydrogenase complex, E1 component, alpha subunit glyceraldehyde 3-phosphate dehydrogenase pyruvate dehydrogenase E1 beta subunit acetaldehyde dehydrogenase/alcohol dehydrogenase pyruvate-formate lyase iron−sulfur subunit of mitochondrial succinate dehydrogenase citrate synthase 6-phosphogluconate dehydrogenase Rieske iron−sulfur protein of mitochondrial ubiquinolcytochrome c reductase vacuolar ATP synthase subunit A vacuolar ATP synthase subunit B cytochrome c cytochrome c oxidase subunit II cytochrome c oxidase subunit VIb NADH:ubiquinone oxidoreductase 49 kDa ND7 subunit NADH:ubiquinone oxidoreductase 51 kDa subunit NADH:ubiquinone oxidoreductase 39 kDa subunit NADH:ubiquinone oxidoreductase B16.6 subunit NADH:ubiquinone oxidoreductase 22 kDa subunit NADH:ubiquinone oxidoreductase 14 kDa subunit NADH:ubiquinone oxidoreductase B14 subunit NADH:ubiquinone oxidoreductase 18 kDa subunit NADH dehydrogenase NADH dehydrogenase (ubiquinone) Fe−S protein 4 P-type ATPase/cation transporter, plasma membrane F-type H+-transporting ATPase subunit beta F-type H+-transporting ATPase subunit alpha ubiquinol:cytochrome c oxidoreductase 50 kDa core 1 subunit ubiquinol:cytochrome c oxidoreductase cytochrome c1 FAD-dependent oxidoreductase FAD/NAD(P)-binding oxidoreductase mitochondrial F1F0 ATP synthase associated 36.3 kDa protein carbonic anhydrase gamma carbonic anhydrase glutamine synthetase glutamine synthetase glutamate synthase (ferredoxin) glutamate synthase (NADPH/NADH)

enzyme/protein functiond

3 12 8 8 6 5 4 5 3 2 3 2 3 3 4 6 32 32 11 3 4 6 14 8 4 12 18 14 15

27 4 25 17 5 6 14 6

12 4

peptides (>95% confidence)

1.33 1.39 1.47 1.47 1.53 1.51 1.52 1.72 1.26 1.39 1.5 2.92 1.33 1.84 1.26 1.73 1.4 1.35 1.37 1.65 2.09 1.19 1.24 1.47 1.26 2.33 1.43 1.43 1.35

2.63 1.53 1.89 1.59 1.66 1.44 1.46 1.15

1.44 1.39

fold change 1.62 1.63 4.03 1.76 1.82 1.67 1.36 1.56 1.63 1.5 1.42 1.32 1.66 1.57 1.88 1.39 1.5 1.69 1.65 1.56 1.56 2.37 1.42 1.76 1.61 1.49 1.67 1.32 1.6 1.39 2.58 1.33 1.62 1.43 1.36 2.24 1.51 1.46 1.23

1.38 × 10−02 2.34 × 10−02 10−05 10−01 10−04 10−02 10−03 10−03 10−02 10−01 10−01 10−03 10−01 10−02 10−02 10−02 10−02 10−02 10−01 10−01 10−02 10−02 10−01 10−02 10−01 10−03 10−02 10−03 10−02 10−01 10−01 10−01 10−02 10−01 10−01 10−04 10−02 10−03 10−02

× × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × ×

2.16 7.41 2.90 1.48 1.55 4.27 1.95 4.68 1.35 3.27 6.17 7.41 2.16 1.02 3.10 8.13 3.47 3.16 1.86 2.09 2.11 7.06 9.12 1.01 1.82 1.00 3.02 7.08 4.57

6.92 2.15 1.23 2.14 4.57 3.39 8.71 1.85

fold change

5.01 2.00 3.47 1.02 1.08 1.59 2.51 6.46 1.00 7.41 1.41 2.95 5.01 1.95 7.59 1.38 3.89 2.00 1.78 4.23 1.12 7.85 2.29 2.01 1.61 2.51 1.91 5.37 4.27

6.31 2.88 3.02 1.74 3.72 4.17 1.38 6.17 × × × × × × × × × × × × × × × × × × × × × × × × × × × × ×

× × × × × × × × 10−02 10−02 10−02 10−02 10−01 10−01 10−02 10−02 10−01 10−02 10−01 10−01 10−02 10−02 10−02 10−02 10−03 10−03 10−03 10−01 10−01 10−01 10−03 10−01 10−01 10−04 10−02 10−04 10−02

10−06 10−02 10−04 10−02 10−02 10−03 10−02 10−02

1.38 × 10−03 1.06 × 10−01

p-value

comparison of 48 h N− vs 24 h N+

p-value


Table 1. Proteins That Are Significantly Increased in Abundance after Removal of Nitrogen from the Growth Mediaa

1.77 1.74 1.67 2.03 1.95 2.06 2.19 2.67 1.75 1.73 1.87 6.1 1.77 2.49 1.75 1.7 1.74 1.54 1.54 2.38 4.44 1.73 1.57 3.19 1.63 4.09 1.57 1.89 1.74

3.93 2.21 1.94 1.59 1.74 1.82 1.83 2.21

1.88 1.98

fold change

5.01 5.75 4.25 1.45 2.14 1.20 7.59 6.92 1.15 1.86 6.46 8.32 5.01 1.26 5.89 3.39 2.82 4.27 1.04 1.73 3.02 9.55 5.25 1.35 2.51 4.27 1.25 3.09 3.47

2.51 1.00 3.09 1.35 4.37 5.50 4.47 2.19

× × × × × × × × × × × × × × × × × × × × × × × × × × × × ×

× × × × × × × ×

10−02 10−04 10−01 10−02 10−02 10−02 10−03 10−03 10−02 10−01 10−02 10−02 10−02 10−03 10−02 10−02 10−02 10−02 10−01 10−01 10−02 10−04 10−02 10−02 10−02 10−05 10−01 10−04 10−03

10−05 10−01 10−04 10−02 10−02 10−05 10−02 10−02

1.05 × 10−03 1.48 × 10−02

p-value


Journal of Proteome Research Article

1383


1384

Cre06.g272900.t1.2 Cre06.g285400.t1.3 Cre10.g456750.t1.2 Cre12.g509750.t1.2

Cre07.g318750.t1.2 g18106.t1 g5357.t1 Cre06.g308850.t1.2 Cre17.g705000.t1.2 Cre02.g108900.t1.2 Cre02.g119900.t1.2 Cre03.g172300.t1.2 Cre05.g241950.t1.2 Cre06.g258800.t1.3

Cre06.g257500.t1.2 Cre12.g559250.t1.2 Cre13.g605150.t1.2 Cre03.g181150.t1.1 g4512.t1 Cre02.g088200.t1.2

g14815.t2 Cre02.g076900.t1.1 g9110.t2

Cre14.g626900.t1.2

g13061.t2 Cre12.g551300.t1.2 g13518.t1 Cre01.g021250.t1.3 Cre06.g308500.t1.2 Cre09.g416050.t1.2 Cre09.g411900.t1.2 Cre07.g336950.t1.1 Cre08.g362450.t1.2 Cre17.g721500.t1.2 Cre13.g566650.t1.1 Cre01.g053000.t1.2

accession no.b

pathwaysc

unclassified to pathway

rna transport

protein processing in endoplasmic reticulum purine metabolism

peroxisome phagosome

cell cycle

amino sugar and nucleotide sugar metabolism aminoacyl-tRNA biosynthesis calcium signaling pathway

fatty acid biosynthesis

starch and sucrose metabolism

Table 1. continued

phosphoribosylformylglycinamidine cyclo-ligase phosphoribosylamine–glycine ligase amidophosphoribosyltransferase translation initiation factor eIF-3 subunit 1 calcium-dependent protein kinase calcium-binding EF hand family protein cysteine proteinases superfamily protein mitochondrial phosphate carrier protein porin/voltage-dependent anion-selective channel protein hydroxyproline-rich glycoprotein component of the outer cell wall flavin-containing amine oxidoreductase bacterial DNA-binding protein dehydroascorbate reductase 2 mitochondrial processing peptidase alpha subunit

glutaminyl-tRNA synthetase cGMP-dependent protein kinase solute carrier family 25 (mitochondrial carrier; adenine nucleotide translocator) 14-3-3 protein 14-3-3 protein Mn superoxide dismutase dynein light chain LC8-type tubulin alpha protein disulfide isomerase 1

periplasmic L-amino acid oxidase, catalytic subunit periplasmic L-amino acid oxidase ammonium transporter argininosuccinate lyase argininosuccinate synthase carbamoyl phosphate synthase, small subunit glycine hydroxymethyltransferase starch phosphorylase alpha-amylase granule-bound starch synthase long-chain acyl-CoA synthetases (AMP-forming) NAD-dependent glycerol-3-phosphate dehydrogenase family protein phosphomannomutase


2 4 4 12

5 4 4 3 2 2 4 6 10 16

6 15 5 3 27 18

4 2 18

3

3 19 2 5 16 7 7 31 8 10 10 3


1.41 1.09 2.01 1.36

2.04 1.54 1.87 1.74 1.63 2.58 1.33 1.96 1.36 1.25

1.34 1.56 1.36 1.45 1.55 1.06

1.43 1.4 1.39

1.44

6.38 3.85 1.78 1.88 2.44 1.35 1.24 1.99 1.65 0.98 1.79 1.91

fold change

1.34 1.25 1.52 1.27 1.53 1.78 2.28 1.61 1.42 1.06

7.59 × 10−02 8.51 × 10−02 4.07 × 10−02 10−02 10−03 10−01 10−01 10−04 10−01

1.45 4.92 4.37 7.59

3.16 1.02 3.47 1.60 1.38 2.19 1.80 3.47 2.75 6.61

3.63 1.70 3.52 2.77 7.08 4.92

1.92 1.48 1.57 1.45 1.32 1.39 1.46 1.5 1.46 1.33 0.9 1.52 2.41 1.47

10−03 10−01 10−02 10−01 10−02 10−02 10−01 10−03 10−02 10−02 10−01 10−01 10−02 10−03

× × × × × × × × × × × × × ×

× × × × × ×

× × × × × × × × × × × ×

4.60 × 10−01

6.31 8.13 1.01 4.79 6.76 1.62 3.10 8.51 5.37 6.10 3.31 6.03

8.2 4.59 1.83 1.77 2.79 1.3 1.23 1.93 1.64 1.31 2.13 2.92

fold change × × × × × × × × × × × ×

10−01 10−05 10−02 10−02 10−06 10−01 10−01 10−09 10−03 10−02 10−03 10−02

8.74 1.00 2.88 1.83

4.79 1.15 5.37 1.14 1.60 1.36 1.88 7.76 3.63 2.57

1.70 9.77 1.12 7.41 3.47 4.08

× × × ×

× × × × × × × × × ×

× × × × × ×

10−01 10−01 10−02 10−01

10−03 10−01 10−02 10−01 10−01 10−01 10−01 10−03 10−02 10−02

10−01 10−05 10−01 10−02 10−03 10−01

4.58 × 10−01 7.76 × 10−02 9.55 × 10−02

4.88 × 10−01

1.19 7.41 5.01 2.34 5.13 2.52 2.25 1.26 1.32 9.77 7.08 2.57

p-value


10−02 10−05 10−01 10−03 10−05 10−01 10−01 10−10 10−03 10−01 10−02 10−02

p-value


1.59 2.24 2.88 2.23

2.49 2.01 1.83 1.71 1.74 3.77 2.55 2.21 1.8 1.5

1.83 2.8 2.6 2.06 1.55 1.67

1.72 1.72 1.63

2

8.12 6.02 3.63 2 3.11 1.97 1.53 2.74 1.65 1.89 3.67 3.22

fold change

× × × × × × × × × × × ×

10−02 10−04 10−02 10−03 10−04 10−01 10−02 10−08 10−01 10−03 10−03 10−03

2.40 7.46 3.72 2.63

1.48 2.82 1.78 4.07 3.39 4.57 2.82 7.76 2.88 2.57

3.80 1.07 2.00 1.26 4.27 4.27

× × × ×

× × × × × × × × × ×

× × × × × ×

10−01 10−01 10−02 10−05

10−03 10−02 10−02 10−02 10−02 10−03 10−03 10−03 10−03 10−02

10−03 10−05 10−02 10−01 10−03 10−04

8.32 × 10−02 3.24 × 10−02 1.62 × 10−01

1.04 × 10−01

3.98 1.45 4.57 8.32 3.39 1.48 5.01 8.91 1.17 1.82 4.37 7.41

p-value




1385

pathwaysc Fe-assimilating protein endoribonuclease L-PSP septin-like protein histone H3 histone H1-3 NADH:flavin oxidoreductase/12-oxophytodienoate reductase cytochrome c peroxidase protein involved in vacuolar polyphosphate accumulation, contains SPX domain 1,4-benzoquinone reductase-like; Trp repressor binding proteinlike/protoplast-secreted protein reticulon peptidylprolyl isomerase plastidic ADP/ATP translocase bifunctional polynucleotide phosphatase/kinase cathepsin V flagellar associated protein predicted phosphoglycerate mutase hypothetical protein hypothetical protein hypothetical protein hypothetical protein hypothetical protein


2 5 7 9 7 9 6 6 3 2 8 8

6

7 6 6 5 4 4 10 8


1.91 1.17 1.54 1.57 1.73 1.15 1.18 1.81 1.46 1.68 0.99 1.59

1.37

1.36 2.21 1.84 1.53 1.67 1.67 1.77 1.43

fold change

1.73 2.03 1.3 1.35 1.58 1.55 1.37 1.25 2 3.81 1.59 1.83 2.08

10−02 10−01 10−02 10−02 10−02 10−02 10−01 10−01 10−01 10−01 10−01 10−02 5.75 1.68 3.98 1.17 8.51 7.94 4.01 2.44 1.95 1.41 2.07 2.57

× × × × × × × × × × × ×

× × × × × × × ×

1.68 × 10−01

6.46 5.01 2.29 9.12 1.04 1.45 1.18 1.20

1.02 2.06 2.14 1.43 1.55 2.33 2.4 1.28

fold change × × × × × × × ×

10−01 10−03 10−03 10−03 10−01 10−02 10−02 10−02

1.16 8.71 6.03 1.48 2.75 7.76 3.02 1.08 4.90 1.62 1.78 3.63

× × × × × × × × × × × ×

10−01 10−02 10−02 10−03 10−02 10−03 10−01 10−01 10−02 10−01 10−02 10−03

5.75 × 10−02

1.62 3.55 2.63 1.41 2.14 8.91 6.31 6.76

p-value


10−02 10−03 10−02 10−03 10−01 10−01 10−01 10−02

p-value


1.7 1.53 3.24 2.04 2.23 1.8 1.94 3.13 4.1 4.62 4.27 2.31

2.76

1.65 2.84 2.86 2.34 2.14 1.87 1.84 1.67

fold change

× × × × × × × ×

10−02 10−03 10−03 10−02 10−02 10−01 10−01 10−04

1.86 6.78 1.45 1.62 1.41 2.19 9.12 6.17 5.25 4.47 2.75 4.37

× × × × × × × × × × × ×

10−01 10−01 10−03 10−03 10−01 10−04 10−03 10−03 10−02 10−02 10−03 10−03

2.63 × 10−02

2.45 2.88 3.89 1.20 6.17 2.62 3.63 5.89

p-value


This list was generated after applying a p-value filter (p ≤ 0.05) and mean protein expression ratio ≥1.5. bTranscript ID of Chlamydomonas genome v5.3 based on the Augustus update u11.6 from JGI assembly v5. cClassification of proteins according to KEGG pathway tools. dComplete lists of all proteins identified including predicted subcellular locations can be found in Supporting Information Table S3.

a

g16854.t1 g6624.t2 g8343.t1 g9621.t3 Cre09.g407700.t1.2 Cre09.g394200.t1.3 Cre06.g293150.t1.2 Cre07.g349350.t1.2 Cre09.g405500.t1.3 Cre09.g406600.t1.1 Cre12.g544450.t1.2 g2758.t1

g11190.t2

Cre12.g546550.t1.1 Cre12.g551350.t1.2 Cre12.g556250.t1.2 g6200.t1 Cre13.g567450.t1.2 Cre17.g727300.t1.2 g10003.t1 g10027.t1

accession no.b

Table 1. continued



Cre06.g294950.t1.3 Cre17.g723650.t1.2 Cre01.g050950.t1.2 Cre01.g015350.t1.1 Cre03.g158000.t1.2 Cre02.g085450.t1.2 g5492.t1 Cre09.g412100.t1.2 Cre07.g330250.t1.2 Cre12.g560950.t1.2 Cre10.g420350.t1.2 Cre07.g344950.t1.2 Cre10.g425900.t1.2 Cre17.g720250.t1.2 Cre16.g673650.t1.1 Cre05.g243800.t1.2 Cre13.g562850.t1.2 g9830.t1 Cre12.g550850.t1.2 g8723.t1 Cre01.g051500.t1.2 Cre12.g558900.t1.2 Cre01.g027000.t1.2 g8426.t2

Cre07.g353450.t1.2 Cre02.g120150.t1.2 g274.t1 Cre12.g554800.t1.2 g738.t2 Cre02.g080200.t1.2 Cre12.g510650.t1.2 g9598.t1 Cre11.g481450.t1.2 Cre12.g512300.t1.2

Cre03.g149100.t1.2 g2904.t1 Cre06.g282800.t1.2 Cre02.g141400.t1.2 Cre13.g567950.t1.2

accession no.

b

1386

ribosomes

photosynthesis

porphyrin and chlorophyll metabolism

alpha-linolenic acid metabolism lipid metabolism

oxidative phosphorylation

pentose phosphate pathway

gluconeogenesis starch and sucrose metabolism acetate metabolism carbon fixation in photosynthetic organisms

citrate cycle (TCA cycle) and glyoxylate cycle

pathways

c

enoyl- acyl-carrier protein reductase I acetyl-CoA acyltransferase 1 geranylgeranyl reductase light-dependent protochlorophyllide reductase glutamate-1-semialdehyde 2,1-aminomutase coproporphyrinogen III oxidase photosystem I subunit II photosystem I subunit III photosystem I subunit VI photosystem I subunit V photosystem I subunit IV light-harvesting protein of photosystem I light-harvesting protein of photosystem I light-harvesting complex II chlorophyll a/b binding protein 4 light-harvesting complex II chlorophyll a/b binding protein 5 photosystem II Psb27 protein thylakoid formation protein photosystem II oxygen-evolving enhancer protein 1 photosystem II oxygen-evolving enhancer protein 2 photosystem II subunit Q-2 oxygen evolving enhancer protein 3 (PsbQ) cytochrome b6f complex subunit V ribosomal protein L11, component of cytosolic 80S ribosome and 60S Large subunit small subunit ribosomal protein S9e

acetyl-CoA synthetase/ligase ribulose-1,5-bisphosphate carboxylase/oxygenase small subunit 2 glyceraldehyde-3-phosphate dehydrogenase (NADP+) (phosphorylating) phosphoribulokinase transaldolase transketolase fructose-1,6-bisphosphatase I inorganic pyrophosphatase F-type H+-transporting ATPase subunit b lipoxygenase

citrate synthase malate synthase isocitrate lyase phosphoenolpyruvate carboxykinase glucose-1-phosphate adenylyltransferase

enzyme/protein function

d

3 2 6 3 2 2 20 14 6 2 2 6 2 18 18 4 2 34 28 20 4 6 3 8

15 15 22 12 2 19 4 4 3 14

10 18 17 13 7


0.61 0.67 0.33 0.37 0.45 0.42 0.99 1.03 0.65 0.65 1.00 0.52 0.30 0.72 0.75 0.60 0.53 0.75 0.79 0.70 0.67 0.55 0.74 0.52

0.80 0.27 0.88 0.84 0.50 0.79 0.85 0.63 0.52 0.78

0.76 0.60 0.40 0.89 0.95

fold change

5.01 5.62 4.47 4.42 1.98 7.18 7.17 8.33 6.74 4.22 8.11 3.37 1.03 4.77 1.19 1.84 4.85 1.02 2.09 7.93 5.56 1.31 3.30 2.19

7.02 1.41 3.08 1.65 8.69 1.25 9.00 5.12 1.40 8.70

6.11 2.45 3.43 5.22 7.07

10−01 10−02 10−03 10−02 10−02 10−02 10−01 10−01 10−02 10−02 10−01 10−02 10−01 10−02 10−01 10−01 10−02 10−01 10−01 10−03 10−02 10−01 10−01 10−02

0.67 0.44 0.28 0.38 0.32 0.53 1.08 0.98 1.22 0.71 0.74 0.71 0.50 0.50 0.47 0.56 0.83 0.49 0.63 0.48 0.58 0.59 0.60 0.65

0.41 0.38 0.51 0.63 0.30 0.61 0.59 0.49 0.85 0.72

10−02 10−01 10−01 10−01 10−02 10−01 10−01 10−02 10−01 10−02 × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × ×

0.46 0.36 0.26 0.55 0.67

10−03 10−05 10−06 10−01 10−01

fold change

3.56 3.95 4.21 7.24 5.43 3.32 6.54 9.14 4.25 1.74 4.92 4.45 1.33 5.22 4.81 1.13 3.67 8.22 1.16 2.65 4.25 5.78 3.51 2.41

1.65 1.74 3.78 3.12 5.30 1.58 6.67 1.53 4.16 1.35

9.49 8.78 2.77 1.83 2.94

× × × × × × × × × × × × × × × × × × × × × × × ×

× × × × × × × × × ×

× × × × ×

10−01 10−02 10−02 10−02 10−03 10−02 10−01 10−01 10−01 10−01 10−01 10−01 10−01 10−06 10−06 10−02 10−01 10−08 10−03 10−04 10−02 10−01 10−02 10−03

10−07 10−01 10−02 10−02 10−01 10−04 10−02 10−02 10−01 10−01

10−06 10−06 10−03 10−01 10−01

p-value


× × × × ×

p-value


Table 2. Proteins That Are Significantly Decreased in Abundance after Removal of Nitrogen from the Growth Mediaa

0.30 0.63 0.33 0.44 0.42 0.75 0.34 0.34 0.33 0.35 0.29 0.37 0.12 0.21 0.19 0.26 0.36 0.23 0.21 0.15 0.16 0.55 0.87 0.85

0.34 0.17 0.73 0.61 0.27 0.76 0.47 0.41 0.40 0.57

0.60 0.43 0.34 0.60 0.58

fold change

2.62 1.42 1.24 8.42 1.65 5.82 1.24 1.40 3.27 2.87 5.20 3.67 5.40 2.05 1.13 2.48 3.15 1.20 9.03 7.27 1.68 5.74 6.31 4.81

1.74 4.64 1.44 4.07 2.65 2.44 7.41 4.18 2.37 6.48

2.65 1.17 1.66 1.05 2.77

× × × × × × × × × × × × × × × × × × × × × × × ×

× × × × × × × × × ×

× × × × ×

10−01 10−01 10−03 10−02 10−02 10−01 10−04 10−01 10−01 10−01 10−01 10−02 10−02 10−03 10−03 10−01 10−02 10−06 10−07 10−08 10−01 10−01 10−01 10−01

10−06 10−02 10−02 10−02 10−01 10−01 10−02 10−02 10−01 10−02

10−03 10−04 10−02 10−01 10−02

p-value




1387

gametogenesis unclassified to pathway

one carbon metabolism

pathways

c

large subunit ribosomal protein L36e large subunit ribosomal protein L23e small subunit ribosomal protein S14e large subunit ribosomal protein L7Ae large subunit ribosomal protein LP0 small subunit ribosomal protein S12e ribosomal protein S17, component of cytosolic 80S ribosome and 40S small subunit large subunit ribosomal protein L9 large subunit ribosomal protein L28e plastid ribosomal protein S16 plastid ribosomal protein S13 60S ribosomal protein L22 predicted RNA-binding protein plastid ribosomal protein L28 large subunit ribosomal protein L6e ribosomal protein S4, component of cytosolic 80S ribosome and 40S small subunit large subunit ribosomal protein L13Ae large subunit ribosomal protein L10Ae ribosomal protein S3a, component of cytosolic 80S ribosome and 40S small subunit large subunit ribosomal protein L19e large subunit ribosomal protein L15e ribosomal protein S23, component of cytosolic 80S ribosome and 40S small subunit glycine hydroxymethyltransferase 5-methyltetrahydropteroyltriglutamate–homocysteine methyltransferase S-adenosylmethionine synthetase NSG1 protein Fe superoxide dismutase (E)-4-hydroxy-3-methylbut-2-enyl-diphosphate synthase thiazole biosynthetic enzyme; THI4 regulatory protein organellar single-stranded DNA binding protein 3 flagellar associated protein plastid transcriptionally active 16 cell division protease FtsH NAD(P) transhydrogenase chaperonin 20 cell division protease FtsH peptidylprolyl isomerase cell wall protein pherophorin-C3

enzyme/protein function

d

6 6 6 5 6 3 3 3 3 2 2 4 5 2 17 12 9 9 7 6 7 5 6 26 25 5 5 4 3 9 4 17 8 4 4 4 2 4

peptides (>95% confidence) 0.60 0.63 0.60 0.62 0.60 0.63 0.51 0.36 0.60 0.29 0.35 0.57 0.58 0.60 0.65 0.70 0.71 0.67 0.67 0.70 0.60 0.63 0.69 0.70 0.74 0.25 0.41 0.52 0.29 0.75 0.71 0.82 0.82 1.01 0.69 0.89 0.90 0.61

fold change 7.66 2.54 2.25 9.62 5.02 9.09 2.38 2.25 2.78 4.20 2.01 3.01 1.41 3.00 1.83 1.39 6.98 1.50 1.09 6.26 4.37 3.90 1.52 2.05 9.38 2.64 9.78 3.06 3.51 3.88 1.57 3.06 1.04 4.73 2.52 7.09 4.08 7.51

× × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × ×

10−02 10−02 10−01 10−03 10−02 10−02 10−01 10−02 10−01 10−02 10−02 10−04 10−01 10−01 10−01 10−01 10−02 10−04 10−03 10−02 10−02 10−02 10−01 10−03 10−03 10−01 10−03 10−02 10−02 10−03 10−01 10−01 10−01 10−01 10−02 10−01 10−01 10−02

p-value


0.66 0.62 0.69 0.54 0.67 0.64 0.72 0.30 0.69 0.20 0.27 0.66 0.60 0.45 0.60 0.60 0.60 0.60 0.60 0.60 0.60 0.57 0.58 0.39 0.60 0.26 0.90 0.44 0.26 0.61 0.37 1.15 0.79 0.68 0.77 0.77 0.87 0.82

fold change 2.24 5.12 5.46 4.99 3.70 1.16 2.02 2.05 5.89 2.93 2.53 2.59 2.03 1.27 4.51 2.66 1.25 4.90 1.40 1.01 4.44 3.16 2.78 1.95 1.23 1.60 1.29 2.83 4.36 1.21 1.48 3.15 4.22 1.21 2.34 1.45 3.47 2.63

× × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × ×

10−01 10−02 10−02 10−03 10−01 10−01 10−01 10−02 10−02 10−02 10−01 10−02 10−01 10−01 10−02 10−03 10−03 10−04 10−05 10−02 10−03 10−02 10−02 10−09 10−04 10−01 10−01 10−01 10−01 10−03 10−01 10−01 10−02 10−01 10−01 10−01 10−01 10−01

p-value


0.54 0.59 0.71 0.63 0.82 0.78 1.03 0.30 1.33 0.21 0.30 0.67 0.54 0.44 0.72 0.99 0.81 0.63 0.73 0.80 0.70 0.87 0.48 0.23 0.45 0.58 0.56 0.43 0.11 0.62 0.39 0.44 0.50 0.46 0.54 0.49 0.55 1.05

fold change

1.41 1.62 5.86 3.88 5.90 5.11 8.93 9.60 3.24 3.12 2.86 1.35 6.30 6.13 1.26 8.93 2.35 1.24 1.47 4.33 5.17 7.11 6.08 3.60 6.97 7.03 1.23 3.73 2.07 1.17 5.09 3.43 6.50 6.97 3.43 2.12 4.78 7.16

× × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × ×

10−01 10−01 10−01 10−02 10−01 10−01 10−01 10−02 10−01 10−02 10−01 10−01 10−01 10−02 10−01 10−01 10−01 10−02 10−02 10−01 10−02 10−01 10−01 10−09 10−04 10−02 10−01 10−01 10−01 10−02 10−01 10−02 10−03 10−02 10−02 10−01 10−02 10−01

p-value


This list was generated after applying a p-value filter (p ≤ 0.05) and mean protein expression ratio ≤0.6. bTranscript ID of Chlamydomonas genome v5.3 based on the Augustus update u11.6 from JGI assembly v5. cClassification of proteins according to KEGG pathway tools. dComplete lists of all proteins identified including predicted subcellular locations can be found in Supporting Information Table S3.

a

g12015.t1 g4743.t1 g11921.t1 Cre12.g529650.t1.2 Cre12.g520500.t1.3 g4687.t2 Cre12.g498250.t1.2 Cre12.g556050.t1.2 g11507.t1 Cre12.g494450.t1.2 Cre12.g493950.t1.2 g8321.t1 g9752.t1 Cre06.g265800.t1.2 g276.t1 Cre06.g308250.t1.2 Cre12.g532550.t1.3 g2068.t1 Cre13.g568650.t1.2 Cre02.g075700.t1.2 Cre02.g091100.t1.2 Cre12.g504200.t1.2 Cre06.g293950.t1.2 Cre03.g180750.t1.2 g5603.t1 Cre17.g708750.t1.2 Cre10.g436050.t1.2 Cre12.g490350.t1.1 Cre04.g214150.t1.3 g12006.t2 Cre02.g081050.t1.2 Cre02.g081250.t1.2 Cre12.g485800.t1.2 Cre01.g054500.t1.2 g8378.t1 Cre17.g720050.t1.2 Cre12.g530300.t1.2 g6305.t1

accession no.

b

Table 2. continued




Article

Figure 6. Venn diagrams showing the confluence of significantly different proteins levels after 24, 48, and 144 h without nitrogen. (A) The number of proteins, shared or unique, whose levels were increased during the indicated time points for cells cultured without nitrogen. (B) The number of proteins, shared or unique, whose levels decreased during nitrogen stress at different time points.

existing ribosomes and synthesis of new ribosomes. This process is incompletely understood but has been suggested to result in unique translational profiles and decreased accuracy.25,57,59 This may explain why we found that the levels of about half the identified ribosomal proteins had recovered by 144 h and were not significantly different from levels measured in cells grown in nitrogen replete media.

pathway, the TCA cycle and glutamate metabolism were also elevated by comparison with N+ controls (Table 1 and Figure 8). These data also revealed that photosynthetic proteins, light harvesting complexes, and chlorophyll, cysteine, and methionine metabolism proteins were in low abundance (Table 2), as might be expected when nitrogen supply is severely limited. Thus, the visualization and mining of global enzymes and metabolite networks using VANTED bioinformatics software, as employed here, turned out to be a reasonably conservative approach for identification of biomarkers that might have a potential role during nitrogen stress when lipid accumulates. Among the metabolites accumulating during nitrogen stress were citric acid and triethanolamine, which may contribute to lipid accumulation via fatty acid and complex lipid synthesis, respectively.

Quantitative Proteomics and Metabolomics Networking Provides a Global Overview of the Changes in the Proteome and Metabolome of Chlamydomonas in Response to Nitrogen Stress

For better visualization of differences induced by nitrogen deprivation at the proteome and metabolome levels and to integrate these data sets into the global expressed network we employed the network visualization software VANTED and the KEGG database.48 The lists of metabolites and proteins identified and compared are provided in Supplemental Tables S2 and S3, respectively. Briefly, the mapped enzymes and compounds belonging to 14 pathways were conceptually linked to 210 proteins and 48 metabolites identified in this study. The metabolic pathway maps generated using VANTED in which these proteins and metabolites participate are given in Supplemental Figures S4A−S4H. Based on our proteomics and metabolite profiling, FAMES, and biochemical data we propose a simplified carbon precursor flow scheme that leads to lipid biosynthesis under nitrogen starvation (Figure 8). This model incorporates our metabolite and protein data with information on compartmentalization of central carbon metabolism based on Johnson and Alric.56 It shows with reasonable clarity the flow of carbon precursors initiating from glycolysis/gluconeogenesis to the different metabolic pathways operative during nitrogen deprivation. The carbon precursors generated via the glycolytic pathway are supplied largely to pyruvate metabolism, the CO2 fixation pathway, starch and sucrose metabolism, the pentose phosphate pathway and the TCA cycle. Our metabolite data demonstrated that during nitrogen starvation the TCA cycle shuttles carbon equivalents between starch synthesis, glutamate metabolism, arginine and proline metabolism, and lipid metabolism. The iTRAQ-based proteomics data set supported this interpretation by demonstrating some proteins involved in starch and sucrose metabolism, calcium signaling, and oxidative phosphorylation were in high abundance during nitrogen stress. Specific enzymes of glycolysis, pyruvate metabolism, pentose phosphate

■

DISCUSSION

Physiological Overview of Metabolic Shifts of Chlamydomonas Cells under N-Limiting Conditions

When Chlamydomonas was switched from nitrogen-replete to nitrogen-deprived conditions, multiple changes occurred in both metabolites and proteins involved in nitrogen, carbon, and energy metabolism. These included metabolites and enzymes involved in transfer of ammonium groups between amino acids and proteins involved in nitrogen assimilation and transport. There were increases in many proteins of oxidative phosphorylation, consistent with an attempt by the cells to compensate for an energy deficit. In contrast, many proteins involved in photosynthesis and protein synthesis were reduced in abundance. Carbon metabolism shifted from glucose synthesis to utilization and storage as starch. Likewise cellular fatty acid content increased commensurate with increases in the saturated and monounsaturated species over polyunsaturated fatty acids. These data define important profiles at the level of both protein and metabolite abundance that reflect the global response to nitrogen limitation over a 144 h period, which includes increased lipid synthesis and storage that may have utility as an oil source. However, this lipid accumulation occurs at the expense of slower growth and reduction in both photosynthetic proteins and pigments. 1388



Article

Figure 7. Proteins involved in oxidative phosphorylation are largely increased during nitrogen deprivation. (A) The cartoon illustrates the respiratory complex in Chlamydomonas reinhardtti. (B) The table lists the changes in concentration of proteins of the complexes illustrated in A involved in oxidative phosphorylation during nitrogen deprivation. The icons indicate fold change (↑ increased; ↓ decreased; and → unchanged) and p-values (*** ≤ 0.05; * >0.049 < 0.1; and ns >0.5).

Nitrogen Deprivation Alters the Expression of Proteins in the Glycolysis and Gluconeogenesis Pathways

nitrogen deprivation were clustered in specific metabolic pathways. As expected, there was an increase in abundance of many proteins involved in amino acid metabolism as well as in nitrogen assimilation and metabolism in response to nitrogen

KEGG GO pathway analysis was used to evaluate whether or not the proteins with significant changes in abundance due to 1389



Article

Figure 8. Regulatory model for compartmentalization and pathway flow of central carbon metabolism and a scheme proposed for lipid biosynthesis in Chlamydomonas. This regulatory model is based on the data reported in this manuscript and Johnson and Alric (2013; ref 53). Proteins and metabolites that are increased in abundance during nitrogen starvation are shown in red, those decreased are shown in green, and those unchanged or undetected are in black (enzymes) or gray (metabolites) using both proteomics and metabolomics profiles defined here. The abbreviations for the enzymes included are as follows: AMY, alpha amylase; PhoA and PhoB, starch phosphorylase; HK, hexokinase; PGI, phosphoglucose isomerase; PFK, phosphofructokinase; FBP, fructose 1,6-bisphosphatase; TPI, triose phosphate isomerase; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; PGM, phosphoglycerate mutase; ALD, fructose bisphosphate aldolase; 6PGHD, 6-phosphogluconate dehydrogenase; TK, transketolase; PGK, phosphoglycerate kinase; RuBisco, ribulose 1,5-bisphosphate carboxylase; PRK, phosphoribulokinase; PDH, pyruvate dehydrogenase; ACCase, Acetyl-CoA carboxylase; MCAT, malonyl:CoA [ACP] transacylase; ACSL, long chain acyl-CoA synthetase (AMPforming); ENO, enolase; ME, malic enzyme; ACS, acetyl-CoA synthetase; CS, citrate synthase; IDH, isocitrate dehydrogenase; SDH, succinate dehydrogenase; MDH, malate dehydrogenase; ACL, ATP-citrate lyase; GPAT, glycerol-3-phosphate acetyltransferase; LPAAT, lyso-phosphatidic acid acyltransferase; PAP, phosphatidic acid phosphatase; and DGAT, diacylglycerol acyltransferase.

by the action of PDH feeds into the TCA cycle. The analysis using VANTED software provided insight into flux through the TCA and glyoxylate pathways. Levels of citrate, 2-ketoglutarate, succinate, and malate tended to increase with nitrogen deprivation. Likewise, NAD-dependent isocitrate dehydrogenase (Cre17.g728800.t1.2; one peptide), 2-oxoglutarate dehydrogenase (Cre12.g537200.t1.2, p > 0.05) and succinate dehydrogenase (Cre06.g264200.t1.2, p < 0.05) were all increased in abundance. Two critical enzymes of the glyoxylate pathway which bypasses the loss of CO2 in the TCA cycle, malate synthase and isocitrate lyase, were reduced in abundance. Together these data indicate the cells are primarily in a catabolic energy state. While the data for glycolytic and TCA cycle metabolite and protein abundance favors the catabolic state, our analyses provided some insight into how the cell might continue to

deprivation. There was also a coordinate increase in some proteins required for glycolysis and oxidative phosphorylation, indicating an increased need for energy and ATP. Only two proteins involved in lipid synthesis had increased expression. These included a long chain acyl-CoA synthetase required to activate long chain fatty acids with coenzyme A and a glycerol3-phosphate dehydrogenase that participates in glycerolipid synthesis. Pyruvate is the product of glycolysis, which can be further converted to acetyl-CoA through the activity of the pyruvate dehydrogenase complex (PDH). As noted above, we observed increased abundance of two subunits of the pyruvate dehydrogenase complex over time after removal of nitrogen. Pyruvate metabolite levels were reduced at 24 and 48 h after removal of nitrogen but recovered by 144 h (Supplemental Table S2 and Supplemental Figure S4B). Acetyl-CoA produced 1390



Article

reduced but not eliminated.64 The utilization of acetate as a carbon source requires uptake from the media and activation with coenzyme A. In Chlamydomonas, acetate can be incorporated by one of two pathways, one-step activation by acetyl-CoA synthetase or in a two-step process requiring acetate kinase and phosphate acetyltransferase; all three proteins were identified in our iTRAQ experiments. Acetyl-CoA synthetase was significantly reduced during nitrogen deprivation, but the latter two proteins remained unchanged. This may indicate that the fate of acetate is different when nitrogen is limiting and may partially explain the increase in starch and lipid synthesis and storage. Growth on acetate also requires the glyoxylate cycle. We found that when cultured without nitrogen, the abundance of malate synthase and isocitrate lyase were significantly reduced (Supplemental Figure S4C). Our proteomics data is in line with a recent transcriptome and 13C-labeling study, which showed that the glyoxylate pathway was downregulated in nitrogen stressed cells.23 Chlamydomonas cells cultured with nitrogen will assimilate nitrogen under light, whereas nitrogen-stressed cells will assimilate nitrogen in the dark and are dependent on the storage of starch; however, the presence of acetate in the growth media can override the dependency on both light and starch.54,56 This forms the basis of carbon/nitrogen partitioning, and therefore, the availability of carbon is essential for nitrogen uptake.54,56 Thus, under mixotrophic growth condition (as used in the present studies) but in the absence of nitrogen, acetate assimilated via the acetate kinase and phosphate acetyltransferase route is catabolized to provide energy to maintain basic cellular functions, and the excess carbon is stored as starch and lipid storage macromolecules that do not contain nitrogen.

synthesize and store lipid during nitrogen deprivation. Three enzymes had increased abundance that can contribute to providing acetyl-CoA as a substrate for lipid synthesis. These include pyruvate carboxylase (PC), citrate synthase, and ATPcitrate lyase. Pyruvate carboxylase catalyzes the synthesis of oxaloacetate from pyruvate and HCO3. It is a key enzyme leading to gluconeogenesis and, as suggested from these data, to lipogenesis. PC is activated by high levels of acetyl-CoA, conditions that disfavor PDH activity. Citrate is produced from oxaloacetate and acetyl-CoA by citrate synthase when these metabolites are in excess of that needed to run the TCA cycle and may be exported for use in fatty acid synthesis. Conversion of citrate back to oxaloacetate and acetyl-CoA to provide acetylCoA for fatty acid biosynthesis requires the activity of ATPcitrate lysase. In contrast, isocitrate dehydrogenase, which further metabolizes citrate in the TCA cycle, was downregulated during nitrogen deprivation. Thus, our data reported above showing citrate levels increase coordinately with PC, CS, and ATP-citrate lyase abundance correlate with the observed increase in lipid storage over 144 h when Chlamydomonas is cultured in the absence of nitrogen. We confirmed the impact of elevated citrate by adding it to the growth media during nitrogen-replete growth conditions and demonstrated that citrate increased lipid storage. Alterations in Methionine Levels and Implications for Methyl Group Transfer

Our metabolite analysis demonstrated significant decreases in the abundance of aspartate, phenylalanine, and tryptophan commensurate with a significant increase in the levels of methionine when nitrogen was removed from the culture media over 144 h. Methionine, a sulfur-containing amino acid, is a universal initiator of protein synthesis and is involved in a variety of cellular processes including methyl-group transfer, primarily through S-adenosylmethionine (SAM). Methylation reactions involving SAM are essential for the biosynthesis of complex lipids, proteins, DNA, and RNA. In Chlamydomonas, methionine via SAM has an important role in the biosynthesis of the glycerolipid diacylglyceryl-trimethylhomoserine (DGTS), a major component of membranes.61,62 The accumulation of methionine might be either due to increased synthesis or decreased catabolism (Supplemental Figure S4E). Methionine is synthesized from aspartate via homocysteine, a metabolite whose concentration trended toward an increase during nitrogen limitation. However, this metabolite was not included in the variable map, because the VIP score was 0.05), which correlated with our Nile Red data showing an increase in the size and abundance of lipid droplets. Triglyceride lipases were increased during nitrogen deprivation (p > 0.05), which may reflect membrane lipid turnover or utilization of TAGs for energy.

(PS-I and II) and the cytochrome complex, levels of proteins of the light-harvesting complexes were also reduced. Subunits of the ATP synthase were differentially abundant in the plastid compared with the mitochondria. The levels of all the chloroplastic ATP synthase subunits, atpA, atpB, atpE, atpF, and atpG, were found to be reduced, whereas all the mitochondrial F1F0 ATP synthase subunits, ASA2, 3, ATP15, and ATP1a, were increased as compared to the nitrogen-replete cells. Thus, we conclude that under nitrogen stress, the algal cells have diminished photosynthetic capacity due to reduction of components of both photosystems I and II, as well as the plastidic ATP-generating system. Our results showing photosynthetic capacity is reduced when Chlamydomonas are deprived of nitrogen are in agreement with previous findings using different strains and growth conditions.23,36 In contrast to the global decrease in proteins of the photosynthetic apparatus, we found most proteins associated with oxidative phosphorylation were increased in abundance during nitrogen deprivation. This suggests that while the overall photosynthetic capacity of the cells during nitrogen deprivation was reduced, there was an apparent increase in respiratory capacity. Recently, results from a study of Nannochloropsis gaditana indicated that under nitrogen stress, photosystem II of the algal cells is more severely depressed than photosystem I, leading to activation of alternative electron-transferring pathways such as cyclic electron transport.81

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CONCLUSIONS The integrated analysis of GC-MS-based intracellular metabolites with changes in proteins quantified via iTRAQ facilitated peptide labeling technique presented here represents one of the most comprehensive functional determinations of metabolic regulation in Chlamydomonas under continuous nitrogen stress leading to the accumulation of storage lipids performed to date. We observed a massive metabolic reprogramming in response to nitrogen deprivation. Overall, in the face of complete nitrogen deprivation, metabolic adjustments in Chlamydomonas compensate by decreasing cell growth rates and photosynthetic components, while accumulating starch, citrate, trehelose, and lipids. This quick adaptation and metabolic plasticity shows the robustness of the Chlamydomonas to nutrient starvation in an effort to protect the organism until conditions become more favorable for growth. During nitrogen deprivation, there was a reduced abundance of proteins and metabolites involved in photosynthesis, carbon assimilation, and chlorophyll biosynthesis and an increase in abundance of proteins of oxidative phosphorylation, GS-GOGAT pathway, nitrogen metabolism, lipid biosynthesis, and starch synthesis components. Measured differences between cells cultured under nitrogen-replete and nitrogen-limiting conditions also included the accumulation of key metabolic intermediary components such as citrate and various amino acids including methionine. Additionally, we observed that an exogenous supply of citrate up to 6 mM increased the total lipid content in algae grown in N+ media. These data lead to the formulation of additional questions that remain to be answered. Importantly, if photosynthetic carbon fixation is decreased in Chlamydomonas and components of both glycolysis and the TCA cycle are up-regulated to support basic cellular functions, why do the cells store energyrich lipids? Interestingly, similar findings were reported earlier in the marine cyanobacterium Prochlorococcus marinus MED4 also examined after nitrogen limitation.86 It is possible that these pathway shifts have a role in regulating the pace of nitrogen reassimilation, supply of carbon skeletons, and/or transfer of excess carbon into fatty acid biosynthesis. Additionally, it is reasonable to suggest that the TCA cycle acts as a central hub in balancing the demand and supply of carbon skeletons to other pathways as they are generated from catabolic processes activated in the face of limiting nitrogen.

Few Changes Identified in Proteins Related to Lipid Metabolism

Nitrogen deprivation leads to the accumulation of storage lipids, particularly TAGs. The source of the lipids appears to be both from the breakdown of plastidic and endoplasmic reticulum membranes as well as from de novo synthesis.82 Thus, we expected that this would be evidenced by changes in the abundance of proteins involved in lipid metabolism. We found that changes in the levels of proteins related to fatty acid and lipid metabolism during nitrogen deprivation were modest, as previously reported (Supplemental Figure S5).20,23,83 There was a significant increase in long chain acyl-CoA synthetase, an enzyme that activates fatty acids for metabolism, including the synthesis of complex lipids. Given that most fatty acid synthetic enzymes were either unchanged or reduced in abundance but that citrate levels increase, we would suggest regulation of lipid synthesis and storage occurs by biochemical mechanisms dependent upon substrate levels and post-translational modifications at the level of individual enzymes of the fatty acid and complex lipid pathways to promote lipid storage observed during nitrogen deprivation. Mammalian AMP-activated protein kinase (AMPK) is generally considered a “metabolic switch” for beta-oxidation and inhibits acetyl CoA carboxylase activity (ACCase).84,85 We observed that Chlamydomonas AMPK (Cre12.g528000.t1.2; p > 0.05) was reduced in abundance under nitrogen-restrictive conditions, which may favor fatty acid synthesis by promoting the dephosphorylated form of ACCase. We note that previous attempts to overexpress the relevant ACCase gene failed to improve lipid storage in the diatoms Cyclotella cryptica and Navicula saprofila, but our data would argue regulation may depend not on the amount of protein but on regulation of its activity.3 Recently, a transcriptome study in Chlorella vulgaris showed that ACCase was upregulated under high lipidproducing conditions,83 but we did not observe similar changes for any of the three identified and quantified ACCase variants 1393



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ASSOCIATED CONTENT

S Supporting Information *

Table S1: FAMES data; Table S2: metabolite data; Table S3: protein and peptide data; Figure S1: growth analysis of Chlamydomonas in TAP N+ and N− media; Figure S2: starch, chlorophyll, and total carotenoid content of Chlamydomonas cultured under nitrogen-replete and nitrogen-depleted conditions; Figure S3: heat map of relative expression of proteins selected after applying a p-value threshold of ≤0.05; Figure S4: combined pathway maps of identified and quantified proteins and metabolites using VANTED software; Figure S5: heat map of identified and quantified lipid metabolism proteins. This material is available free of charge via the Internet at http:// pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Fax: 402-472-7842. Tel.: 402-4726504. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Science Foundation EPSCoR Program, grant no. EPS-1004094. The authors are thankful to an undergraduate assistant, Boqiang Tu, for his excellent help in the citrate-supplementation experiments. We thank Dr. Wayne R. Riekhof for critically reading the manuscript.

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