Characterizing the Anaerobic Response of Chlamydomonas reinhardtii by Quantitative Proteomics

The versatile metabolism of the green alga Chlamydomonas reinhardtii is reflected in its complex response to anaerobic conditions. The anaerobic response is also remarkable in the context of renewable energy because C. reinhardtii is able to produce hydrogen under anaerobic conditions. To identify proteins involved during anaerobic acclimation as well as to localize proteins and pathways to the powerhouses of the cell, chloroplasts and mitochondria from C. reinhardtii in aerobic and anaerobic (induced by 8 h of argon bubbling) conditions were isolated and analyzed using comparative proteomics. A total of 2315 proteins were identified. Further analysis based on spectral counting clearly localized 606 of these proteins to the chloroplast, including many proteins of the fermentative metabolism. Comparative quantitative analyses were performed with the chloroplast-localized proteins using stable isotopic labeling of amino acids ([13C6]arginine/[12C6]arginine in an arginine auxotrophic strain). The quantitative data confirmed proteins previously characterized as induced at the transcript level as well as identified several new proteins of unknown function induced under anaerobic conditions. These proteins of unknown function provide new candidates for further investigation, which could bring insights for the engineering of hydrogen-producing alga strains. The versatile metabolism of the green alga Chlamydomonas reinhardtii is reflected in its complex response to anaerobic conditions. The anaerobic response is also remarkable in the context of renewable energy because C. reinhardtii is able to produce hydrogen under anaerobic conditions. To identify proteins involved during anaerobic acclimation as well as to localize proteins and pathways to the powerhouses of the cell, chloroplasts and mitochondria from C. reinhardtii in aerobic and anaerobic (induced by 8 h of argon bubbling) conditions were isolated and analyzed using comparative proteomics. A total of 2315 proteins were identified. Further analysis based on spectral counting clearly localized 606 of these proteins to the chloroplast, including many proteins of the fermentative metabolism. Comparative quantitative analyses were performed with the chloroplast-localized proteins using stable isotopic labeling of amino acids ([13C6]arginine/[12C6]arginine in an arginine auxotrophic strain). The quantitative data confirmed proteins previously characterized as induced at the transcript level as well as identified several new proteins of unknown function induced under anaerobic conditions. These proteins of unknown function provide new candidates for further investigation, which could bring insights for the engineering of hydrogen-producing alga strains. Due to an urgent demand for clean energy for the future, there has been an increased interest in research regarding Chlamydomonas reinhardtii in the context of renewable energy. Among the numerous possibilities for clean energy, hydrogen is considered to be one of the most attractive because its combustion produces zero carbon emission (1.Hemschemeier A. Melis A. Happe T. Analytical approaches to photobiological hydrogen production in unicellular green algae.Photosynth. Res. 2010; (in press)Google Scholar). C. reinhardtii is a promising organism for renewable energy because it is able to produce hydrogen as a photosynthetic product (1.Hemschemeier A. Melis A. Happe T. Analytical approaches to photobiological hydrogen production in unicellular green algae.Photosynth. Res. 2010; (in press)Google Scholar, 2.Gaffron H. 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Hydrogenases in green algae: do they save the algae's life and solve our energy problems?.Trends Plant Sci. 2002; 7: 246-250Abstract Full Text Full Text PDF PubMed Scopus (154) Google Scholar). There has been an array of studies that have investigated C. reinhardtii under anaerobic conditions and provided valuable insights into the metabolic changes undertaken by the cell to acclimate to an anaerobic condition. Despite the wide range of knowledge regarding C. reinhardtii and anaerobiosis, many of the studies have been based on transcript or metabolite levels (6.Klöck G. Kreuzberg K. Compartmented metabolite pools in protoplasts from the green alga Chlamydomonas reinhardtii: changes after transition from aerobiosis to anaerobiosis in the dark.Biochim. Biophys. Acta. 1991; 1073: 410-415Crossref PubMed Scopus (14) Google Scholar, 7.Mus F. Dubini A. Seibert M. Posewitz M.C. Grossman A.R. 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Insights into the survival of Chlamydomonas reinhardtii during sulfur starvation based on microarray analysis of gene expression.Eukaryot. Cell. 2004; 3: 1331-1348Crossref PubMed Scopus (147) Google Scholar). To expand the current knowledge on the subject, we investigated the chloroplast and mitochondrial proteomes of C. reinhardtii under anaerobiosis. It is now well established that under anaerobic conditions C. reinhardtii induces a wide range of fermentative pyruvate-dependent metabolic pathways (11.Dubini A. Mus F. Seibert M. Grossman A.R. Posewitz M.C. Flexibility in anaerobic metabolism as revealed in a mutant of Chlamydomonas reinhardtii lacking hydrogenase activity.J. Biol. Chem. 2009; 284: 7201-7213Abstract Full Text Full Text PDF PubMed Scopus (89) Google Scholar, 12.Grossman A.R. Croft M. Gladyshev V.N. Merchant S.S. Posewitz M.C. Prochnik S. Spalding M.H. Novel metabolism in Chlamydomonas through the lens of genomics.Curr. Opin. Plant Biol. 2007; 10: 190-198Crossref PubMed Scopus (131) Google Scholar, 13.Hemschemeier A. Happe T. The exceptional photofermentative hydrogen metabolism of the green alga Chlamydomonas reinhardtii.Biochem. Soc. Trans. 2005; 33: 39-41Crossref PubMed Scopus (59) Google Scholar). The induction of these pathways has been confirmed at the transcript level for dark anaerobic and sulfur-depleted anaerobic conditions (7.Mus F. Dubini A. Seibert M. Posewitz M.C. Grossman A.R. Anaerobic acclimation in Chlamydomonas reinhardtii: anoxic gene expression, hydrogenase induction, and metabolic pathways.J. Biol. Chem. 2007; 282: 25475-25486Abstract Full Text Full Text PDF PubMed Scopus (231) Google Scholar, 8.Nguyen A.V. Thomas-Hall S.R. Malnoë A. Timmins M. Mussgnug J.H. Rupprecht J. Kruse O. Hankamer B. Schenk P.M. Transcriptome for photobiological hydrogen production induced by sulfur deprivation in the green alga Chlamydomonas reinhardtii.Eukaryot. Cell. 2008; 7: 1965-1979Crossref PubMed Scopus (109) Google Scholar, 10.Zhang Z. Shrager J. Jain M. Chang C.W. Vallon O. Grossman A.R. Insights into the survival of Chlamydomonas reinhardtii during sulfur starvation based on microarray analysis of gene expression.Eukaryot. Cell. 2004; 3: 1331-1348Crossref PubMed Scopus (147) Google Scholar) as well as through the increase in fermentative products such as formate, ethanol, and acetate (6.Klöck G. Kreuzberg K. Compartmented metabolite pools in protoplasts from the green alga Chlamydomonas reinhardtii: changes after transition from aerobiosis to anaerobiosis in the dark.Biochim. Biophys. Acta. 1991; 1073: 410-415Crossref PubMed Scopus (14) Google Scholar, 9.Timmins M. Zhou W. Rupprecht J. Lim L. Thomas-Hall S.R. Doebbe A. Kruse O. Hankamer B. Marx U.C. Smith S.M. Schenk P.M. The metabolome of Chlamydomonas reinhardtii following induction of anaerobic H2 production by sulfur deprivation.J. Biol. Chem. 2009; 284: 23415-23425Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar). Despite the identification of these induced proteins of the fermentative metabolism, there have been little biochemical data to support the localization for some of the proteins (7.Mus F. Dubini A. Seibert M. Posewitz M.C. Grossman A.R. Anaerobic acclimation in Chlamydomonas reinhardtii: anoxic gene expression, hydrogenase induction, and metabolic pathways.J. Biol. Chem. 2007; 282: 25475-25486Abstract Full Text Full Text PDF PubMed Scopus (231) Google Scholar, 14.Atteia A. van Lis R. Gelius-Dietrich G. Adrait A. Garin J. Joyard J. Rolland N. Martin W. Pyruvate formate-lyase and a novel route of eukaryotic ATP synthesis in Chlamydomonas mitochondria.J. Biol. Chem. 2006; 281: 9909-9918Abstract Full Text Full Text PDF PubMed Scopus (105) Google Scholar). Although discovering induced proteins is crucial for the understanding of the anaerobic response, it is equally important to understand the localization of these proteins to engineer a strain that potentially produces higher amounts of hydrogen. In this study, we aimed to localize currently known key proteins involved in the anaerobic response to within or outside of the chloroplast as well as to identify proteins that are significantly induced under anaerobiosis through quantitative proteomics. Qualitative and semiquantitative analyses of isolated chloroplasts and mitochondria from aerobic and anaerobic C. reinhardtii cultures allowed for the identification and localization of proteins, including a handful of fermentative proteins. We identified 606 proteins highly likely to be chloroplast-localized that well supplement the recently published significant list of mitochondrial proteins by Atteia et al. (15.Atteia A. Adrait A. Brugière S. Tardif M. van Lis R. Deusch O. Dagan T. Kuhn L. Gontero B. Martin W. Garin J. Joyard J. Rolland N. A proteomic survey of Chlamydomonas reinhardtii mitochondria sheds new light on the metabolic plasticity of the organelle and on the nature of the alpha-proteobacterial mitochondrial ancestor.Mol. Biol. Evol. 2009; 26: 1533-1548Crossref PubMed Scopus (141) Google Scholar) as well as aspects of the chloroplast proteome already characterized (16.Allmer J. Naumann B. Markert C. Zhang M. Hippler M. Mass spectrometric genomic data mining: novel insights into bioenergetic pathways in Chlamydomonas reinhardtii.Proteomics. 2006; 6: 6207-6220Crossref PubMed Scopus (50) Google Scholar, 17.Michelet L. Zaffagnini M. Vanacker H. Le Maréchal P. Marchand C. Schroda M. Lemaire S.D. Decottignies P. In vivo targets of S-thiolation in Chlamydomonas reinhardtii.J. Biol. Chem. 2008; 283: 21571-21578Abstract Full Text Full Text PDF PubMed Scopus (87) Google Scholar, 18.Naumann B. Busch A. Allmer J. Ostendorf E. Zeller M. Kirchhoff H. Hippler M. Comparative quantitative proteomics to investigate the remodeling of bioenergetic pathways under iron deficiency in Chlamydomonas reinhardtii.Proteomics. 2007; 7: 3964-3979Crossref PubMed Scopus (140) Google Scholar, 19.Stauber E.J. Fink A. Markert C. Kruse O. Johanningmeier U. Hippler M. Proteomics of Chlamydomonas reinhardtii light-harvesting proteins.Eukaryot. Cell. 2003; 2: 978-994Crossref PubMed Scopus (141) Google Scholar, 20.Yamaguchi K. Beligni M.V. Prieto S. Haynes P.A. McDonald W.H. Yates 3rd, J.R. Mayfield S.P. Proteomic characterization of the Chlamydomonas reinhardtii chloroplast ribosome. Identification of proteins unique to th e70 S ribosome.J. Biol. Chem. 2003; 278: 33774-33785Abstract Full Text Full Text PDF PubMed Scopus (91) Google Scholar, 21.Yamaguchi K. Prieto S. Beligni M.V. Haynes P.A. McDonald W.H. Yates 3rd, J.R. Mayfield S.P. Proteomic characterization of the small subunit of Chlamydomonas reinhardtii chloroplast ribosome: identification of a novel S1 domain-containing protein and unusually large orthologs of bacterial S2, S3, and S5.Plant Cell. 2002; 14: 2957-2974Crossref PubMed Scopus (67) Google Scholar). We further analyzed the identified chloroplast proteins by means of quantitative proteomics, which allowed for identification of proteins that are induced under anaerobiosis. These consist of the proteins previously characterized to be highly expressed under anaerobiosis, including those that are co-induced under anaerobic and copper-deficient conditions. Additionally, induced proteins of particular interest are those of unknown function, some of which are part of the GreenCut proteins (22.Merchant S.S. Prochnik S.E. Vallon O. Harris E.H. Karpowicz S.J. Witman G.B. Terry A. Salamov A. Fritz-Laylin L.K. Maréchal-Drouard L. Marshall W.F. Qu L.H. Nelson D.R. Sanderfoot A.A. Spalding M.H. Kapitonov V.V. Ren Q. Ferris P. Lindquist E. Shapiro H. Lucas S.M. Grimwood J. Schmutz J. Cardol P. Cerutti H. Chanfreau G. Chen C.L. Cognat V. Croft M.T. Dent R. Dutcher S. Fernández E. Fukuzawa H. González-Ballester D. González-Halphen D. Hallmann A. Hanikenne M. Hippler M. Inwood W. Jabbari K. Kalanon M. Kuras R. Lefebvre P.A. Lemaire S.D. Lobanov A.V. Lohr M. Manuell A. Meier I. Mets L. Mittag M. Mittelmeier T. Moroney J.V. Moseley J. Napoli C. Nedelcu A.M. Niyogi K. Novoselov S.V. Paulsen I.T. Pazour G. Purton S. Ral J.P. Riaño-Pachón D.M. Riekhof W. Rymarquis L. Schroda M. Stern D. Umen J. Willows R. Wilson N. Zimmer S.L. Allmer J. Balk J. Bisova K. Chen C.J. Elias M. Gendler K. Hauser C. Lamb M.R. Ledford H. Long J.C. Minagawa J. Page M.D. Pan J. Pootakham W. Roje S. Rose A. Stahlberg E. Terauchi A.M. Yang P. Ball S. Bowler C. Dieckmann C.L. Gladyshev V.N. Green P. Jorgensen R. Mayfield S. Mueller-Roeber B. Rajamani S. Sayre R.T. Brokstein P. Dubchak I. Goodstein D. Hornick L. Huang Y.W. Jhaveri J. Luo Y. Martínez D. Ngau W.C. Otillar B. Poliakov A. Porter A. Szajkowski L. Werner G. Zhou K. Grigoriev I.V. Rokhsar D.S. Grossman A.R. The Chlamydomonas genome reveals the evolution of key animal and plant functions.Science. 2007; 318: 245-250Crossref PubMed Scopus (1881) Google Scholar), making them favorable candidates for further analyses. The arginine auxotrophic C. reinhardtii strain CC424 mt− was used for all experiments. Cells were grown under standard conditions (23.Naumann B. Stauber E.J. Busch A. Sommer F. Hippler M. N-terminal processing of Lhca3 is a key step in remodeling of the photosystem I-light-harvesting complex under iron deficiency in Chlamydomonas reinhardtii.J. Biol. Chem. 2005; 280: 20431-20441Abstract Full Text Full Text PDF PubMed Scopus (111) Google Scholar) or supplemented with isotopically labeled l-[13C6]arginine as described in Naumann et al. (23.Naumann B. Stauber E.J. Busch A. Sommer F. Hippler M. N-terminal processing of Lhca3 is a key step in remodeling of the photosystem I-light-harvesting complex under iron deficiency in Chlamydomonas reinhardtii.J. Biol. Chem. 2005; 280: 20431-20441Abstract Full Text Full Text PDF PubMed Scopus (111) Google Scholar) and grown under 50 microeinsteins·m−2·s−1 light. Isotopically labeled cultures were maintained in standard, aerobic conditions and cultivated to a cell density of 3–4 × 106 cells/ml. Unlabeled cell cultures were also grown to a cell density of 3–4 × 106 cells/ml followed by anaerobic induction by bubbling with argon for 8 h under 80 microeinsteins·m−2·s−1 light. Formate levels were measured with a test kit (catalog number 10979732035) from R-Biopharm AG, Darmstadt, Germany, following the supplier's instructions. Chloroplasts were isolated as described by Naumann et al. (23.Naumann B. Stauber E.J. Busch A. Sommer F. Hippler M. N-terminal processing of Lhca3 is a key step in remodeling of the photosystem I-light-harvesting complex under iron deficiency in Chlamydomonas reinhardtii.J. Biol. Chem. 2005; 280: 20431-20441Abstract Full Text Full Text PDF PubMed Scopus (111) Google Scholar). Mitochondria were isolated as described by Eriksson et al. (24.Eriksson M. Gardestrom P. Samuelsson G. Isolation, purification, and characterization of mitochondria from Chlamydomonas reinhardtii.Plant Physiol. 1995; 107: 479-483Crossref PubMed Scopus (65) Google Scholar) with a few modifications as described by Busch et al. (25.Busch A. Rimbauld B. Naumann B. Rensch S. Hippler M. Ferritin is required for rapid remodeling of the photosynthetic apparatus and minimizes photo-oxidative stress in response to iron availability in Chlamydomonas reinhardtii.Plant J. 2008; 55: 201-211Crossref PubMed Scopus (53) Google Scholar). Protein analysis and immunodetection were performed as described in Naumann et al. (23.Naumann B. Stauber E.J. Busch A. Sommer F. Hippler M. N-terminal processing of Lhca3 is a key step in remodeling of the photosystem I-light-harvesting complex under iron deficiency in Chlamydomonas reinhardtii.J. Biol. Chem. 2005; 280: 20431-20441Abstract Full Text Full Text PDF PubMed Scopus (111) Google Scholar) and Hippler et al. (26.Hippler M. Klein J. Fink A. Allinger T. Hoerth P. Towards functional proteomics of membrane protein complexes: analysis of thylakoid membranes from Chlamydomonas reinhardtii.Plant J. 2001; 28: 595-606Crossref PubMed Scopus (138) Google Scholar). Antibodies against CoxIIB were purchased from AgriSera, and antibodies for TEF7 (against peptide sequence EEIYIGFVKEEGFGS) were purchased from Eurogentec. Samples for mass spectrometric analyses were prepared as in Naumann et al. (23.Naumann B. Stauber E.J. Busch A. Sommer F. Hippler M. N-terminal processing of Lhca3 is a key step in remodeling of the photosystem I-light-harvesting complex under iron deficiency in Chlamydomonas reinhardtii.J. Biol. Chem. 2005; 280: 20431-20441Abstract Full Text Full Text PDF PubMed Scopus (111) Google Scholar). Samples fractionated by SDS-PAGE were excised into 46 bands for the chloroplast samples and 56 bands for the mitochondrial samples and digested tryptically. A schematic diagram in Fig. 1 demonstrates the number of preparations and measurements that were performed in this study. LC-MS/MS analyses were performed as described in Naumann et al. (23.Naumann B. Stauber E.J. Busch A. Sommer F. Hippler M. N-terminal processing of Lhca3 is a key step in remodeling of the photosystem I-light-harvesting complex under iron deficiency in Chlamydomonas reinhardtii.J. Biol. Chem. 2005; 280: 20431-20441Abstract Full Text Full Text PDF PubMed Scopus (111) Google Scholar) with the following modifications: the nano-LC was performed on an Ultimate 3000 system (Dionex, Sunnyvale, CA) with the solvent gradients described in Stauber et al. (19.Stauber E.J. Fink A. Markert C. Kruse O. Johanningmeier U. Hippler M. Proteomics of Chlamydomonas reinhardtii light-harvesting proteins.Eukaryot. Cell. 2003; 2: 978-994Crossref PubMed Scopus (141) Google Scholar) and Naumann et al. (23.Naumann B. Stauber E.J. Busch A. Sommer F. Hippler M. N-terminal processing of Lhca3 is a key step in remodeling of the photosystem I-light-harvesting complex under iron deficiency in Chlamydomonas reinhardtii.J. Biol. Chem. 2005; 280: 20431-20441Abstract Full Text Full Text PDF PubMed Scopus (111) Google Scholar). An HD-05 γPrecolumn holder from Dionex (order number 6720.0012) was used for trapping (4 min), and a 3-γm Atlantis® (part number 186002197) column from Waters was used for peptide separation (56 min). An LTQ Orbitrap XL (Thermo, Bremen, Germany) mass spectrometer was used with FT Programs 2.0.7, Xcalibur 2.0.7, and LTQ Orbitrap XL MS 2.4 SP1. Peptides were measured in the FTMS (mass range, 375–2000 m/z; resolution, 60,000) and selected for fragmentation in the ion trap mass spectrometer (CID collision energy, 35 V) with a 2-Da isolation width using the "Big 5" method (which selects the five most abundant precursor ions detected in the full scan), requiring a minimum precursor charge state of 2. Automatic gain control was used, and dynamic exclusion was enabled for 90 s. For the MS2 identification of peptides, OMSSA 1The abbreviations used are:OMSSAOpen Mass Spectrometry Search AlgorithmACKacetate kinaseADH1alcohol dehydrogenase 1AMTaccurate mass/time tagATP2mitochondrial ATP synthase β subunitCAT1catalaseCHLmagnesium chelatase subunitCIScitrate synthaseCPX1coproporphyrinogen III oxidaseCoxIIBcytochrome c oxidase subunit IICRD1copper response defect 1 proteinCTH1copper target homolog 1FAD7glycerolipid ω-3-fatty-acid desaturaseFDXferredoxinFPRfalse positive rateFTSHmembrane AAA metalloproteasesGPFGenomic Peptide FinderHYD1Fe-hydrogenaseICL1isocitrate lyase 1KAS23-ketoacyl-acyl carrier protein synthaseLhcalight-harvesting chlorophyll a/b protein of photosystem ILhcbMlight-harvesting chlorophyll a/b protein of photosystem IIMAS1malate synthase 1MDHmalate dehydrogenaseNDANADH dehydrogenaseMETCcystathionine β-lyasePATphosphate acetyltransferasePCYAphycocyanobilin-ferredoxin oxidoreductasePDCpyruvate decarboxylasePFLpyruvate-formate lyasePFR1pyruvate-ferredoxin oxidoreductase 1PsaBphotosystem I protein subunit PsaBPsaDphotosystem I protein subunit PsaDPsbBphotosystem II protein subunit PsbBPsbPphotosystem II protein subunit PsbPPsbQphotosystem II protein subunit PsbQROSreactive oxygen speciesRPE1ribulose-phosphate 3-epimeraseSIR1ferredoxin-sulfite reductaseUROD2uroporphyrinogen decarboxylaseSILACstable isotope labeling with amino acids in cell cultureJGIJoint Genome InstituteNCBINational Center for Biotechnology InformationPBCpeptide, band, and charge stateFforward primerRreverse primerPPDBPlant Proteome DatabaseANanaerobicARaerobicBLASTBasic Local Alignment Search ToolFTMSFT mass spectrometerHYDGHydrogenase assembly factorLHCSRStress-related chlorophyll a/b binding proteinTHICHydroxymethylpyrimidine phosphate synthaseLACTLecithin:cholesterol acyltransferasePETOCytochrome b6f complex subunit V. (version 2.1.4) (27.Geer L.Y. Markey S.P. Kowalak J.A. Wagner L. Xu M. Maynard D.M. Yang X. Shi W. Bryant S.H. Open mass spectrometry search algorithm.J. Proteome Res. 2004; 3: 958-964Crossref PubMed Scopus (1148) Google Scholar) was used with a target/decoy approach (28.Elias J.E. Gygi S.P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry.Nat. Methods. 2007; 4: 207-214Crossref PubMed Scopus (2727) Google Scholar). The maximum number of missed cleavages allowed was set to 2. No modifications were used except a variable heavy [13C6]arginine modification for the SILAC runs. Mass tolerances were set to 0.02 Da for the precursor ions and 0.5 Da for the product ions. The JGI Chlamydomonas gene model database v3.1 and NCBI databases BK000554.2 and NC_001638.1 were merged in a resulting database containing 15001 protein sequences that was used for the database search, and a decoy protein was generated for each of these proteins by sequence reversal. For each set of bands from one sample, an adaptive E-value threshold was determined in such a way that the estimated false positive rate (FPR) was 1% or lower, derived from the following formula: FPR = 2·ndecoys/(ntargets + ndecoys). In addition, the peptides were filtered through a 5-ppm mass accuracy requirement. Open Mass Spectrometry Search Algorithm acetate kinase alcohol dehydrogenase 1 accurate mass/time tag mitochondrial ATP synthase β subunit catalase magnesium chelatase subunit citrate synthase coproporphyrinogen III oxidase cytochrome c oxidase subunit II copper response defect 1 protein copper target homolog 1 glycerolipid ω-3-fatty-acid desaturase ferredoxin false positive rate membrane AAA metalloproteases Genomic Peptide Finder Fe-hydrogenase isocitrate lyase 1 3-ketoacyl-acyl carrier protein synthase light-harvesting chlorophyll a/b protein of photosystem I light-harvesting chlorophyll a/b protein of photosystem II malate synthase 1 malate dehydrogenase NADH dehydrogenase cystathionine β-lyase phosphate acetyltransferase phycocyanobilin-ferredoxin oxidoreductase pyruvate decarboxylase pyruvate-formate lyase pyruvate-ferredoxin oxidoreductase 1 photosystem I protein subunit PsaB photosystem I protein subunit PsaD photosystem II protein subunit PsbB photosystem II protein subunit PsbP photosystem II protein subunit PsbQ reactive oxygen species ribulose-phosphate 3-epimerase ferredoxin-sulfite reductase uroporphyrinogen decarboxylase stable isotope labeling with amino acids in cell culture Joint Genome Institute National Center for Biotechnology Information peptide, band, and charge state forward primer reverse primer Plant Proteome Database anaerobic aerobic Basic Local Alignment Search Tool FT mass spectrometer Hydrogenase assembly factor Stress-related chlorophyll a/b binding protein Hydroxymethylpyrimidine phosphate synthase Lecithin:cholesterol acyltransferase Cytochrome b6f complex subunit V. As an alternative to peptide-mass spectrum matching via a protein database search algorithm, de novo prediction (using PEAKS (29.Ma B. Zhang K. Hendrie C. Liang C. Li M. Doherty-Kirby A. Lajoie G. PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry.Rapid Commun. Mass Spectrom. 2003; 17: 2337-2342Crossref PubMed Scopus (929) Google Scholar)) was used together with Genomic Peptide Finder (GPF) (16.Allmer J. Naumann B. Markert C. Zhang M. Hippler M. Mass spectrometric genomic data mining: novel insights into bioenergetic pathways in Chlamydomonas reinhardtii.Proteomics. 2006; 6: 6207-6220Crossref PubMed Scopus (50) Google Scholar) to yield additional peptide identifications or to confirm peptide identifications that originated from the protein database search. GPF is a tool that takes de novo predicted amino acid sequences and aligns them to the genomic DNA sequence in an error-tolerant way that compensates for de novo sequencing errors. In addition, GPF allows for intron splits to occur within peptide sequence matches. Recently, GPF has been redesigned to allow for intron splits to occur within a single nucleotide triplet. 2M. Specht, M. Stanke, and M. Hippler, manuscript in preparation. In addition, GPF is now much faster due to the use of an indexing strategy that, although requiring a correctly predicted sequence of three amino acids within one exon, achieves a 300-fold speed increase compared with the previous GPF. In addition, the N- and C-terminal masses of the correctly predicted amino acid trimer must be correct within a certain mass accuracy. Finally, all GPF alignments are filtered in such a way that a correctly predicted amino acid pentamer must be present in the final (possibly spliced) alignment. Combining putative GPF peptides with a target/decoy approach for the protein database posed a challenge because it is not known how many of the putative GPF peptides are already false positives, which means that if decoys were added to an unknown mixture of true and false positives, a correct target FPR could not be estimated. This problem was solved in the following way: GPF peptides and gene model proteins were combined into a single mixed database, and decoys were only created for gene model proteins. Then OMSSA was run on this combined database to yield comparable E-values, and the target FPR estimation was performed on the target and decoy entries from the protein database alone. The resulting E-value threshold was then applied to all identified peptides, and all remaining decoy identifications were discarded. Because of this setup, we were able to identify putative GPF peptides at a predefined estimated target FPR. From a total of 12,216 model peptides, 49.5% of the peptides could be independently identified by PEAKS/GPF. A protein was considered for localization only if it was identified with at least two distinct peptides or at least one GPF-confirmed peptide having at least two spectral counts because the PEAKS/GPF identification can be regarded as an independent verification of the database identification (16.Allmer J. Naumann B. Markert C. Zhang M. Hippler M. Mass spectrometric genomic data mining: novel insights into bioenergetic pathways in Chlamydomonas reinhardtii.Proteomics. 2006; 6: 6207-6220Crossref PubMed Scopus (50) Google Scholar). In our chloroplast data set, 142 proteins were additionally included in the chloroplast proteome after GPF confirmation. To check whether a transit peptide consensus sequence can be predicted from the mass spectrometric data, OMSSA was used to search for semitryptic peptides. The parameters used for the search were the same as described above except that enzymatic cleavage was set to "semitryptic." Due to the tryptic digestion of the protein mixtures, we would expect fully tryptic peptides in the sample. However, if a semitryptic peptide can be identified in the sample and the non-tryptic cleavage site is located at the N terminus, we can assume that the cleavage might have happened not due to the tryptic digestion but due to a transit peptide having been cleaved off the protein. From all significantly identified, proteotypic semitryptic peptides, those that were not part of the exper