A Comparative Proteomic Strategy for Subcellular Proteome … · 2015-08-07 · A Comparative...

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A Comparative Proteomic Strategy for Subcellular Proteome Research ICAT APPROACH COUPLED WITH BIOINFORMATICS PREDICTION TO ASCERTAIN RAT LIVER MITOCHONDRIAL PROTEINS AND INDICATION OF MITOCHONDRIAL LOCALIZATION FOR CATALASE*S Xiao-Sheng Jiang, Jie Dai, Quan-Hu Sheng, Lei Zhang, Qi-Chang Xia, Jia-Rui Wu, and Rong Zeng‡ Subcellular proteomics, as an important step to functional proteomics, has been a focus in proteomic research. However, the co-purification of “contaminating” proteins has been the major problem in all the subcellular pro- teomic research including all kinds of mitochondrial pro- teome research. It is often difficult to conclude whether these “contaminants” represent true endogenous part- ners or artificial associations induced by cell disruption or incomplete purification. To solve such a problem, we ap- plied a high-throughput comparative proteome experimen- tal strategy, ICAT approach performed with two-dimen- sional LC-MS/MS analysis, coupled with combinational usage of different bioinformatics tools, to study the pro- teome of rat liver mitochondria prepared with traditional centrifugation (CM) or further purified with a Nycodenz gradient (PM). A total of 169 proteins were identified and quantified convincingly in the ICAT analysis, in which 90 proteins have an ICAT ratio of PM:CM >1.0, while another 79 proteins have an ICAT ratio of PM:CM <1.0. Almost all the proteins annotated as mitochondrial according to Swiss-Prot annotation, bioinformatics prediction, and lit- erature reports have a ratio of PM:CM >1.0, while proteins annotated as extracellular or secreted, cytoplasmic, en- doplasmic reticulum, ribosomal, and so on have a ratio of PM:CM <1.0. Catalase and AP endonuclease 1, which have been known as peroxisomal and nuclear, respec- tively, have shown a ratio of PM:CM >1.0, confirming the reports about their mitochondrial location. Moreover, the 125 proteins with subcellular location annotation have been used as a testing dataset to evaluate the efficiency for ascertaining mitochondrial proteins by ICAT analysis and the bioinformatics tools such as PSORT, TargetP, SubLoc, MitoProt, and Predotar. The results indicated that ICAT analysis coupled with combinational usage of different bioinformatics tools could effectively ascertain mitochondrial proteins and distinguish contaminant pro- teins and even multilocation proteins. Using such a strat- egy, many novel proteins, known proteins without subcel- lular location annotation, and even known proteins that have been annotated as other locations have been strongly indicated for their mitochondrial location. Molecular & Cellular Proteomics 4:12–34, 2005. There has been a tendency to focus on subcellular pro- teomes concerning specific subcellular compartments and macromolecular structures of the cell (1– 6). The separation of the protein mixture into organelles or other multiprotein com- plex fractions prior to a proteomics analysis can increase the probability of detecting low-copynumber proteins. Subcellular proteome research cannot only provide information about subcellular location of certain proteins and imply their func- tion, but also tell us the whole-protein components of the specific subcellular fractions (organelles or other multiprotein complexes) and then help understand their structures and biological functions (1– 6). Mitochondria are ubiquitous organelles responsible for the energy metabolism of eukaryotic cells. They are best known for housing the oxidative phosphorylation machinery as well as enzymes needed for free fatty acid metabolism and the Kreb’s cycle. Key steps of heme biosynthesis, ketone body generation, and hormone synthesis also reside within this organelle (7). More recent studies suggest an additional role of the mitochondrion in ionic homeostasis, apoptosis, and aging (8 –13). Consequently, many diseases have been attributed to mitochondrial defects, including Alzheimer’s disease, Parkin- son’s disease, Friedreich ataxia, diabetes mellitus, malignant tumors, cardiovascular disease, and osteoarthritis (14 –24). These findings have promoted increasing efforts to define the mitochondrial proteome and to discover new molecular tar- gets for drug development and therapeutic intervention (7, 20, 25–33). Mass spectrometric methods and automation of a large part of the process including robotics application have con- tinued to improve dramatically in recent years, allowing both increased sensitivity and higher throughput. Improved soft- ware and databases containing different species genes— known or putative—are also now available, allowing auto- mated data processing of the large volume of acquired mass From the Research Centre for Proteome Analysis, Key Lab of Proteomics, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Graduate School of the Chinese Academy of Sciences, Shanghai 200031, China Received, July 1, 2003, and in revised form, October 18, 2004 Published, MCP Papers in Press, October 25, 2004, DOI 10.1074/mcp.M400079-MCP200 Research © 2005 by The American Society for Biochemistry and Molecular Biology, Inc. 12 Molecular & Cellular Proteomics 4.1 This paper is available on line at http://www.mcponline.org

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A Comparative Proteomic Strategy forSubcellular Proteome ResearchICAT APPROACH COUPLED WITH BIOINFORMATICS PREDICTION TO ASCERTAIN RAT LIVER MITOCHONDRIALPROTEINS AND INDICATION OF MITOCHONDRIAL LOCALIZATION FOR CATALASE*□S

Xiao-Sheng Jiang, Jie Dai, Quan-Hu Sheng, Lei Zhang, Qi-Chang Xia, Jia-Rui Wu,and Rong Zeng‡

Subcellular proteomics, as an important step to functionalproteomics, has been a focus in proteomic research.However, the co-purification of “contaminating” proteinshas been the major problem in all the subcellular pro-teomic research including all kinds of mitochondrial pro-teome research. It is often difficult to conclude whetherthese “contaminants” represent true endogenous part-ners or artificial associations induced by cell disruption orincomplete purification. To solve such a problem, we ap-plied a high-throughput comparative proteome experimen-tal strategy, ICAT approach performed with two-dimen-sional LC-MS/MS analysis, coupled with combinationalusage of different bioinformatics tools, to study the pro-teome of rat liver mitochondria prepared with traditionalcentrifugation (CM) or further purified with a Nycodenzgradient (PM). A total of 169 proteins were identified andquantified convincingly in the ICAT analysis, in which 90proteins have an ICAT ratio of PM:CM >1.0, while another79 proteins have an ICAT ratio of PM:CM <1.0. Almost allthe proteins annotated as mitochondrial according toSwiss-Prot annotation, bioinformatics prediction, and lit-erature reports have a ratio of PM:CM >1.0, while proteinsannotated as extracellular or secreted, cytoplasmic, en-doplasmic reticulum, ribosomal, and so on have a ratio ofPM:CM <1.0. Catalase and AP endonuclease 1, whichhave been known as peroxisomal and nuclear, respec-tively, have shown a ratio of PM:CM >1.0, confirming thereports about their mitochondrial location. Moreover, the125 proteins with subcellular location annotation havebeen used as a testing dataset to evaluate the efficiencyfor ascertaining mitochondrial proteins by ICAT analysisand the bioinformatics tools such as PSORT, TargetP,SubLoc, MitoProt, and Predotar. The results indicatedthat ICAT analysis coupled with combinational usage ofdifferent bioinformatics tools could effectively ascertainmitochondrial proteins and distinguish contaminant pro-teins and even multilocation proteins. Using such a strat-

egy, many novel proteins, known proteins without subcel-lular location annotation, and even known proteins thathave been annotated as other locations have beenstrongly indicated for their mitochondrial location.Molecular & Cellular Proteomics 4:12–34, 2005.

There has been a tendency to focus on subcellular pro-teomes concerning specific subcellular compartments andmacromolecular structures of the cell (1–6). The separation ofthe protein mixture into organelles or other multiprotein com-plex fractions prior to a proteomics analysis can increase theprobability of detecting low-copynumber proteins. Subcellularproteome research cannot only provide information aboutsubcellular location of certain proteins and imply their func-tion, but also tell us the whole-protein components of thespecific subcellular fractions (organelles or other multiproteincomplexes) and then help understand their structures andbiological functions (1–6).

Mitochondria are ubiquitous organelles responsible for theenergy metabolism of eukaryotic cells. They are best knownfor housing the oxidative phosphorylation machinery as wellas enzymes needed for free fatty acid metabolism and theKreb’s cycle. Key steps of heme biosynthesis, ketone bodygeneration, and hormone synthesis also reside within thisorganelle (7). More recent studies suggest an additional role ofthe mitochondrion in ionic homeostasis, apoptosis, and aging(8–13). Consequently, many diseases have been attributed tomitochondrial defects, including Alzheimer’s disease, Parkin-son’s disease, Friedreich ataxia, diabetes mellitus, malignanttumors, cardiovascular disease, and osteoarthritis (14–24).These findings have promoted increasing efforts to define themitochondrial proteome and to discover new molecular tar-gets for drug development and therapeutic intervention (7, 20,25–33).

Mass spectrometric methods and automation of a largepart of the process including robotics application have con-tinued to improve dramatically in recent years, allowing bothincreased sensitivity and higher throughput. Improved soft-ware and databases containing different species genes—known or putative—are also now available, allowing auto-mated data processing of the large volume of acquired mass

From the Research Centre for Proteome Analysis, Key Lab ofProteomics, Institute of Biochemistry and Cell Biology, ShanghaiInstitutes for Biological Sciences, Chinese Academy of Sciences,Graduate School of the Chinese Academy of Sciences, Shanghai200031, China

Received, July 1, 2003, and in revised form, October 18, 2004Published, MCP Papers in Press, October 25, 2004, DOI

10.1074/mcp.M400079-MCP200

Research

© 2005 by The American Society for Biochemistry and Molecular Biology, Inc.12 Molecular & Cellular Proteomics 4.1This paper is available on line at http://www.mcponline.org

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spectra. As a result, many largest subcellular proteome data-bases (1–6, 34), especially for mitochondria proteome, havebeen set (7, 25–32). For example, the largest two-dimensional(2D)1-PAGE map database by far for rat liver mitochondriacontains 192 individual proteins from 1,800 spots (25) and foryeast mitochondria contains 253 individual proteins from 459spots (26). On the other hand, the largest shotgun proteomedatabases by far have been set for human heart mitochondriawith 615 individual proteins (27), for mouse mitochondria with591 individual proteins (7), for Saccharomyces cerevisiae mi-tochondria with 750 different proteins (28), and for rat livermitochondria with 227 unique rat proteins (29).

In this new context, the perfect purity of intact proteincomplexes has been crucial to subcellular proteome research(35, 36). In addition to conventional differential centrifugation(25), many further purification methods such as density gra-dient centrifugation (7, 26–29, 37), immunoisolation (37), andfree-flow electrophoresis (38) have been applied to subcellularproteome research and have shown improved effects in iden-tification of more specific subcellular proteins (29, 37, 38).However, the co-purification of “contaminating” proteins isstill the major problem in all the subcellular proteome re-search. For those largest mitochondrial proteome databases,only 33.3�62.8% of the identified proteins have been anno-tated as mitochondrial proteins, while 14.1�43.2% of themhave been annotated as other organelle proteins and9.9�33.9% of them have no subcellular location annotation(7, 25–29). Of the 192 proteins in the 2D-PAGE database forthe rat liver mitochondrial fraction prepared by differentialcentrifugation, only 64 (33.3%) proteins have been annotatedas mitochondrial proteins, while 83 (43.2%) proteins havebeen annotated as other organelle proteins and 45 (23.4%)proteins have no subcellular location annotation in Swiss-Protdatabase (25). Of the 253 proteins in the 2D-PAGE databasefor the S. cerevisiae mitochondrial fraction purified with athree-step sucrose gradient, 159 (62.8%) proteins have beenannotated as mitochondrial proteins, while 69 (27.2%) pro-teins have been annotated as other organelle proteins and 25(9.9%) proteins have no subcellular location annotation (26).In the 591 proteins identified in mouse mitochondria purifiedwith a Percoll gradient, 163 proteins (27.6%) have not previ-ously annotated as associated with mitochondria (7). Amongthe 750 proteins identified in S. cerevisiae mitochondria puri-fied with a three-step sucrose gradient, 436 (58.1%) proteinsare known mitochondrial proteins, while a total of 208 (27.7%)proteins have not been localized so far, and 106 (14.1%)proteins have been reported to be located in other cellularcompartments (28). For the 227 rat proteins identified from therat liver mitochondria purified with a Nycodenz gradient, 80

(35.2%) have been annotated as mitochondrial proteins inSwiss-Prot database, while 70 (30.8%) proteins have beenannotated as other organelle proteins and 77 (33.9%) proteinshave no subcellular location annotation (29). Even though theperoxisome purified with a Nycodenz gradient were furtherimmunoisolated, many mitochondrial and endoplasmic retic-ulum proteins have been detected (37).

It is often difficult to conclude whether these “contami-nants” represent true endogenous partners or artificial asso-ciations induced by cell disruption or incomplete purification(35). At the same time, the novel proteins and proteins withoutsubcellular location annotation need more evidence for theirsubcellular location. Experimental determination of subcellu-lar location is mainly accomplished by three approaches: cellfractionation, electron microscopy, and fluorescence micros-copy. As currently practiced, these approaches are time con-suming, subjective, and highly variable (39). With experimen-tally verified information on protein subcellular locationlagging far behind, a series of bioinformatics tools such asPSORT (40, 41), TargetP (40, 42), SubLoc (43), MitoProtII (44),and Predotar (www.inra.fr/Internet/Produits/Predotar/) havebeen developed and widely used in many subcellular pro-teome data (25, 26, 28–32). PSORT was developed as anexpert system that uses a set of 100 “if-then”-type rulesbased on analysis of characterized protein sequences from avariety of subcellular locations (40, 41). TargetP, based onneural network programming, was developed to predict tar-geting of protein sequences to chloroplasts, mitochondria,and the secretory system using a knowledgebase derivedfrom Swiss-Prot sequence entries (40, 42). SubLoc is a pre-diction system for protein subcellular localization based onamino acid composition alone using a Support Vector Ma-chine method (43). MitoProt was developed to predict mito-chondrial targeting and presequence cleavage sites based ona set of 47 known characteristics of presequences and cleav-age sites (44). Predotar is particularly good at distinguishingmitochondrial and plastid targeting sequences and recog-nizes the N-terminal targeting sequences of classically tar-geted mitochondrial and chloroplast precursor proteins. How-ever, many problems are involved in the prediction (45), and itis questionable whether the efficiency still holds when appliedto proteome data (46). Using an actual subcellular proteomedataset, we have shown that the sensitivity and specificity ofPSORT and TargetP have been overevaluated previously. Butinterestingly, the combinational usage of TargetP and PSORThas a high specificity up to 0.86 for mitochondrial proteinprediction (29).

More recently, Dunkley et al. has just discussed that the useof comparative proteomics to analyze the relative levels ofproteins in different organelle-enriched fractions can solutethe problem of contaminants and distinguish between pro-teins from different subcellular compartments without theneed to obtain pure organelles (36). Implicated from the factthat further purified mitochondrial fractions enriched mito-

1 The abbreviations used are: 2D, two-dimensional; CM, crudemitochondria; PM, purified mitochondria; TM, transmembrane; RP,reversed phase; TCA, tricarboxylic acid; SILAC, stable isotope label-ing with amino acids in cell culture.

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chondrial proteins and decreased apparent contaminant pro-teins compared with crude mitochondrial fraction (29, 38), wethink that comparative proteomic research on the crude andpurified subcellular fraction would be in favor of ascertain-ment of the specific subcellular proteins and exclusion of thecontaminants. In the present study, we apply ICAT technol-ogy, a key strategy in comparative proteomic research (47–49), to compare the crude mitochondrial (CM) fraction of ratliver prepared with differential centrifugation with the purifiedmitochondrial (PM) fraction further purified with a Nycodenzgradient. After 2D-LC-MS/MS analysis, a total of 169 proteinswere identified and quantified. The ICAT data were evaluatedaccording to Swiss-Prot annotation and prediction of fivebioinformatics tools such as PSORT, TargetP, SubLoc, Mito-Prot, and Predotar. The prediction efficiency for mitochondrialprotein of the five bioinformatics tools was compared. Theresults indicated that ICAT analysis coupled with combina-tional usage of different bioinformatics tools could effectivelyascertain mitochondrial proteins and distinguish contaminantproteins and even multilocation proteins. Using such a strat-egy, many novel proteins, known proteins without subcellularlocation annotation, and even known proteins that have beenannotated as other locations have been strongly indicated fortheir mitochondrial location.

EXPERIMENTAL PROCEDURES

Materials—Analytical reagent-grade chemicals were used through-out unless otherwise stated. Water was prepared using a Milli-Qsystem (Millipore, Bedford, MA). Nycodenz, formic acid, guanidinehydrochloride, sodium orthovanadate (Na3VO4), and sodium fluoride(NaF) were obtained from Sigma (St. Louis, MO). Chemicals employedfor gel electrophoresis were purchased from Bio-Rad (Hercules, CA).ACN with HPLC grade was obtained from Fisher (Fair Lawn, NJ).Trypsin sequencing grade was obtained from Promega (Southamp-ton, United Kingdom). EDTA, EGTA, and PMSF were purchased fromAmresco (Solon, OH). Adult male Sprague-Dawley rats were pur-chased from Shanghai Laboratory Animal Center (Jiu-Ting, Shanghai,China).

Differential Centrifugation Separation of Rat Liver Subcellular Frac-tions—Subcellular fractionation of rat liver was performed as de-scribed previously (29). Briefly, Sprague-Dawley rats were sacrificedand the livers were promptly removed and placed in ice-cold homog-enization buffer consisting of 200 mM mannitol, 50 mM sucrose, 1 mM

EDTA, 0.5 mM EGTA, and a mixture of protease inhibitor (1 mM PMSF)and phosphatase inhibitors (0.2 mM Na3VO4, 1 mM NaF) and 10 mM

Tris-HCl at pH 7.4. After mincing with scissors and washing to removeblood, the livers were homogenized in a Potter-Elvejhem homoge-nizer with a Teflon piston, using 10 ml of the homogenization bufferper 2 g of tissue. Centrifugation at successively higher speeds at 4 °Cyielded the following fractions: crude nuclear fraction at 1,000 � g for10 min; mitochondria at 15,000 � g for 15 min; and microsomes at144,000 � g for 90 min. The final supernatant was the cytosol frac-tion. Each successive pellet was washed three times with the homog-enization buffer. The centrifuges used were the Himac CR 21G high-speed refrigerated centrifuge and Himac CP 80MX preparativeultracentrifuge, both from Hitachi Koki Co. Ltd. (Tokyo, Japan).

Purification of Rat Liver Mitochondria Through Nycodenz DensityGradient Centrifugation—The procedures recommended by Ny-comed Pharma and Invitrogen Life Technologies were followed as

described previously (29). Nycodenz was dissolved to 50% (w/v) in 5mM Tris-HCl, pH 7.4, containing 1 mM EDTA, 0.5 mM EGTA, and amixture of protease inhibitor and phosphatase inhibitors as above.This stock solution was diluted with buffer containing 0.25 M sucrose,5 mM Tris-HCl, 1 mM EDTA, 0.5 mM EGTA, and a mixture of proteaseinhibitor and phosphatase inhibitors at pH 7.4. The crude mitochon-drial pellets obtained from differential centrifugation were suspendedin 12 ml of 25% nycodenz and placed on the following discontinuousnycodenz gradients: 5 ml of 34% and 8 ml of 30%, and this wastopped off with 8 ml of 23% and finally 3 ml of 20%. The sealed tubeswere centrifuged for 90 min at 52,000 � g at 4 °C. The bands ofparticles seen after centrifugation have been identified by NycomedPharma and Invitrogen Life Technologies as follows: nuclei at the40/50% interface; peroxisomes at the 34/40% interface; mitochon-dria at the 25/30% interface, lysosomes at the 15/20% interface, andGolgi membranes at the 10/15% interface. The band at the 25/30%interface was collected and diluted with the same volume homoge-nization buffer and then centrifuged at 15,000 � g for 20 min.

Protein Preparation—The mitochondria pellets from differentialcentrifugation (CM) and nycodenz density gradient purification (PM)were respectively suspended in lysis buffer consisting of 8 M urea, 4%CHAPS, 65 mM DTT, 40 mM Tris, sonicated at 100 W for 30 s, andcentrifuged at 25,000 � g for 1 h. The supernatants were collected asCM and PM fractions. The protein concentration was determined bythe Bradford assay. Then the protein samples were directly used for2D-PAGE or ICAT analysis after another precipitation andredissolving.

ICAT Analysis—ICAT analysis was performed using CleavableICATTM Reagent Kit (Applied Biosystems, Foster City, CA) accordingto the manufacturer’s guidelines with some modifications. For ICATanalysis, the protein samples were precipitated overnight with 5�volumes of �20 °C 50:50:0.1 volumes of ethanol:acetone:acetic acidand resolubilized in denaturing buffer (6 M guanidine hydrochloride,100 mM TrisCl, pH 8.3). One hundred micrograms of the CM or PMprotein sample in 80 �l of denaturing buffer were reduced at 37 °C for2 h with 5 mM tributylphosphine (Bio-Rad) and alkylated at 37 °C for2 h in the dark with ICAT-light and ICAT-heavy reagent, respectively.After reaction, ICAT-light and ICAT-heavy reactants were mixed to-gether and exchanged into 100 mM ammonium bicarbonate, pH 8.5,with ultrafiltration through 3-kDa Microcon Centrifugal Filter Devices(Millipore). The buffer-exchanged sample was digested with 4 �g oftrypsin (50:1) at 37 °C for 20 h. Then the ICAT-labeled peptides werepurified using the kit of ICAT™ Avidin Buffer Pack and Avidin AffinityCartridge (Applied Biosystems) according to the manufacturer’sguidelines. Briefly, the peptide mixture was dried by vacuum centri-fuge and resolubilized in the loading buffer of the kit. The peptidemixture was loaded in the Avidin Affinity Cartridge and washed twicewith two kinds of wash buffer to reduce the salt concentration andremove nonspecifically bound peptides. Then the ICAT-labeled pep-tides were eluted with the elution buffer and dried by vacuum centri-fuge. The dried peptides were cleaved of the biotin portion of the ICATreagent with the cleaving reagents at 37 °C for 2 h. Then the ICAT-labeled peptides were dried by vacuum centrifuge and resolubilized in0.1% formic acid for 2D-LC-MS/MS analysis.

2D-LC-MS/MS—Orthogonal 2D-LC-MS/MS was performed usinga ProteomeX Work station (Thermo Finnigan, San Jose, CA). Thesystem was fitted with a strong cation exchange column (320 �minner diameter � 100 mm, DEV SCX; Thermo Hypersil-Keystone) andtwo C18 reversed-phase columns (RP, 180 �m � 100 mm, Bio-Basic® C18, 5 �m; Thermo Hypersil-Keystone). The salt steps usedwere 0, 25, 50, 75, 100, 150, 200, 400, and 800 mM NH4Cl synchro-nized with nine 140-min RP gradients. RP solvents were 0.1% formicacid in either water (A) or ACN (B). The setting of the LCQ Deca Xplusion-trap mass spectrometer is as following. One full MS scan was

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followed by three MS/MS scans on the three most intense ions fromthe MS spectrum according to such a dynamic exclusion setting:repeat count, 1; repeat duration, 0.5 min; exclusion duration, 3.0 min.

Database Searches—The acquired MS/MS spectra were automat-ically searched against the combined human, mouse, and rat nonre-dundant database (NCBI (www.ncbi.nlm.nih.gov), 12/04/2003 re-leased) using the TurboSEQUEST program in the BioWorks™ 3.1software suite. An accepted SEQUEST result had to have a �Cnscore of at least 0.1 (regardless of charge state). For ICAT analysis,protein identification and quantification was achieved by using SE-QUEST and EXPRESS software tools. Peptides with a �1 chargestate were accepted if they were fully tryptic and had a cross corre-lation (Xcorr) of at least 1.5. Peptides with a �2 charge state wereaccepted if they had an Xcorr �2.0. Peptides with a �3 charge statewere accepted if they had an Xcorr �2.5. Then the peptides werefurther analyzed manually by detailed spectral analysis for confirma-tion of protein identification and quantification as described by Han etal. (48).

Bioinformatics Annotation Tools—The theoretical pI and Mr valuesof proteins were defined by program pepstats (www.hgmp.mrc.ac.uk/Software/EMBOSS). The protein function and subcellular locationannotation was from Swiss-Prot and TrEMBL protein database(us.expasy.org/sprot/). The bioinformatics tools such as PSORT(psort.nibb.ac.jp/form2.html) (40, 41), TargetP (www.cbs.dtu.dk/ser-vices/TargetP/) (40, 42), SubLoc (www.bioinfo.tsinghua.edu.cn/Sub-Loc/) (43), MitoProtII (www.mips.biochem.mpg.de/cgi-bin/proj/med-gen/mitofilter) (44), and Predotar (www.inra.fr/Internet/Produits/Predotar/) have been used to predict protein subcellular location. TheTMHMM (www.cbs.dtu.dk/services/TMHMM/) (50) was used to pre-dict protein transmembrane domains. GRAVY values were deter-mined according to Kyte-Doolittle (51). SIB BLAST2 Network Service(us.expasy.org/tools/blast/) was used for novel protein blast againstthe UniProt knowledgebase (Swiss-Prot � TrEMBL � TrEMBL_NEW).

RESULTS

Proteins Identification and Quantification

Rat liver CM were prepared using conventional differentialcentrifugation and were further purified with a Nycodenz gra-dient to obtain PM. One hundred micrograms of the CM or PMprotein sample were labeled with ICAT-light and ICAT-heavyreagent, respectively. After 2D-LC-MS/MS analysis, theMS/MS spectra were searched against the combined human,mouse, and rat nonredundant database using the programSEQUEST. Protein identification and quantification wasachieved by using SEQUEST and EXPRESS software tools(see “Experimental Procedures” for details).

A total of 169 different proteins were identified and quanti-fied from 755 cysteine-containing peptides (253 unique pep-tides) including 398 �2 charge peptides with Xcorr �2.0, and355 �3 charge peptides with Xcorr �2.5. All the peptideshave a �Cn score of at least 0.1 (regardless of charge state).Only two peptides with a �1 charge were identified, with anXcorr of 1.58 and 1.99, respectively.

For the 169 proteins, 69.8% (118/169) proteins were iden-tified and quantitated on the basis of at least one �2 chargepeptide with Xcorr �2.5 and �Cn �0.15 or �3 charge peptidewith Xcorr �3.0 and �Cn �0.15. For 127 (75.1%, 127/169)proteins identified and quantitated according to one uniquepeptide that could have two ICAT reagent-labeling states,

ICAT-light reagent labeling or ICAT-heavy reagent labeling,62.2% (79/127) of them were identified and quantitated on thebasis of at least one �2 charge peptide with Xcorr �2.5 and�Cn �0.15 or �3 charge peptide with Xcorr �3.0 and �Cn�0.15 (Supplemental Table I).

According to physicochemical characteristics analysis, the169 proteins (Table I) include 22 (13.0%) proteins with molec-ular mass �100 kDa, 22 (13.0%) proteins with pI value �9.0,23 (13.6%) hydrophobic proteins with GRAVY value �0, and20 (11.8%) proteins with one or more predicted transmem-brane (TM) domain (Fig. 1), which indicate the ICAT analysisperformed by 2D-LC-MS/MS have little limitations for identi-fication of proteins with extreme size and charge values, andeven hydrophobic and membrane proteins, which is consist-ent with our former 2D-LC-MS/MS analysis of rat liver sub-cellular fractions (29).

The relative quantification of each peptide was determinedby the ratio of signal intensities of peptide pairs using theExpress software tool (see Ref. 58). Ninety proteins have aratio of PM:CM �1.0 with 45.6% (41/90) of them with a ratio �

2.0, which indicates that those proteins are enriched in the PMfraction and should be mitochondrial proteins. Seventy-nineproteins have a ratio of PM:CM �1.0 with 48.1% (38/79) ofthem with a ratio � 0.5 (–2.0), which indicates that they aredecreased in the PM fraction and should be contaminantproteins. Only 12 (7.1%) proteins have a ratio of PM:CMbetween 0.83 (–1.2) and 1.2, in which four protein have a ratioof PM:CM �1.0 and eight proteins have a ratio of PM:CM�1.0 (Fig. 1). Interestingly, there is no apparent differenceamong the percentage of mitochondrial proteins for proteinswith a ratio of PM:CM �1.0, �1.2, �1.5, and �2.0, or thepercentage of nonmitochondrial proteins for proteins with aratio of PM:CM �1.0, �0.83, �0.67, and �0.50, based on theSwiss-Prot annotation or five bioinformatics prediction results(Fig. 2). Considering that the protein or peptide abundancemight influence many steps in the ICAT analysis such as ICATreagent labeling, peptide enrichment and elution in the AvidinAffinity Cartridge, peptide separation in 2D-LC, and peptideionization and detection by MS, the ratio of PM:CM mightreflect synthetically the mitochondria purification effect, pro-tein abundance, and multilocation influence. The relative lowratio such as PM:CM of 1.0�1.5 might implicate proteins withhigh abundance or mitochondria-associated multilocation,while the relative high ratio such as PM:CM �4.0 or moremight implicate proteins with low abundance. Thus, the 169proteins were classified into two groups of PM:CM �1.0 andPM:CM �1.0 in the following analysis.

Subcellular Location of the Identified Proteins

Swiss-Prot Annotation—As shown in Table I and Fig. 3,about 71.0% (120/169) of the 169 proteins have subcellularlocation annotation in Swiss-Prot database. For the 79 pro-teins with a ratio of PM:CM �1.0, in addition to 20 (25.3%)

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riali

nner

mem

bra

ne;

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S,

mito

chon

dria

lint

erm

emb

rane

spac

e;M

OM

,m

itoch

ond

rialo

uter

mem

bra

ne;

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nucl

ear;

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othe

r;P

,p

erox

isom

al;

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ribos

omal

;S

,se

cret

ed;

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who

llyin

trac

ellu

lar.

PS

OR

TII

and

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Loc

are

used

inth

ew

inne

r-ta

kes-

all

mod

ew

ithou

tse

ttin

ga

spec

ifici

tycu

t-of

ffo

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ing.

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etP

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itoP

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dP

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asm

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rmat

ics

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ins

pre

dic

ted

asm

itoch

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by

diff

eren

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ioin

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atic

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ols

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atth

esa

me

time.

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icat

edth

ata

pro

tein

wer

ep

red

icte

das

am

itoch

ond

rialp

rote

inb

yno

neor

allo

fth

efiv

eb

ioin

form

atic

sto

ols,

resp

ectiv

ely.

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,no

anno

tatio

n.S

P,

sub

cellu

lar

loca

tion

anno

tatio

nac

cord

ing

toS

wis

s-P

rot

orlit

erat

ure

rep

ort

(mar

ked

with

Ref

.no

.).

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tein

acce

ssio

nno

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rote

inna

me

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(kD

a)p

IG

RA

VY

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dH

elR

atio

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)(C

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M)

nuP

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PS

OR

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ubLo

cTa

rget

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dot

arB

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nctio

nal

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atio

n

Non

mito

chon

dria

lpro

tein

sC

ytop

lasm

icO

0917

1B

etai

ne—

hom

ocys

tein

eS

-met

hyltr

ansf

eras

e44

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524

1C

CM

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135

0.63

70.

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min

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olis

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old

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ain

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e

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cera

ldeh

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se(G

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)

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coly

sis

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ase

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mp

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unit

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00

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ecul

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one

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ngat

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118

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slat

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or

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smic

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ulum

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tera

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CC

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tty

ald

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ed

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87.

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CC

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00.

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e

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rans

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eras

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lop

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003

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yme

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ated

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0)

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ecul

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tein

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ulfid

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ase

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I)56

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isul

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mily

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tein

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ulfid

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001

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rote

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isul

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roso

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sfer

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tein

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rge

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unit

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MS

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spor

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orch

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race

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ror

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ogen

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004

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agul

atio

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ICAT Analysis of Rat Liver Mitochondria with Different Purity

16 Molecular & Cellular Proteomics 4.1

Page 6: A Comparative Proteomic Strategy for Subcellular Proteome … · 2015-08-07 · A Comparative Proteomic Strategy for Subcellular Proteome Research ... erature reports have a ratio

TAB

LEI—

cont

inue

d

Pro

tein

acce

ssio

nno

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rote

inna

me

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(kD

a)p

IG

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dH

elR

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M)

nuP

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OR

TS

ubLo

cTa

rget

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dot

arB

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nal

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sific

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thro

mb

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681

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487

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od coag

ulat

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ysis

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ha-1

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001

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tera

ctio

nsb

etw

een

cells

and

the

extr

acel

lula

rm

atrix

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fate

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rote

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rosa

pos

in)

61.1

25.

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0.

315

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0.03

60.

128

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31

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ract

ions

bet

wee

nce

llsan

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eex

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ellu

lar

mat

rixP

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nect

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N)

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11

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344

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ract

ion

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nce

llsan

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lar

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rixQ

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rom

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inhi

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spor

ter

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ter

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ICAT Analysis of Rat Liver Mitochondria with Different Purity

Molecular & Cellular Proteomics 4.1 17

Page 7: A Comparative Proteomic Strategy for Subcellular Proteome … · 2015-08-07 · A Comparative Proteomic Strategy for Subcellular Proteome Research ... erature reports have a ratio

TAB

LEI—

cont

inue

d

Pro

tein

acce

ssio

nno

.P

rote

inna

me

Mr

(kD

a)p

IG

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VY

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elR

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)(C

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M)

nuP

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OR

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ubLo

cTa

rget

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TPM

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rot

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dot

arB

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nctio

nal

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sific

atio

n

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276

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min

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ing

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curs

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spor

ter

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rote

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ICAT Analysis of Rat Liver Mitochondria with Different Purity

18 Molecular & Cellular Proteomics 4.1

Page 8: A Comparative Proteomic Strategy for Subcellular Proteome … · 2015-08-07 · A Comparative Proteomic Strategy for Subcellular Proteome Research ... erature reports have a ratio

TAB

LEI—

cont

inue

d

Pro

tein

acce

ssio

nno

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rote

inna

me

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(kD

a)p

IG

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arB

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nal

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n

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0.27

30.

212

0.00

21

Intr

acel

lula

rtr

affic

king

Cyt

oske

leta

lQ

9ER

D7

Tub

ulin

�-3

50.4

24.

82–0

.371

01:

0.76

21

CS

KC

SK

CO

0.08

80.

043

0.00

00

Cyt

oske

leta

l

Gol

gico

mp

lex

Q62

638

Ggi

app

arat

usp

rote

in1

133.

566.

52–0

.48

11:

0.24

11

GM

eN

S0.

468

0.47

40.

982

1S

igna

ling

pro

tein

Pla

sma

mem

bra

neP

2645

3B

asig

in29

.59

5.12

–0.2

21

1:0.

424

1M

eG

CS

0.03

20.

033

0.01

40

Inte

ract

ions

bet

wee

nce

llsan

dth

eex

trac

ellu

lar

mat

rixM

itoch

ond

rial

pro

tein

sM

itoch

ond

rial

Q02

253

Met

hylm

alon

ate-

sem

iald

ehyd

ed

ehyd

roge

nase

(MM

SD

H)

57.8

18.

47–0

.048

11:

1.17

31

MM

MM

0.90

40.

942

0.79

05

Am

ino

acid

met

abol

ism

P23

434

Gly

cine

clea

vage

syst

emH

pro

tein

18.6

15.

36–0

.285

01:

1.38

31

MM

MM

0.89

50.

999

0.98

65

Am

ino

acid

met

abol

ism

O70

351

3-hy

dro

xyac

yl-C

oAd

ehyd

roge

nase

typ

eII

(Typ

eII

HA

DH

)

27.2

58.

910.

237

01:

1.05

11

MC

MM

0.63

10.

858

0.97

94

Enz

yme

P29

266

3-hy

dro

xyis

obut

yrat

ed

ehyd

roge

nase

(HIB

AD

H)

35.3

08.

730.

031

01:

2.07

31

MM

NM

0.94

30.

990

0.42

93

Enz

yme

P13

437

3-ke

toac

yl-C

oAth

iola

se( �

-ket

othi

olas

e)41

.87

8.09

–0.0

440

1:2.

182

1M

CM

M0.

594

0.53

90.

508

4Fa

tty

acid

�-

oxid

atio

nP

2279

1H

ydro

xym

ethy

lglu

tary

l-C

oAsy

ntha

se(H

MG

-CoA

synt

hase

)

56.9

18.

86–0

.36

01:

1.76

31

MM

MM

0.92

50.

952

0.99

45

Ket

one

bod

ym

etab

olis

m

P17

764

Ace

tyl-

CoA

acet

yltr

ansf

eras

e(A

CE

TOA

CE

TYL-

CO

ATH

IOLA

SE

)

44.6

98.

920.

086

01:

3.18

111

MC

MM

0.90

40.

994

0.92

94

Ket

one

bod

ym

etab

olis

m

P48

721

Str

ess-

70p

rote

in(G

RP

75)

(pep

tide-

bin

din

gp

rote

in74

)

73.8

65.

97–0

.424

01:

3.26

21

MM

MM

0.91

20.

939

0.98

95

Mol

ecul

arch

aper

one

Q9Y

4W6

AFG

3-lik

ep

rote

in2

(par

aple

gin-

like

pro

tein

)88

.48

8.87

–0.3

522

1:1.

682

1M

ER

CM

0.71

50.

858

0.96

23

Pro

teas

e

Q9J

HS

4A

TP-d

epen

den

tC

LPp

rote

ase

ATP

-bin

din

gsu

bun

itC

lpX

-lik

e

69.3

18.

07–0

.421

01:

1.81

11

MM

NM

0.55

80.

975

0.01

33

Pro

teas

e

P31

399

ATP

synt

hase

Dch

ain

18.7

66.

16–0

.718

01:

1.25

71

MC

NO

0.09

80.

501

0.17

91

The

oxid

ativ

ep

hosp

hory

latio

n(O

XP

HO

S)

ICAT Analysis of Rat Liver Mitochondria with Different Purity

Molecular & Cellular Proteomics 4.1 19

Page 9: A Comparative Proteomic Strategy for Subcellular Proteome … · 2015-08-07 · A Comparative Proteomic Strategy for Subcellular Proteome Research ... erature reports have a ratio

TAB

LEI—

cont

inue

d

Pro

tein

acce

ssio

nno

.P

rote

inna

me

Mr

(kD

a)p

IG

RA

VY

Pre

dH

elR

atio

(L:H

)(C

M:P

M)

nuP

SP

PS

OR

TS

ubLo

cTa

rget

Pm

TPM

itoP

rot

Pre

dot

arB

TFu

nctio

nal

clas

sific

atio

n

P35

435

ATP

synt

hase

�ch

ain

30.1

98.

87–0

.221

01:

1.73

71

MM

MO

0.44

50.

848

0.85

24

The

oxid

ativ

ep

hosp

hory

latio

n(O

XP

HO

S)

P30

042

ES

1p

rote

inho

mol

og(p

rote

inK

NP

-I)

28.1

48.

500.

030

1:4.

401

1M

MM

M0.

918

0.63

30.

895

5Tr

ansp

orte

ror

chan

nelp

rote

inP

1308

6S

ucci

nyl-

CoA

synt

heta

se�

chai

n(S

CS

-�)

35.0

39.

54–0

.092

01:

1.19

0.

039

2M

MM

M0.

866

0.84

40.

983

5TC

Acy

cle

Q9Z

219

Suc

ciny

l-C

oAsy

nthe

tase

,�

Ach

ain

(SC

S-�

A)

46.2

45.

65–0

.031

01:

1.72

11

MC

CO

0.29

90.

264

0.00

30

TCA

cycl

e

P54

071

Isoc

itrat

ed

ehyd

roge

nase

(IDH

)58

.75

8.89

–0.4

020

1:4.

251

1M

CM

O0.

303

0.01

20.

977

2TC

Acy

cle

P07

756

Car

bam

oyl-

pho

spha

tesy

nthe

tase

I(C

PS

AS

EI)

164.

586.

33–0

.117

01:

2.14

0.

4639

8M

CC

M0.

695

0.94

60.

862

3U

rea

cycl

e

Mito

chon

dria

linn

erm

emb

rane

P99

028

Ub

iqui

nol-

cyto

chro

me

cre

duc

tase

com

ple

x11

-kD

ap

rote

in(c

omp

lex

IIIsu

bun

itV

III)

10.4

34.

81–0

.987

01:

1.25

41

MIM

NN

O0.

063

0.00

30.

000

0Th

eox

idat

ive

pho

spho

ryla

tion

(OX

PH

OS

)

Q16

134

Ele

ctro

ntr

ansf

erfla

vop

rote

in-u

biq

uino

neox

idor

educ

tase

68.2

07.

33–0

.311

01:

1.62

0.

3713

3M

IMM

CO

0.36

00.

952

0.99

63

The

oxid

ativ

ep

hosp

hory

latio

n(O

XP

HO

S)

P52

504

NA

DH

-ub

iqui

none

oxid

ored

ucta

se13

-kD

a-A

sub

unit

(com

ple

xI-

13K

D-A

)

12.7

89.

37–0

.376

01:

1.89

31

MIM

MM

M0.

937

0.96

70.

999

5Th

eox

idat

ive

pho

spho

ryla

tion

(OX

PH

OS

)

P32

551

Ub

iqui

nol-

cyto

chro

me

cre

duc

tase

com

ple

xco

rep

rote

in2

(com

ple

xIII

sub

unit

II)

48.3

79.

16–0

.068

01:

1.85

31

MIM

CM

M0.

781

0.80

50.

999

4Th

eox

idat

ive

pho

spho

ryla

tion

(OX

PH

OS

)

Q9D

6J6

NA

DH

-ub

iqui

none

oxid

ored

ucta

se24

-kD

asu

bun

it

27.3

27.

00–0

.307

01:

3.76

11

MIM

MN

M0.

945

0.99

20.

998

4Th

eox

idat

ive

pho

spho

ryla

tion

(OX

PH

OS

)P

1626

1M

itoch

ond

rials

olut

eca

rrie

rp

rote

inho

mol

og33

.50

8.59

0.25

10

1:3.

751

1M

IMC

EX

S0.

021

0.74

30.

000

1Tr

ansp

orte

ror

chan

nelp

rote

inM

itoch

ond

rial

inte

rmem

bra

nesp

ace

Q07

116

Sul

fite

oxid

ase

54.3

55.

79–0

.408

01:

1.83

11

MIM

SC

EX

O0.

303

0.04

30.

013

0A

min

oac

idm

etab

olis

mQ

9JM

53P

rogr

amm

edce

lld

eath

pro

tein

8(a

pop

tosi

s-in

duc

ing

fact

or)

66.7

29.

06–0

.231

01:

1.31

41

MIM

SC

MM

0.74

60.

850

0.99

64

Ap

opto

sis

Q9D

0M3

Cyt

ochr

ome

c1(c

ytoc

hrom

ec-

1)35

.33

9.24

–0.2

471

1:1.

391

1M

IMS

MM

M0.

876

0.91

40.

704

5Th

eox

idat

ive

pho

spho

ryla

tion

(OX

PH

OS

)

Mito

chon

dria

lmat

rixP

1200

7Is

oval

eryl

-CoA

deh

ydro

gena

se(IV

D)

46.4

48.

03–0

.113

01:

1.02

11

MM

AM

MM

0.92

80.

994

0.99

35

Am

ino

acid

met

abol

ism

P00

507

Asp

arta

team

inot

rans

fera

se(T

rans

amin

ase

A)

47.3

19.

13–0

.23

01:

1.56

11

MM

AC

MM

0.81

80.

643

0.70

44

Am

ino

acid

met

abol

ism

ICAT Analysis of Rat Liver Mitochondria with Different Purity

20 Molecular & Cellular Proteomics 4.1

Page 10: A Comparative Proteomic Strategy for Subcellular Proteome … · 2015-08-07 · A Comparative Proteomic Strategy for Subcellular Proteome Research ... erature reports have a ratio

TAB

LEI—

cont

inue

d

Pro

tein

acce

ssio

nno

.P

rote

inna

me

Mr

(kD

a)p

IG

RA

VY

Pre

dH

elR

atio

(L:H

)(C

M:P

M)

nuP

SP

PS

OR

TS

ubLo

cTa

rget

Pm

TPM

itoP

rot

Pre

dot

arB

TFu

nctio

nal

clas

sific

atio

n

P97

519

Hyd

roxy

met

hylg

luta

ryl-

CoA

lyas

e(H

MG

-CO

ALY

AS

E)

34.1

98.

690.

198

01:

1.85

11

MM

AM

MM

0.88

40.

974

0.99

65

Am

ino

acid

met

abol

ism

P10

860

Glu

tam

ate

deh

ydro

gena

se(G

DH

)61

.43

8.05

–0.3

160

1:3.

031

1M

MA

CC

M0.

614

0.76

80.

823

3A

min

oac

idm

etab

olis

mP

2911

7P

eptid

yl-p

roly

lcis

-tra

nsis

omer

ase

(Cyc

lop

hilin

F)21

.81

9.30

–0.1

640

1:1.

69

0.25

32

MM

AC

CO

0.15

60.

088

0.16

80

Enz

yme

P53

395

Lip

oam

ide

acyl

tran

sfer

ase

com

pon

ent

ofb

ranc

hed

-ch

ain

�-k

eto

acid

deh

ydro

gena

seco

mp

lex

53.4

68.

71–0

.165

01:

2.02

0.

754

2M

MA

MM

M0.

932

0.99

80.

713

5E

nzym

e

P29

147

D- �

-hyd

roxy

but

yrat

ed

ehyd

roge

nase

(BD

H)

38.3

58.

93–0

.234

01:

2.19

0.

5619

4M

MA

CM

M0.

849

0.88

90.

824

4E

nzym

e

O08

749

Dih

ydro

lipoa

mid

ed

ehyd

roge

nase

54.2

17.

97–0

.033

01:

2.21

0.

6612

2M

MA

MC

M0.

914

0.98

70.

987

4E

nzym

e

P11

884

Ald

ehyd

ed

ehyd

roge

nase

(ALD

Hcl

ass

2)(A

LDH

1)(A

LDH

-E2)

56.4

96.

63–0

.138

01:

1.41

0.

195

2M

MA

MM

M0.

907

0.99

60.

937

5E

than

olut

iliza

tion

Q64

428

Trifu

nctio

nale

nzym

eal

pha

sub

unit

(TP

-alp

ha)

82.5

19.

11–0

.05

01:

1.10

21

MM

AM

MM

0.91

30.

998

0.97

65

Fatt

yac

id�

-ox

idat

ion

NP

_032

238.

1H

ydro

xyla

cyl-

Coe

nzym

eA

deh

ydro

gena

se33

.09

7.73

–0.1

691

1:1.

343

1M

MA

CM

O0.

303

0.72

30.

663

3Fa

tty

acid

�-

oxid

atio

nP

1460

4S

hort

chai

nen

oyl-

CoA

hyd

rata

se(S

CE

H)

31.5

28.

40–0

.104

01:

2.56

0.

3314

2M

MA

MM

M0.

905

0.98

30.

208

4Fa

tty

acid

�-

oxid

atio

nQ

9WV

K7

3-hy

dro

xyac

yl-C

oAd

ehyd

roge

nase

(HC

DH

)34

.45

8.83

–0.1

420

1:3.

86

1.35

42

MM

AM

MM

0.80

90.

995

0.96

65

Fatt

yac

id�

-ox

idat

ion

P49

432

Pyr

uvat

ed

ehyd

roge

nase

E1

com

pon

ent

�su

bun

it(P

DH

E1-

B)

38.8

55.

940.

116

01:

1.28

11

MM

AC

CM

0.76

30.

989

0.97

53

Gly

coly

sis

P26

284

Pyr

uvat

ed

ehyd

roge

nase

E1

com

pon

ent

�su

bun

it(P

DH

E1-

Aty

pe

I)

43.2

18.

49–0

.304

01:

1.36

11

MM

AC

MM

0.90

00.

995

0.96

64

Gly

coly

sis

P08

461

Dih

ydro

lipoa

mid

eac

etyl

tran

sfer

ase

com

pon

ent

ofp

yruv

ate

deh

ydro

gena

seco

mp

lex

(PD

C-E

2)

58.7

65.

700.

034

01:

2.77

0.

648

3M

MA

ER

EX

O0.

110

0.03

80.

000

0G

lyco

lysi

s

P19

226

60-k

Da

heat

shoc

kp

rote

in(H

sp60

)60

.96

5.91

–0.0

850

1:2.

69

0.40

243

MM

AC

CM

0.92

60.

995

0.98

13

Mol

ecul

arch

aper

one

P24

329

Thio

sulfa

tesu

lfurt

rans

fera

se(R

hod

anes

e)

33.1

87.

84–0

.463

01:

2.06

41

MM

AM

MM

0.63

90.

941

0.56

95

The

oxid

ativ

ep

hosp

hory

latio

n(O

XP

HO

S)

P13

803

Ele

ctro

ntr

ansf

erfla

vop

rote

in�

-sub

unit

(�-E

TF)

34.9

88.

670.

120

1:3.

005

1M

MA

MC

M0.

936

0.97

10.

182

3Th

eox

idat

ive

pho

spho

ryla

tion

(OX

PH

OS

)P

0463

6M

alat

ed

ehyd

roge

nase

35.6

68.

920.

121

01:

2.30

0.

5559

5M

MA

MM

M0.

903

0.99

50.

869

5TC

Acy

cle

Mito

chon

dria

lout

erm

emb

rane

P19

643

Mon

oam

ine

oxid

ase

(MA

O-

B)

58.3

98.

30–0

.152

11:

2.01

31

MO

MC

CO

0.02

50.

022

0.00

00

Am

ino

acid

met

abol

ism

Q60

932

Vol

tage

-dep

end

ent

anio

n-se

lect

ive

chan

nelp

rote

in1

(VD

AC

-1)

32.3

58.

55–0

.334

01:

2.48

41

MO

MC

MS

0.05

70.

026

0.02

01

Tran

spor

ter

orch

anne

lpro

tein

ICAT Analysis of Rat Liver Mitochondria with Different Purity

Molecular & Cellular Proteomics 4.1 21

Page 11: A Comparative Proteomic Strategy for Subcellular Proteome … · 2015-08-07 · A Comparative Proteomic Strategy for Subcellular Proteome Research ... erature reports have a ratio

TAB

LEI—

cont

inue

d

Pro

tein

acce

ssio

nno

.P

rote

inna

me

Mr

(kD

a)p

IG

RA

VY

Pre

dH

elR

atio

(L:H

)(C

M:P

M)

nuP

SP

PS

OR

TS

ubLo

cTa

rget

Pm

TPM

itoP

rot

Pre

dot

arB

TFu

nctio

nal

clas

sific

atio

n

Mito

chon

dria

-ass

ocia

ted

mul

tiloc

atio

np

rote

ins

Q62

651

�3,5

-�2,

4-d

ieno

yl-C

oAis

omer

ase

36.1

78.

14–0

.097

01:

0.22

31

M,

PP

NM

0.60

00.

976

0.18

62

Fatt

yac

id�

-oxi

dat

ion

P18

163

Long

-cha

inac

yl-C

oAsy

nthe

tase

2(L

AC

S2)

78.1

86.

60–0

.08

11:

1.13

0.

034

2M

OM

,E

R,

PC

CO

0.09

50.

092

0.02

90

Fatt

yac

id�

-oxi

dat

ion

Q9R

063

Per

oxire

dox

in5

22.1

88.

940.

179

01:

1.42

61

M,

C,

PM

CM

0.61

30.

799

0.98

84

Red

oxin

Q63

272

Tyro

sine

-pro

tein

kina

seJA

K3

(JA

K-3

)12

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CC

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064

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40.

000

0S

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ling

pro

tein

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759

14.5

-kD

atr

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atio

nal

inhi

bito

rp

rote

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40

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672

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MM

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425

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977

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atio

nfa

ctor

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915

Non

spec

ific

lipid

-tra

nsfe

rp

rote

in(N

SL-

TP)

58.8

16.

62–0

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1.25

21

M,

C,

PC

MM

0.50

40.

753

0.54

94

Tran

spor

ter

orch

anne

lp

rote

inP

rote

ins

with

sub

cellu

lar

loca

tion

anno

tatio

nno

tin

acco

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ithIC

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anal

ysis

Q8V

ID1

NA

DP

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inol

deh

ydro

gena

se27

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9.06

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70

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38

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42

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RM

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537

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nzym

e

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alas

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213

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hyd

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ase

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103

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ion

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oxis

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tifun

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1.46

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378

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tty

acid

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atio

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ired

oxin

4(P

rx-I

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31.0

16.

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1.13

21

CE

RC

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023

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000

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edox

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4313

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-(ap

urin

icor

apyr

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inic

site

)ly

ase

(AP

end

oNle

ase

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35.5

48.

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11

N,

M (56)

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001

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ansc

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nor re

plic

atio

nfa

ctor

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ATP

-bin

din

gca

sset

tesu

b-

fam

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ber

3(P

MP

70)

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11

PC

MM

0.89

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781

0.88

64

Tran

spor

ter

orch

anne

lp

rote

inK

now

np

rote

ins

with

out

sub

cellu

lar

loca

tion

anno

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nin

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iss-

Pro

td

atab

ase

NP

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057.

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ileac

idC

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7)E

RC

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acid

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acyl

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sfer

ase

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m

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500

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sep

hosp

hate

isom

eras

e(T

IM)

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56.

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601

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AC

CO

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80.

326

0.94

41

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yme

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109

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ock

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ate

71-

kDa

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tein

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Me

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eron

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cell

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curs

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MC

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hym

ase)

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3.03

21

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MS

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62

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NP

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a

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ICAT Analysis of Rat Liver Mitochondria with Different Purity

22 Molecular & Cellular Proteomics 4.1

Page 12: A Comparative Proteomic Strategy for Subcellular Proteome … · 2015-08-07 · A Comparative Proteomic Strategy for Subcellular Proteome Research ... erature reports have a ratio

TAB

LEI—

cont

inue

d

Pro

tein

acce

ssio

nno

.P

rote

inna

me

Mr

(kD

a)p

IG

RA

VY

Pre

dH

elR

atio

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)(C

M:P

M)

nuP

SP

PS

OR

TS

ubLo

cTa

rget

Pm

TPM

itoP

rot

Pre

dot

arB

TFu

nctio

nal

clas

sific

atio

n

NP

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173.

1P

rote

inki

nase

NY

D-S

P15

58.4

68.

34–0

.503

01:

0.40

11

NA

NN

O0.

421

0.03

20.

011

0S

igna

ling

pro

tein

NP

_079

076.

2N

IMA

(nev

erin

mito

sis

gene

a)-r

elat

edki

nase

1174

.16

5.02

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960

1:12

.34

11

NA

NC

O0.

162

0.70

60.

069

1S

igna

ling

pro

tein

O43

295

SLI

T-R

OB

OR

hoG

TPas

eac

tivat

ing

pro

tein

212

1.43

6.29

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340

1:4.

901

1N

AN

NO

0.14

30.

008

0.00

50

Sig

nalin

gp

rote

inN

P_0

0909

9.1

ATP

-bin

din

gca

sset

tesu

b-

fam

ilyA

mem

ber

817

9.25

6.81

0.05

513

1:3.

391

1N

AM

eE

XS

0.32

50.

984

0.05

91

Tran

spor

ter

orch

anne

lp

rote

inP

0903

4A

rgin

inos

ucci

nate

synt

hase

(Citr

ullin

e—as

par

tate

ligas

e)

46.5

07.

63–0

.357

01:

0.58

11

C(6

0)C

CO

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033

0.00

00

Ure

acy

cle

NP

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1C

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105

pro

tein

34.5

88.

48–0

.028

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41

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CC

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881

0.57

40.

906

3U

nkno

wn

func

tion

Nov

elp

rote

ins

XP

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339.

2S

imila

rto

CD

NA

seq

uenc

eB

C02

4561

13.8

010

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140

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047

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pro

tein

XP

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866.

2S

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rto

lam

inin

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2ch

ain

356.

536.

33–0

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0.05

11

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30.

020

0.00

00

Nov

elp

rote

in

BA

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571.

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0315

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415.

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11

NA

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CS

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006

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60

Nov

elp

rote

inX

P_1

1142

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Sim

ilar

tom

yosi

nlig

htch

ain

kina

se(M

LCK

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50

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elp

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in

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583.

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110

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NO

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50

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251

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34

Nov

elp

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elp

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NO

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10

Nov

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tein

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84.

45–0

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11

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000

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tein

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lyl

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eras

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76.

58–0

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31

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123

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000

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XP

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170.

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23

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71

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3071

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11

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1723

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mus

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21

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40

Nov

elp

rote

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565.

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41

NA

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CM

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950

0.99

43

Nov

elp

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120

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411

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23

Nov

elp

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toR

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6.21

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830

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511

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613.

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MO

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21

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tein

ICAT Analysis of Rat Liver Mitochondria with Different Purity

Molecular & Cellular Proteomics 4.1 23

Page 13: A Comparative Proteomic Strategy for Subcellular Proteome … · 2015-08-07 · A Comparative Proteomic Strategy for Subcellular Proteome Research ... erature reports have a ratio

TAB

LEI—

cont

inue

d

Pro

tein

acce

ssio

nno

.P

rote

inna

me

Mr

(kD

a)p

IG

RA

VY

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dH

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)(C

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M)

nuP

SP

PS

OR

TS

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rget

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TPM

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rot

Pre

dot

arB

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nctio

nal

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sific

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n

XP

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417.

2S

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EN

cDN

A99

3002

6A05

79.4

15.

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01:

1.24

21

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736

0.65

20.

985

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pro

tein

XP

_342

795.

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imila

rto

alco

hol

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ydro

gena

se8

50.8

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31

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143

0.03

70.

000

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XP

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NA

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1537

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8.91

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381

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795

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MS

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930

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34

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elp

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MM

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05

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elp

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HS

CO

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86.

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11

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XM

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70.

968

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elp

rote

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00

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elp

rote

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605.

1S

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hyp

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tical

pro

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MG

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66.3

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11

NA

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929

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10.

985

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pro

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XP

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356.

1S

imila

rto

hyp

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tical

pro

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DJ3

28E

19.C

1.1

147.

594.

47–0

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01:

2.65

11

NA

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0.00

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709

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1H

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681

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40

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elp

rote

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XP

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509.

2S

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cop

rote

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3.23

0.

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50.

138

0.48

40

Nov

elp

rote

in

XP

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323.

2S

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rto

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omp

rote

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970

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341

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NO

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150

0.00

00

Nov

elp

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1492

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760

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5116

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MO

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91

Nov

elp

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555.

2S

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NO

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00

Nov

elp

rote

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11

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70.

022

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rote

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11

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20.

587

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hp

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521

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rote

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olog

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ain

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ICAT Analysis of Rat Liver Mitochondria with Different Purity

24 Molecular & Cellular Proteomics 4.1

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proteins without subcellular location annotation, 22 (27.8%) ofthem have been annotated as extracellular or secreted pro-teins, 9 (11.4%) as endoplasmic reticulum, 6 (7.6%) as cyto-plasmic, 5 (6.3%) as peroxisomal, 5 (6.3%) as lysosomal, 4(5.1%) as ribosomal, 2 (2.5%) as nuclear, and 1 (1.3%) ascaveolae, membrane, Golgi complex, and cytoskeletal, re-spectively (Fig. 3A). Only two multilocation proteins have beenassociated with mitochondria (Table I). Delta-3,5-�2,4-dien-oyl-CoA isomerase with the ratio of PM:CM being 0.22 hasbeen annotated as mitochondrial and peroxisomal, and 14.5-kDa translational inhibitor protein with the ratio of PM:CMbeing 0.67 has been annotated as mitochondrial, cytoplas-mic, and nuclear.

On the contrary, for the 90 proteins with a ratio of PM:CM�1.0, 17 (18.9%) proteins have been annotated as mitochon-drial, 20 (22.2%) as mitochondrial matrix, 6 (6.7%) as mito-chondrial inner membrane, 3 (3.3%) as mitochondrial inter-membrane space, and 2 (2.2%) as mitochondrial outermembrane (Fig. 3B). Four mitochondria-associated multiloca-tion proteins have a ratio of PM:CM �1.0 (Table I). Long-chainacyl-CoA synthetase 2 (LACS 2) (1.13 0.03) has been an-notated as microsomes, outer mitochondrial membrane, andperoxisomal membrane. Nonspecific lipid-transfer protein(with the ratio of PM:CM being 1.25) has been annotated ascytoplasmic and mitochondrial. Peroxiredoxin 5 (1.42) hasbeen annotated as mitochondrial, peroxisomal, and cytoplas-mic. Tyrosine-protein kinase JAK3 (2.61) has been annotatedas wholly intracellular, possibly membrane associated.

As expected, all the annotated mitochondrial proteins have

a ratio of PM:CM �1.0, indicating their enrichment in PMfraction, while most proteins annotated as other organellessuch as endoplasmic reticulum, cytoplasmic, lysosomal, ribo-somal, nuclear, Golgi complex, caveolae, cytoskeletal, andmembrane, and some apparent contaminant proteins such assome extracellular or secreted proteins, for example, serumalbumin (0.30 0.13) and plasminogen (0.49), all have a ratioof PM/CM �1.0, indicating their decrease in PM fraction.Thus, using such a comparative proteomics research, mito-chondrial proteins have been effectively distinguished fromother contaminant proteins.

Bioinformatics Prediction—The five bioinformatics tools,PSORT II, SubLoc, TargetP, MitoProtII, and Predotar, havebeen used to predict subcellular location of the 169 proteins,respectively. For the overview prediction, PSORT II and Sub-Loc are used in the winner-takes-all mode without setting aspecificity cut-off for targeting. TargetP (mTP), MitoProtII, andPredotar predict proteins as mitochondrial based on a prob-ability cut-off of �0.50 (Fig. 2, Table I).

For the 79 proteins with a ratio of PM:CM �1.0, PSORTpredicted 3.8% (3/79) proteins as mitochondrial and 96.2%(76/79) as nonmitochondrial including 32.9% (26/79) as cyto-plasmic, 25.3% (20/79) as extracellular, 19.0% (15/79) asnuclear, 8.9% (7/79) as endoplasmic reticulum, 3.8% (3/79) asGolgi complex, 2.5% (2/79) as peroxisomal, 2.5% (2/79) asplasma membrane, and 1.3% (1/79) as cytoskeletal. SubLocpredicted 12.7% (10/79) proteins as mitochondrial and 87.3%(69/79) as nonmitochondrial including 34.2% (27/79) as cyto-plasmic, 30.4% (24/79) as extracellular, and 22.8% (18/79) as

FIG. 1. Category of the identified and quantitated 169 different proteins. The 169 proteins are categorized according to the number ofunique peptides used for protein identification and quantitation, physicochemical characteristics such as molecular mass, pI value, GRAVYvalue, and PredHel predicted by TMHMM, and ICAT ratio.

ICAT Analysis of Rat Liver Mitochondria with Different Purity

Molecular & Cellular Proteomics 4.1 25

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nuclear. TargetP predicted 10.1% (8/79) proteins as mito-chondrial and 89.9% (71/79) as nonmitochondrial including51.9% (41/79) as secreted and 38.0% (30/79) as other. Mito-ProtII and Predotar predicted 21.5% (17/79) and 27.8% (22/79) proteins as mitochondrial in the 79 proteins with a ratio ofPM:CM �1.0, respectively. Moreover, for the 79 proteins witha ratio of PM:CM �1.0, 55.7% (44/79) proteins have beenpredicted as nonmitochondrial by all the five bioinformaticstools (BT � 0), only 8.9% (7/79) proteins have been predictedas mitochondrial by three of the five bioinformatics tools at thesame time (BT � 3), and only 2.5% (2/79) proteins predictedas mitochondrial by four of the five bioinformatics tools at thesame time (BT � 4) (Table II).

On the other hand, for the 90 proteins with a ratio of PM:CM�1.0, PSORT predicted 34.4% (31/90) proteins as mitochon-drial and 65.6% (59/90) as nonmitochondrial including 41.1%(37/90) as cytoplasmic, 4.4% (4/90) as extracellular, 8.9%(8/90) as nuclear, 4.4% (4/90) as endoplasmic reticulum, 2.2%(2/90) as peroxisomal, 3.3% (3/90) as plasma membrane, and1.1% (1/90) as cytoskeletal. SubLoc predicted 47.8% (43/90)proteins as mitochondrial and 52.2% (47/90) as nonmitochon-drial including 27.8% (25/90) as cytoplasmic, 10.0% (9/90) asextracellular, and 14.4% (13/90) as nuclear. TargetP predicted51.1% (46/90) proteins as mitochondrial and 48.9% (44/90) asnonmitochondrial including 8.9% (8/90) as secreted and40.0% (36/90) as other. MitoProtII and Predotar predicted

FIG. 2. Percentage of the proteins annotated or predicted as mitochondrial or nonmitochondrial in the proteins with different PM:CMratio cut-offs. A, percentage of nonmitochondrial proteins for proteins with ratios of PM:CM �1.0, �0.83, �0.67, and �0.50. B, percentageof mitochondrial proteins for proteins with ratios of PM:CM �1.0, �1.2, �1.5, and �2.0. For Swiss-Prot, only proteins with subcellular locationannotation are concerned. PSORT II and SubLoc are used in the winner-takes-all mode without setting a specificity cut-off for targeting.TargetP (mTP), MitoProtII, and Predotar predict proteins as mitochondrial based on a probability cut-off of �0.50.

ICAT Analysis of Rat Liver Mitochondria with Different Purity

26 Molecular & Cellular Proteomics 4.1

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FIG. 3. Summary of the subcellular location of the identified proteins according to Swiss-Prot annotation. A, proteins with ratios ofPM:CM �1.0. B, proteins with ratios of PM:CM �1.0.

ICAT Analysis of Rat Liver Mitochondria with Different Purity

Molecular & Cellular Proteomics 4.1 27

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60.0% (54/90) and 56.7% (51/90) proteins as mitochondrial inthe 90 proteins with a ratio of PM:CM �1.0, respectively.Moreover, for the 90 proteins with a ratio of PM:CM �1.0,74.4% (67/90) proteins have been predicted as mitochondrialby at least one bioinformatics tools (BT � 1), and 54.4%(49/90) proteins have been predicted as mitochondrial by atleast three of the five bioinformatics tools at the same time(BT � 3), in which 18 proteins have been predicted as mito-chondrial by all the five bioinformatics tools (BT � 5) (Table III).

As we can see, the prediction results also indicate the ICATanalysis has effectively distinguished mitochondrial proteinsfrom possible contaminant proteins. At the same time, thedifference among the results from the bioinformatics predic-tion, Swiss-Prot annotation, and ICAT analysis may resultfrom the limitations of the bioinformatics tools (45), proteinmultilocation, and even the faulty of Swiss-Prot database.

Proteins with Subcellular Location Annotation not in Ac-cordance with ICAT Analysis—Nine proteins that have beenannotated as nonmitochondrial in Swiss-Prot have a ratio ofPM:CM �1.0 (Table I). They include quinone oxidoreductase(-crystallin) (1.46 0.21) that was annotated as cytoplasmic,AP endonuclease 1 (4.55) annotated as nuclear, and catalase(1.41 0.26), ATP-binding cassette sub-family D member 3(1.75), peroxisomal multifunctional enzyme type 2 (MFE-2)(1.46 0.37), NADP-retinol dehydrogenase (1.38 0.03), and2-hydroxyphytanoyl-CoA lyase (2.72), which were annotatedas peroxisomal.

Catalase, a scavenger of H2O2, has been long known as themost abundant matrix protein within peroxisomes (52). How-ever, the presence of catalase in rat heart mitochondria wasdemonstrated by biochemical and immunocytochemical anal-ysis (53). Yeast catalase A (Cta1p) contains two peroxisomaltargeting signals localized at its carboxyl terminus (SSNSKF)and within the N-terminal third of the protein, which both cantarget foreign proteins to peroxisomes. It has been morerecently demonstrated that Cta1p can also enter mitochon-dria, although the enzyme lacks a classical mitochondrialimport sequence. Peroxisomal and mitochondrial coimport ofcatalase A were tested qualitatively by fluorescence micros-copy and functional complementation of a �cta1 null muta-tion, and quantitatively by subcellular fractionation followedby Western analysis and enzyme activity assays (54). Moreinterestingly, in the proteomic analysis of the rat liver peroxi-some obtained by differential centrifugation and further puri-fied by density gradient and by immunopurification, accordingto the SDS-PAGE band intensity, the amount of the mostabundant matrix protein, catalase, seemed to decrease, whilethe band corresponding to another major matrix protein,uricase, clearly increased in its intensity (37). In addition, in therat liver subcellular fractions obtained with one-step subcel-lular fractionation using a Nycodenz density gradient pre-pared by freezing-thawing, catalase has shown more abun-dance in the mitochondria fraction than in the peroxisomefraction according to Western blotting detection though cat-

alase was used as a peroxisome marker protein (55). In thepresent study, catalase has been quantified according to twounique peptides and has a ratio of PM:CM as 1.41 0.26(Supplemental Fig. 1), which confirm its mitochondrial target-ing and further implicate its abundance may be higher in ratliver mitochondria than in peroxisome (37, 55).

Mutations of mitochondrial DNA (mtDNA) are associatedwith different human diseases, including cancer and aging(11, 12). Reactive oxygen species produced during oxidativephosphorylation are a major source of mtDNA damage. APE/Ref-1 is a nuclear protein possessing both redox activity andDNA repair activity over apurinic/apyrimidinic sites. Immuno-histochemical evidences indicate that in follicular thyroidcells, APE/Ref-1 is located in both nucleus and cytoplasm.Electronmicroscopy immunocytochemistry performed in therat thyroid FRTL-5 cell line indicates that part of the cytoplas-mic APE/Ref-1 is located in mitochondria. The presence ofAPE/Ref-1 inside mitochondria is further demonstrated byWestern blot analysis after cell fractionation (56). In the pres-ent study, the ICAT ratio of PM:CM for AP endonuclease 1 isup to 4.55 (Supplemental Fig. 2), which also indicate its loca-tion in mitochondrial and may further implicate its low abun-dance in mitochondria.

Known Proteins Without Subcellular Location Annotation inthe Swiss-Prot Database—Twelve proteins are known pro-teins without subcellular location annotation in the Swiss-Protdatabase (Table I). Fortunately, many of them have beenreported about their subcellular locations that are consistentwith our ICAT analysis results (57–60). For example, bile acidCoA:amino acid N-acyltransferase (BAT) is responsible for theamidation of bile acids with the amino acids taurine andglycine. Immunoblot analysis of rat tissues detected rat liverBAT (rBAT) only in rat liver cytosol prepared with homogeni-zation and ultracentrifugation. Subcellular localization of rBATdetected activity and immunoreactive protein in both cytosoland isolated peroxisomes (57). Rat bile acid CoA ligase(rBAL), the enzyme responsible for the formation of bile acidCoA esters, was detected only in rat liver microsomes (57). Inthe present study, bile acid CoA ligase and bile acid-CoA:amino acid N-acyltransferase have a ratio of PM:CM as0.66 0.09 and 0.71 0.25, respectively.

Novel Proteins—Thirty-seven novel proteins have beenidentified in the ICAT analysis. SIB BLAST2 Network Service(us.expasy.org/tools/blast/) was used for novel protein blastagainst the UniProt knowledgebase (Swiss-Prot � TrEMBL �

TrEMBL_NEW). Twenty-one novel proteins show high identi-ties (�70%) with known proteins, respectively, most of whichwith subcellular location annotation consistent with ICATanalysis results (Supplemental Table II). For example, novelprotein XP_230637.2, with ratio of PM:CM being 0.25 0.05,shows 83% identities with mouse ribosome-binding protein 1,which is annotated as endoplasmic reticulum membrane pro-tein. Protein XP_224605.1, predicted as mitochondrial proteinby all the five bioinformatics tools, with a ratio of PM:CM

ICAT Analysis of Rat Liver Mitochondria with Different Purity

28 Molecular & Cellular Proteomics 4.1

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ICAT Analysis of Rat Liver Mitochondria with Different Purity

Molecular & Cellular Proteomics 4.1 29

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being 2.37, has been just confirmed as choline dehydrogen-ase and localized in mitochondrial (61). The novel proteinXP_214838.1 predicted as mitochondrial protein by fourbioinformatics tools, with ratio of PM:CM being 1.96, shows88% identities with human HSCO protein, which has just beenrenamed as “ETHE1” and localized in mitochondrial matrix(62).

Thirteen novel proteins have a ratio of PM:CM �1.0, inwhich 10 (76.9% ,10/13) proteins have been predicted asnonmitochondrial by all the five bioinformatics tools (BT � 0)and are strongly implicated as nonmitochondrial proteins (Ta-ble II). For the 24 novel proteins with a ratio of PM:CM �1.0,14 (58.3%) proteins have been predicted as mitochondrial byat least one bioinformatics tools (BT � 1), in which 7 (29.2%)proteins have been predicted as mitochondrial by at leastthree bioinformatics tools.

Functional Classification

The 169 proteins are categorized according to Swiss-Protfunctional annotation (Fig. 4). As we know, many physiologicalactivities such as amino acid metabolism, fatty acid metabo-lism, glycolysis, urea cycle, transcription, and replication arefulfilled in or associated with multi-organelles including mito-chondria. It is not surprising to find proteins involved in thoseactivities distribute in the two groups of proteins with a ratio ofPM:CM �1.0 or �1.0. As expected, proteins involved in theoxidative phosphorylation (such as ATP synthase D chain and� chain, NADH-ubiquinone oxidoreductase 13-kDa-A subunitand 24-kDa subunit, ubiquinol-cytochrome c reductase com-plex 11-kDa protein and core protein 2, electron transferflavoprotein-ubiquinone oxidoreductase, and electron trans-fer flavoprotein �-subunit), the tricarboxylic acid (TCA) cycle(such as succinyl-CoA ligase �-chain and �-chain, isocitratedehydrogenase, and malate dehydrogenase), and ketonebody metabolism (such as HMG-CoA synthase and aceto-acetyl-CoA thiolase), all have a ratio of PM:CM �1.0 (Fig. 4B).On the other hand, proteins that function in interactions be-tween cells and the extracellular matrix (such as �-1 catenin,sulfated glycoprotein 1 precursor (SGP-1), fibronectin, throm-bospondin 2, and basigin), blood coagulation or fibrinolysis(such as plasminogen and prothrombin), bile acid metabolism(such as bile acid CoA ligase and bile acid-CoA:amino acidN-acyltransferase), and ribosomal proteins (such as 60S ribo-somal protein L10a (CSA-19), L30, L12, and 60S acidic ribo-somal protein P1), all have a ratio of PM:CM �1.0 (Fig. 4A).

As we can see, in accordance with multifunction of mito-chondria (7–13), proteins with a ratio of PM:CM �1.0 includemany proteins that function in amino acid metabolism, fattyacid metabolism, glycolysis, the oxidative phosphorylation,the TCA cycle, ketone body metabolism, urea cycle, andtranscription and replication. Moreover, many novel proteinshave been implicated their mitochondrial location.

Evaluation of the Efficiency to Ascertain MitochondrialProteins by ICAT Analysis and a Series of

Bioinformatics Tools

The 125 proteins with subcellular location informationhave been used as a test dataset to evaluate the efficiencyto ascertain mitochondrial proteins by ICAT analysis and thefive bioinformatics tools (Table II). When PSORT II andSubLoc are used in the winner-takes-all mode without set-ting a specificity cut-off for targeting, the sensitivity andspecificity to predict mitochondrial proteins is 0.50 and 0.89for PSORT and 0.60 and 0.67 for SubLoc, respectively.Based on a probability cut-off of �0.50, the sensitivity andspecificity to predict mitochondrial proteins is 0.73 and 0.76for TargetP (mTP), 0.83 and 0.70 for MitoProtII, and 0.73 and0.60 for Predotar, respectively. Moreover, TargetP (mTP) andMitoProtII show increased specificity based on the probabilitycut-off of �0.70 or �0.85, while Predotar shows low speci-ficity even if based on the probability cut-off of �0.85 or�0.95.

For ICAT analysis, according to the ratio of PM:CM �1.0,the sensitivity and specificity to ascertain mitochondrial pro-teins is 1.00 and 0.79, respectively, almost higher than everybioinformatics tools. Moreover, according to the ratio ofPM:CM �1.0, the sensitivity and specificity to ascertain non-mitochondrial proteins is 0.90 and 0.97, respectively, alsohigher than every bioinformatics tools based on a probabilitycut-off of �0.50. So ICAT analysis, as a high-throughputproteomics experimental strategy, has shown great superior-ity in ascertaining mitochondrial proteins than the most widelyused bioinformatics tools such as PSORT, SubLoc, TargetP,MitoProtII, and Predotar.

The different sensitivity and specificity of the bioinformaticstools would favor in combinational usage of those bioinfor-matics tools to predict mitochondrial proteins. In the testdataset, prediction based on at least one bioinformatics tool(BT � 1) shows sensitivity high up to 0.88 with specificity as0.53. At the same time, the more bioinformatics tools usedcombinationally, the higher specificity for the prediction ofmitochondrial proteins. For prediction based on all the fivebioinformatics tools (BT � 5), the specificity is high up to 1.00.Interestingly, when in combination with the ICAT analysis, thespecificity increased from 0.53 to 0.86 for the predictionbased on at least one bioinformatics tool (BT � 1).

DISCUSSION

More recently, the combination of LC, stable ICAT, andMS/MS has emerged as an alternative quantitative proteom-ics technology (47). In ICAT analysis, two pools of proteins,labeled with light and heavy reagent, respectively, are chem-ically identical and therefore serve as a good internal stand foraccurate quantification. The method has been proved com-plementary to traditional 2D-PAGE (63, 64) and widely appliedin comparative proteomics research such as quantification ofmicrosomal proteins in differentiated versus undifferentiated

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HL-60 cells and quantification of protein expression in ratmyc-null cells versus myc-plus cells (48, 65).

In the present study, we apply such a high-throughput

comparative proteomic experimental strategy, the ICAT tech-nique, to analyze rat liver mitochondria fractions with differentdegrees of purity, prepared with traditional centrifugation or

FIG. 4. Functional classification of the identified proteins according to Swiss-Prot annotation. A, proteins with ratios of PM:CM �1.0.B, proteins with ratios of PM:CM �1.0.

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further purified with a Nycodenz gradient, in the aim to ascer-tain mitochondrial proteins and distinguish contaminantproteins.

A total of 169 different proteins were identified and quanti-fied convincingly. Ninety proteins have a ratio of PM:CM�1.0, while 79 proteins have a ratio of PM:CM �1.0. Accord-ing to Swiss-Prot annotation, bioinformatics prediction, andliterature reports, almost all the proteins with a ratio of PM:CM�1.0 are mitochondrial proteins, while proteins annotated asextracellular or secreted, cytoplasmic, ribosome, endoplas-mic reticulum, and lysomal have a ratio of PM:CM �1.0 (Figs.2 and 3; Table I). Thus, such a comparative proteome exper-imental strategy has been proven effective in ascertainingmitochondrial proteins in a high-throughput way. Many novelproteins, known proteins without subcellular location annota-tion and even known proteins that have been annotated asother locations have been strongly indicated for their mito-chondrial location. Especially, protein catalse and AP endo-nuclease 1, which have been known as peroxisomal andnuclear, respectively, have shown a ratio of PM:CM �1.0(1.41 0.26 and 4.55, respectively) (Supplemental Figs. 1 and2), confirming the reports about their mitochondrial location(37, 53–56). Functional study of those proteins will promoteour understanding on mitochondria structure and function.

In all eukaryotic cells, peroxisomes and mitochondria sharea great variety of enzymatic reactions that are catalyzed byisozymes present in both organelles. For some of these en-zymes it is known that they can be cotargeted to differentorganelles, for example, �3,5-�2,4-dienoyl-CoA isomeraseand long-chain acyl-CoA synthetase 2 (LACS 2), which areresponsible for fatty acid �-oxidation, have been annotated asmultilocation in both mitochondria and peroxisome. A mostrecent example for such a cotargeting has been given for theyeast peroxisomal citrate synthase Cit2p, an enzyme of theTCA cycle that contains a cryptic amino-terminal signal se-quence that functions in both peroxisomal and mitochondrialtargeting (66). Besides enzymes that catalyze related reac-tions within the fatty acid �-oxidation, TCA, and the glyoxylatecycle, enzymes involved in the detoxification of oxygen radi-cals are also present in both peroxisome and mitochondria.Peroxiredoxins (Prxs) form a recently discovered large familyof antioxidant enzymes that act as peroxidases reducing hy-drogen peroxide and alkyl hydroperoxides to water or thecorresponding alcohol, respectively (67). Peroxiredoxin 5 hasbeen annotated as mitochondrial, peroxisomal, and cytoplas-mic. In the present study, in addition to catalase, ATP-bindingcassette subfamily D member 3 (1.75), peroxisomal multifunc-tional enzyme type 2 (MFE-2) (1.46 0.37), NADP-retinoldehydrogenase (1.38 0.03), and 2-hydroxyphytanoyl-CoAlyase (2.72), which were annotated as peroxisomal, all have aratio of PM:CM �1.0 and have been implicated their locationin mitochondria as multilocation proteins.

Comparison of the results from the five bioinformatics toolsand Swiss-Prot annotation and ICAT analysis have shown the

limitations of the bioinformatics tools (45) and even the faultynature of Swiss-Prot annotation (Fig. 2, Table II). The ICATanalysis coupled with combinational usage of different bioin-formatics tools can effectively ascertain mitochondrial pro-teins with high sensitivity and specificity (Table II). Moreover,the inconsistence between ICAT analysis and bioinformaticsprediction could implicate mitochondria-associated multilo-cation. For example, in the present study, �3,5-�2,4-dienoyl-CoA isomerase and 14.5-kDa translational inhibitor protein,with the ratio of PM:CM �1.0 (0.22 and 0.69, respectively),have been predicted as mitochondrial proteins by two andfour bioinformatics tools, respectively. AP endonuclease 1and catalase, which have been predicted as nonmitochondrialproteins by all five bioinformatics tools, have a ratio of PM:CMas 4.55 and 1.41 0.26, respectively. All four proteins havebeen annotated as mitochondria-associated multilocationproteins according to Swiss-Prot database or literature reports(37, 53–56).

In summary, we have applied a high-throughput compara-tive proteome experimental strategy, the ICAT approach per-formed with 2D-LC-MS/MS, coupled with combinational us-age of different bioinformatics tools, to study the proteome ofrat liver mitochondria, with the major problem of contamina-tion in subcellular proteomics research effectively circum-vented. Concerning the limitation of the ICAT approach foranalyzing proteins lacking cysteine residues, acidic proteins,or proteins with low molecular mass (63, 64), other compar-ative proteomics approaches, such as traditional 2D-PAGE,DIGE (68, 69), and stable isotope labeling with amino acids incell culture (SILAC) (70, 71), should be used as complemen-tary methods. Such a comparative proteomics strategyshould be widely used in subcellular proteomics research toprovide more subcellular proteome data with high quality.

* This work was supported by National High-Technology Project(2002BA711A11) and Basic Research Foundation (2001CB210501,2002CB713807). The costs of publication of this article were defrayedin part by the payment of page charges. This article must therefore behereby marked “advertisement” in accordance with 18 U.S.C. Section1734 solely to indicate this fact.

□S The on-line version of this manuscript (available at http://www.mcponline.org) contains supplemental material.

‡ To whom correspondence should be addressed: Research Cen-tre for Proteome Analysis, Institute of Biochemistry and Cell Biology,Shanghai Institutes for Biological Sciences, Chinese Academy ofSciences, 320 YueYang Road, Shanghai 200031, China. Tel.: 86-21-54920170; Fax: 86-21-54920171; E-mail: [email protected].

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