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{ddagger}

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


    ABSTRACT
 TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
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 proteomic research including all kinds of mitochondrial proteome research. It is often difficult to conclude whether these "contaminants" represent true endogenous partners or artificial associations induced by cell disruption or incomplete purification. To solve such a problem, we applied a high-throughput comparative proteome experimental strategy, ICAT approach performed with two-dimensional LC-MS/MS analysis, coupled with combinational usage of different bioinformatics tools, to study the proteome 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 literature reports have a ratio of PM:CM >1.0, while proteins annotated as extracellular or secreted, cytoplasmic, endoplasmic 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, respectively, 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 proteins and even multilocation proteins. Using such a strategy, many novel proteins, known proteins without subcellular location annotation, and even known proteins that have been annotated as other locations have been strongly indicated for their mitochondrial location.


There has been a tendency to focus on subcellular proteomes concerning specific subcellular compartments and macromolecular structures of the cell (16). The separation of the protein mixture into organelles or other multiprotein complex 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 function, 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 (16).

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 (813). Consequently, many diseases have been attributed to mitochondrial defects, including Alzheimer’s disease, Parkinson’s disease, Friedreich ataxia, diabetes mellitus, malignant tumors, cardiovascular disease, and osteoarthritis (1424). These findings have promoted increasing efforts to define the mitochondrial proteome and to discover new molecular targets for drug development and therapeutic intervention (7, 20, 2533).

Mass spectrometric methods and automation of a large part of the process including robotics application have continued to improve dramatically in recent years, allowing both increased sensitivity and higher throughput. Improved software and databases containing different species genes—known or putative—are also now available, allowing automated data processing of the large volume of acquired mass spectra. As a result, many largest subcellular proteome databases (16, 34), especially for mitochondria proteome, have been set (7, 2532). For example, the largest two-dimensional (2D)1-PAGE map database by far for rat liver mitochondria contains 192 individual proteins from 1,800 spots (25) and for yeast mitochondria contains 253 individual proteins from 459 spots (26). On the other hand, the largest shotgun proteome databases by far have been set for human heart mitochondria with 615 individual proteins (27), for mouse mitochondria with 591 individual proteins (7), for Saccharomyces cerevisiae mitochondria with 750 different proteins (28), and for rat liver mitochondria with 227 unique rat proteins (29).

In this new context, the perfect purity of intact protein complexes has been crucial to subcellular proteome research (35, 36). In addition to conventional differential centrifugation (25), many further purification methods such as density gradient centrifugation (7, 2629, 37), immunoisolation (37), and free-flow electrophoresis (38) have been applied to subcellular proteome research and have shown improved effects in identification of more specific subcellular proteins (29, 37, 38). However, the co-purification of "contaminating" proteins is still the major problem in all the subcellular proteome research. For those largest mitochondrial proteome databases, only 33.3~62.8% of the identified proteins have been annotated as mitochondrial proteins, while 14.1~43.2% of them have been annotated as other organelle proteins and 9.9~33.9% of them have no subcellular location annotation (7, 2529). Of the 192 proteins in the 2D-PAGE database for the rat liver mitochondrial fraction prepared by differential centrifugation, only 64 (33.3%) proteins have been annotated as mitochondrial proteins, while 83 (43.2%) proteins have been annotated as other organelle proteins and 45 (23.4%) proteins have no subcellular location annotation in Swiss-Prot database (25). Of the 253 proteins in the 2D-PAGE database for the S. cerevisiae mitochondrial fraction purified with a three-step sucrose gradient, 159 (62.8%) proteins have been annotated as mitochondrial proteins, while 69 (27.2%) proteins 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 purified with a Percoll gradient, 163 proteins (27.6%) have not previously annotated as associated with mitochondria (7). Among the 750 proteins identified in S. cerevisiae mitochondria purified with a three-step sucrose gradient, 436 (58.1%) proteins are 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 cellular compartments (28). For the 227 rat proteins identified from the rat liver mitochondria purified with a Nycodenz gradient, 80 (35.2%) have been annotated as mitochondrial proteins in Swiss-Prot database, while 70 (30.8%) proteins have been annotated as other organelle proteins and 77 (33.9%) proteins have no subcellular location annotation (29). Even though the peroxisome purified with a Nycodenz gradient were further immunoisolated, many mitochondrial and endoplasmic reticulum proteins have been detected (37).

It is often difficult to conclude whether these "contaminants" represent true endogenous partners or artificial associations induced by cell disruption or incomplete purification (35). At the same time, the novel proteins and proteins without subcellular location annotation need more evidence for their subcellular location. Experimental determination of subcellular location is mainly accomplished by three approaches: cell fractionation, electron microscopy, and fluorescence microscopy. As currently practiced, these approaches are time consuming, subjective, and highly variable (39). With experimentally verified information on protein subcellular location lagging far behind, a series of bioinformatics tools such as PSORT (40, 41), TargetP (40, 42), SubLoc (43), MitoProtII (44), and Predotar (www.inra.fr/Internet/Produits/Predotar/) have been developed and widely used in many subcellular proteome data (25, 26, 2832). PSORT was developed as an expert system that uses a set of 100 "if-then"-type rules based on analysis of characterized protein sequences from a variety of subcellular locations (40, 41). TargetP, based on neural network programming, was developed to predict targeting of protein sequences to chloroplasts, mitochondria, and the secretory system using a knowledgebase derived from Swiss-Prot sequence entries (40, 42). SubLoc is a prediction system for protein subcellular localization based on amino acid composition alone using a Support Vector Machine method (43). MitoProt was developed to predict mitochondrial targeting and presequence cleavage sites based on a set of 47 known characteristics of presequences and cleavage sites (44). Predotar is particularly good at distinguishing mitochondrial and plastid targeting sequences and recognizes the N-terminal targeting sequences of classically targeted mitochondrial and chloroplast precursor proteins. However, many problems are involved in the prediction (45), and it is questionable whether the efficiency still holds when applied to proteome data (46). Using an actual subcellular proteome dataset, we have shown that the sensitivity and specificity of PSORT and TargetP have been overevaluated previously. But interestingly, the combinational usage of TargetP and PSORT has a high specificity up to 0.86 for mitochondrial protein prediction (29).

More recently, Dunkley et al. has just discussed that the use of comparative proteomics to analyze the relative levels of proteins in different organelle-enriched fractions can solute the problem of contaminants and distinguish between proteins from different subcellular compartments without the need to obtain pure organelles (36). Implicated from the fact that further purified mitochondrial fractions enriched mitochondrial proteins and decreased apparent contaminant proteins compared with crude mitochondrial fraction (29, 38), we think that comparative proteomic research on the crude and purified subcellular fraction would be in favor of ascertainment of the specific subcellular proteins and exclusion of the contaminants. In the present study, we apply ICAT technology, a key strategy in comparative proteomic research (4749), to compare the crude mitochondrial (CM) fraction of rat liver prepared with differential centrifugation with the purified mitochondrial (PM) fraction further purified with a Nycodenz gradient. After 2D-LC-MS/MS analysis, a total of 169 proteins were identified and quantified. The ICAT data were evaluated according to Swiss-Prot annotation and prediction of five bioinformatics tools such as PSORT, TargetP, SubLoc, MitoProt, and Predotar. The prediction efficiency for mitochondrial protein of the five bioinformatics tools was compared. The results indicated that ICAT analysis coupled with combinational usage of different bioinformatics tools could effectively ascertain mitochondrial proteins and distinguish contaminant proteins and even multilocation proteins. Using such a strategy, many novel proteins, known proteins without subcellular location annotation, and even known proteins that have been annotated as other locations have been strongly indicated for their mitochondrial location.


    EXPERIMENTAL PROCEDURES
 TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
Materials—
Analytical reagent-grade chemicals were used throughout unless otherwise stated. Water was prepared using a Milli-Q system (Millipore, Bedford, MA). Nycodenz, formic acid, guanidine hydrochloride, sodium orthovanadate (Na 3VO 4), and sodium fluoride (NaF) were obtained from Sigma (St. Louis, MO). Chemicals employed for 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 (Southampton, United Kingdom). EDTA, EGTA, and PMSF were purchased from Amresco (Solon, OH). Adult male Sprague-Dawley rats were purchased from Shanghai Laboratory Animal Center (Jiu-Ting, Shanghai, China).

Differential Centrifugation Separation of Rat Liver Subcellular Fractions—
Subcellular fractionation of rat liver was performed as described previously (29). Briefly, Sprague-Dawley rats were sacrificed and the livers were promptly removed and placed in ice-cold homogenization 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 Na 3VO 4, 1 mM NaF) and 10 mM Tris-HCl at pH 7.4. After mincing with scissors and washing to remove blood, the livers were homogenized in a Potter-Elvejhem homogenizer with a Teflon piston, using 10 ml of the homogenization buffer per 2 g of tissue. Centrifugation at successively higher speeds at 4 °C yielded the following fractions: crude nuclear fraction at 1,000 x g for 10 min; mitochondria at 15,000 x g for 15 min; and microsomes at 144,000 x g for 90 min. The final supernatant was the cytosol fraction. Each successive pellet was washed three times with the homogenization buffer. The centrifuges used were the Himac CR 21G high-speed refrigerated centrifuge and Himac CP 80MX preparative ultracentrifuge, both from Hitachi Koki Co. Ltd. (Tokyo, Japan).

Purification of Rat Liver Mitochondria Through Nycodenz Density Gradient Centrifugation—
The procedures recommended by Nycomed Pharma and Invitrogen Life Technologies were followed as described previously (29). Nycodenz was dissolved to 50% (w/v) in 5 mM Tris-HCl, pH 7.4, containing 1 mM EDTA, 0.5 mM EGTA, and a mixture 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 protease inhibitor and phosphatase inhibitors at pH 7.4. The crude mitochondrial pellets obtained from differential centrifugation were suspended in 12 ml of 25% nycodenz and placed on the following discontinuous nycodenz gradients: 5 ml of 34% and 8 ml of 30%, and this was topped off with 8 ml of 23% and finally 3 ml of 20%. The sealed tubes were centrifuged for 90 min at 52,000 x g at 4 °C. The bands of particles seen after centrifugation have been identified by Nycomed Pharma and Invitrogen Life Technologies as follows: nuclei at the 40/50% interface; peroxisomes at the 34/40% interface; mitochondria at the 25/30% interface, lysosomes at the 15/20% interface, and Golgi membranes at the 10/15% interface. The band at the 25/30% interface was collected and diluted with the same volume homogenization buffer and then centrifuged at 15,000 x g for 20 min.

Protein Preparation—
The mitochondria pellets from differential centrifugation (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, and centrifuged at 25,000 x g for 1 h. The supernatants were collected as CM and PM fractions. The protein concentration was determined by the Bradford assay. Then the protein samples were directly used for 2D-PAGE or ICAT analysis after another precipitation and redissolving.

ICAT Analysis—
ICAT analysis was performed using Cleavable ICAT TM Reagent Kit (Applied Biosystems, Foster City, CA) according to the manufacturer’s guidelines with some modifications. For ICAT analysis, the protein samples were precipitated overnight with 5x volumes of –20 °C 50:50:0.1 volumes of ethanol:acetone:acetic acid and resolubilized in denaturing buffer (6 M guanidine hydrochloride, 100 mM TrisCl, pH 8.3). One hundred micrograms of the CM or PM protein sample in 80 µl of denaturing buffer were reduced at 37 °C for 2 h with 5 mM tributylphosphine (Bio-Rad) and alkylated at 37 °C for 2 h in the dark with ICAT-light and ICAT-heavy reagent, respectively. After reaction, ICAT-light and ICAT-heavy reactants were mixed together 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 µg of trypsin (50:1) at 37 °C for 20 h. Then the ICAT-labeled peptides were purified using the kit of ICATTM Avidin Buffer Pack and Avidin Affinity Cartridge (Applied Biosystems) according to the manufacturer’s guidelines. Briefly, the peptide mixture was dried by vacuum centrifuge and resolubilized in the loading buffer of the kit. The peptide mixture was loaded in the Avidin Affinity Cartridge and washed twice with two kinds of wash buffer to reduce the salt concentration and remove nonspecifically bound peptides. Then the ICAT-labeled peptides were eluted with the elution buffer and dried by vacuum centrifuge. The dried peptides were cleaved of the biotin portion of the ICAT reagent with the cleaving reagents at 37 °C for 2 h. Then the ICAT-labeled peptides were dried by vacuum centrifuge and resolubilized in 0.1% formic acid for 2D-LC-MS/MS analysis.

2D-LC-MS/MS—
Orthogonal 2D-LC-MS/MS was performed using a ProteomeX Work station (Thermo Finnigan, San Jose, CA). The system was fitted with a strong cation exchange column (320 µm inner diameter x 100 mm, DEV SCX; Thermo Hypersil-Keystone) and two C18 reversed-phase columns (RP, 180 µm x 100 mm, BioBasic® C18, 5 µm; Thermo Hypersil-Keystone). The salt steps used were 0, 25, 50, 75, 100, 150, 200, 400, and 800 mM NH 4Cl synchronized with nine 140-min RP gradients. RP solvents were 0.1% formic acid in either water (A) or ACN (B). The setting of the LCQ Deca Xplus ion-trap mass spectrometer is as following. One full MS scan was followed by three MS/MS scans on the three most intense ions from the 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 automatically searched against the combined human, mouse, and rat nonredundant database (NCBI (www.ncbi.nlm.nih.gov), 12/04/2003 released) using the TurboSEQUEST program in the BioWorksTM 3.1 software suite. An accepted SEQUEST result had to have a {Delta}Cn score of at least 0.1 (regardless of charge state). For ICAT analysis, protein identification and quantification was achieved by using SEQUEST and EXPRESS software tools. Peptides with a +1 charge state were accepted if they were fully tryptic and had a cross correlation (Xcorr) of at least 1.5. Peptides with a +2 charge state were accepted if they had an Xcorr >2.0. Peptides with a +3 charge state were accepted if they had an Xcorr >2.5. Then the peptides were further analyzed manually by detailed spectral analysis for confirmation of protein identification and quantification as described by Han et al. (48).

Bioinformatics Annotation Tools—
The theoretical pI and M r values of proteins were defined by program pepstats (www.hgmp.mrc.ac.uk/Software/EMBOSS). The protein function and subcellular location annotation 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/services/TargetP/) (40, 42), SubLoc (www.bioinfo.tsinghua.edu.cn/SubLoc/) (43), MitoProtII (www.mips.biochem.mpg.de/cgi-bin/proj/medgen/mitofilter) (44), and Predotar (www.inra.fr/Internet/Produits/Predotar/) have been used to predict protein subcellular location. The TMHMM (www.cbs.dtu.dk/services/TMHMM/) (50) was used to predict protein transmembrane domains. GRAVY values were determined according to Kyte-Doolittle (51). SIB BLAST2 Network Service (us.expasy.org/tools/blast/) was used for novel protein blast against the UniProt knowledgebase (Swiss-Prot + TrEMBL + TrEMBL_NEW).


    RESULTS
 TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
Proteins Identification and Quantification
Rat liver CM were prepared using conventional differential centrifugation and were further purified with a Nycodenz gradient to obtain PM. One hundred micrograms of the CM or PM protein sample were labeled with ICAT-light and ICAT-heavy reagent, respectively. After 2D-LC-MS/MS analysis, the MS/MS spectra were searched against the combined human, mouse, and rat nonredundant database using the program SEQUEST. Protein identification and quantification was achieved by using SEQUEST and EXPRESS software tools (see "Experimental Procedures" for details).

A total of 169 different proteins were identified and quantified from 755 cysteine-containing peptides (253 unique peptides) including 398 +2 charge peptides with Xcorr >2.0, and 355 +3 charge peptides with Xcorr >2.5. All the peptides have a {Delta}Cn score of at least 0.1 (regardless of charge state). Only two peptides with a +1 charge were identified, with an Xcorr of 1.58 and 1.99, respectively.

For the 169 proteins, 69.8% (118/169) proteins were identified and quantitated on the basis of at least one +2 charge peptide with Xcorr >2.5 and {Delta}Cn >0.15 or +3 charge peptide with Xcorr >3.0 and {Delta}Cn >0.15. For 127 (75.1%, 127/169) proteins identified and quantitated according to one unique peptide 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 the basis of at least one +2 charge peptide with Xcorr >2.5 and {Delta}Cn >0.15 or +3 charge peptide with Xcorr >3.0 and {Delta}Cn >0.15 (Supplemental Table I).

According to physicochemical characteristics analysis, the 169 proteins (Table I) include 22 (13.0%) proteins with molecular mass >100 kDa, 22 (13.0%) proteins with pI value >9.0, 23 (13.6%) hydrophobic proteins with GRAVY value >0, and 20 (11.8%) proteins with one or more predicted transmembrane (TM) domain (Fig. 1), which indicate the ICAT analysis performed by 2D-LC-MS/MS have little limitations for identification of proteins with extreme size and charge values, and even hydrophobic and membrane proteins, which is consistent with our former 2D-LC-MS/MS analysis of rat liver subcellular fractions (29).


View this table:
[in this window]
[in a new window]
 
TABLE I Summary of proteins identified and quantified in rat liver mitochondria performed by ICAT analysis

n, total number of independent peptide identifications and quantification events for each protein. uP, number of unique peptide sequences identified for each protein. Subcellular location: C, cytoplasmic; CAV, caveolae; CSK, cytoskeletal; ER, endoplasmic reticulum; EX, extracellular; G, Golgi complex; L, lysosomal; Me, plasma membrane; M, mitochondrial; MIM, mitochondrial inner membrane; MIMS, mitochondrial intermembrane space; MOM, mitochondrial outer membrane; N, nuclear; O, other; P, peroxisomal; R, ribosomal; S, secreted; W, wholly intracellular. 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. BT, Bioinformatics tools, proteins predicted as mitochondrial proteins by different bioinformatics tools (BT) at the same time. BT = 0 and BT = 5 indicated that a protein were predicted as a mitochondrial protein by none or all of the five bioinformatics tools, respectively. NA, no annotation. SP, subcellular location annotation according to Swiss-Prot or literature report (marked with Ref. no.).

 


View larger version (17K):
[in this window]
[in a new window]
 
FIG. 1. Category of the identified and quantitated 169 different proteins. The 169 proteins are categorized according to the number of unique peptides used for protein identification and quantitation, physicochemical characteristics such as molecular mass, pI value, GRAVY value, and PredHel predicted by TMHMM, and ICAT ratio.

 
The relative quantification of each peptide was determined by the ratio of signal intensities of peptide pairs using the Express software tool (see Ref. 58). Ninety proteins have a ratio 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 PM fraction and should be mitochondrial proteins. Seventy-nine proteins have a ratio of PM:CM <1.0 with 48.1% (38/79) of them with a ratio ≤ 0.5 (–2.0), which indicates that they are decreased in the PM fraction and should be contaminant proteins. Only 12 (7.1%) proteins have a ratio of PM:CM between 0.83 (–1.2) and 1.2, in which four protein have a ratio of PM:CM <1.0 and eight proteins have a ratio of PM:CM >1.0 (Fig. 1). Interestingly, there is no apparent difference among the percentage of mitochondrial proteins for proteins with a ratio of PM:CM >1.0, >1.2, >1.5, and >2.0, or the percentage of nonmitochondrial proteins for proteins with a ratio of PM:CM <1.0, <0.83, <0.67, and <0.50, based on the Swiss-Prot annotation or five bioinformatics prediction results (Fig. 2). Considering that the protein or peptide abundance might influence many steps in the ICAT analysis such as ICAT reagent labeling, peptide enrichment and elution in the Avidin Affinity Cartridge, peptide separation in 2D-LC, and peptide ionization and detection by MS, the ratio of PM:CM might reflect synthetically the mitochondria purification effect, protein abundance, and multilocation influence. The relative low ratio such as PM:CM of 1.0~1.5 might implicate proteins with high abundance or mitochondria-associated multilocation, while the relative high ratio such as PM:CM >4.0 or more might implicate proteins with low abundance. Thus, the 169 proteins were classified into two groups of PM:CM >1.0 and PM:CM <1.0 in the following analysis.



View larger version (37K):
[in this window]
[in a new window]
 
FIG. 2. Percentage of the proteins annotated or predicted as mitochondrial or nonmitochondrial in the proteins with different PM:CM ratio cut-offs. A, percentage of nonmitochondrial proteins for proteins with ratios of PM:CM <1.0, <0.83, <0.67, and <0.50. B, percentage of 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 location annotation 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.

 
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 subcellular location annotation in Swiss-Prot database. For the 79 proteins with a ratio of PM:CM <1.0, in addition to 20 (25.3%) proteins without subcellular location annotation, 22 (27.8%) of them have been annotated as extracellular or secreted proteins, 9 (11.4%) as endoplasmic reticulum, 6 (7.6%) as cytoplasmic, 5 (6.3%) as peroxisomal, 5 (6.3%) as lysosomal, 4 (5.1%) as ribosomal, 2 (2.5%) as nuclear, and 1 (1.3%) as caveolae, membrane, Golgi complex, and cytoskeletal, respectively (Fig. 3A). Only two multilocation proteins have been associated with mitochondria (Table I). Delta-3,5-{delta}2,4-dienoyl-CoA isomerase with the ratio of PM:CM being 0.22 has been annotated as mitochondrial and peroxisomal, and 14.5-kDa translational inhibitor protein with the ratio of PM:CM being 0.67 has been annotated as mitochondrial, cytoplasmic, and nuclear.



View larger version (25K):
[in this window]
[in a new window]
 
FIG. 3. Summary of the subcellular location 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.

 
On the contrary, for the 90 proteins with a ratio of PM:CM >1.0, 17 (18.9%) proteins have been annotated as mitochondrial, 20 (22.2%) as mitochondrial matrix, 6 (6.7%) as mitochondrial inner membrane, 3 (3.3%) as mitochondrial intermembrane space, and 2 (2.2%) as mitochondrial outer membrane (Fig. 3B). Four mitochondria-associated multilocation proteins have a ratio of PM:CM >1.0 (Table I). Long-chain acyl-CoA synthetase 2 (LACS 2) (1.13 ± 0.03) has been annotated as microsomes, outer mitochondrial membrane, and peroxisomal membrane. Nonspecific lipid-transfer protein (with the ratio of PM:CM being 1.25) has been annotated as cytoplasmic and mitochondrial. Peroxiredoxin 5 (1.42) has been annotated as mitochondrial, peroxisomal, and cytoplasmic. Tyrosine-protein kinase JAK3 (2.61) has been annotated as wholly intracellular, possibly membrane associated.

As expected, all the annotated mitochondrial proteins have a ratio of PM:CM >1.0, indicating their enrichment in PM fraction, while most proteins annotated as other organelles such as endoplasmic reticulum, cytoplasmic, lysosomal, ribosomal, nuclear, Golgi complex, caveolae, cytoskeletal, and membrane, and some apparent contaminant proteins such as some extracellular or secreted proteins, for example, serum albumin (0.30 ± 0.13) and plasminogen (0.49), all have a ratio of PM/CM <1.0, indicating their decrease in PM fraction. Thus, using such a comparative proteomics research, mitochondrial proteins have been effectively distinguished from other contaminant proteins.

Bioinformatics Prediction—
The five bioinformatics tools, PSORT II, SubLoc, TargetP, MitoProtII, and Predotar, have been used to predict subcellular location of the 169 proteins, respectively. For the overview prediction, 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 (Fig. 2, Table I).

For the 79 proteins with a ratio of PM:CM <1.0, PSORT predicted 3.8% (3/79) proteins as mitochondrial and 96.2% (76/79) as nonmitochondrial including 32.9% (26/79) as cytoplasmic, 25.3% (20/79) as extracellular, 19.0% (15/79) as nuclear, 8.9% (7/79) as endoplasmic reticulum, 3.8% (3/79) as Golgi complex, 2.5% (2/79) as peroxisomal, 2.5% (2/79) as plasma membrane, and 1.3% (1/79) as cytoskeletal. SubLoc predicted 12.7% (10/79) proteins as mitochondrial and 87.3% (69/79) as nonmitochondrial including 34.2% (27/79) as cytoplasmic, 30.4% (24/79) as extracellular, and 22.8% (18/79) as nuclear. TargetP predicted 10.1% (8/79) proteins as mitochondrial and 89.9% (71/79) as nonmitochondrial including 51.9% (41/79) as secreted and 38.0% (30/79) as other. MitoProtII and Predotar predicted 21.5% (17/79) and 27.8% (22/79) proteins as mitochondrial in the 79 proteins with a ratio of PM:CM <1.0, respectively. Moreover, for the 79 proteins with a ratio of PM:CM <1.0, 55.7% (44/79) proteins have been predicted as nonmitochondrial by all the five bioinformatics tools (BT = 0), only 8.9% (7/79) proteins have been predicted as mitochondrial by three of the five bioinformatics tools at the same time (BT = 3), and only 2.5% (2/79) proteins predicted as mitochondrial by four of the five bioinformatics tools at the same time (BT = 4) (Table II).


View this table:
[in this window]
[in a new window]
 
TABLE II Evaluation of the efficiency to ascertain mitochondrial proteins by ICAT analysis and a series of bioinformatics tools using the 125 proteins with subcellular location annotation as a test dataset

 
On the other hand, for the 90 proteins with a ratio of PM:CM >1.0, PSORT predicted 34.4% (31/90) proteins as mitochondrial 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, and 1.1% (1/90) as cytoskeletal. SubLoc predicted 47.8% (43/90) proteins as mitochondrial and 52.2% (47/90) as nonmitochondrial including 27.8% (25/90) as cytoplasmic, 10.0% (9/90) as extracellular, and 14.4% (13/90) as nuclear. TargetP predicted 51.1% (46/90) proteins as mitochondrial and 48.9% (44/90) as nonmitochondrial including 8.9% (8/90) as secreted and 40.0% (36/90) as other. MitoProtII and Predotar predicted 60.0% (54/90) and 56.7% (51/90) proteins as mitochondrial in the 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 mitochondrial by at least one bioinformatics tools (BT = 1), and 54.4% (49/90) proteins have been predicted as mitochondrial by at least three of the five bioinformatics tools at the same time (BT = 3), in which 18 proteins have been predicted as mitochondrial by all the five bioinformatics tools (BT = 5) (Table III).

As we can see, the prediction results also indicate the ICAT analysis has effectively distinguished mitochondrial proteins from possible contaminant proteins. At the same time, the difference among the results from the bioinformatics prediction, Swiss-Prot annotation, and ICAT analysis may result from the limitations of the bioinformatics tools (45), protein multilocation, and even the faulty of Swiss-Prot database.

Proteins with Subcellular Location Annotation not in Accordance with ICAT Analysis—
Nine proteins that have been annotated as nonmitochondrial in Swiss-Prot have a ratio of PM:CM >1.0 (Table I). They include quinone oxidoreductase ({zeta}-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), and 2-hydroxyphytanoyl-CoA lyase (2.72), which were annotated as peroxisomal.

Catalase, a scavenger of H 2O 2, has been long known as the most abundant matrix protein within peroxisomes (52). However, the presence of catalase in rat heart mitochondria was demonstrated by biochemical and immunocytochemical analysis (53). Yeast catalase A (Cta1p) contains two peroxisomal targeting signals localized at its carboxyl terminus (SSNSKF) and within the N-terminal third of the protein, which both can target foreign proteins to peroxisomes. It has been more recently demonstrated that Cta1p can also enter mitochondria, although the enzyme lacks a classical mitochondrial import sequence. Peroxisomal and mitochondrial coimport of catalase A were tested qualitatively by fluorescence microscopy and functional complementation of a {Delta}cta1 null mutation, and quantitatively by subcellular fractionation followed by Western analysis and enzyme activity assays (54). More interestingly, in the proteomic analysis of the rat liver peroxisome obtained by differential centrifugation and further purified by density gradient and by immunopurification, according to the SDS-PAGE band intensity, the amount of the most abundant matrix protein, catalase, seemed to decrease, while the band corresponding to another major matrix protein, uricase, clearly increased in its intensity (37). In addition, in the rat liver subcellular fractions obtained with one-step subcellular fractionation using a Nycodenz density gradient prepared by freezing-thawing, catalase has shown more abundance in the mitochondria fraction than in the peroxisome fraction according to Western blotting detection though catalase was used as a peroxisome marker protein (55). In the present study, catalase has been quantified according to two unique peptides and has a ratio of PM:CM as 1.41 ± 0.26 (Supplemental Fig. 1), which confirm its mitochondrial targeting and further implicate its abundance may be higher in rat liver mitochondria than in peroxisome (37, 55).

Mutations of mitochondrial DNA (mtDNA) are associated with different human diseases, including cancer and aging (11, 12). Reactive oxygen species produced during oxidative phosphorylation are a major source of mtDNA damage. APE/Ref-1 is a nuclear protein possessing both redox activity and DNA repair activity over apurinic/apyrimidinic sites. Immunohistochemical evidences indicate that in follicular thyroid cells, APE/Ref-1 is located in both nucleus and cytoplasm. Electronmicroscopy immunocytochemistry performed in the rat thyroid FRTL-5 cell line indicates that part of the cytoplasmic APE/Ref-1 is located in mitochondria. The presence of APE/Ref-1 inside mitochondria is further demonstrated by Western blot analysis after cell fractionation (56). In the present study, the ICAT ratio of PM:CM for AP endonuclease 1 is up to 4.55 (Supplemental Fig. 2), which also indicate its location in mitochondrial and may further implicate its low abundance in mitochondria.

Known Proteins Without Subcellular Location Annotation in the Swiss-Prot Database—
Twelve proteins are known proteins without subcellular location annotation in the Swiss-Prot database (Table I). Fortunately, many of them have been reported about their subcellular locations that are consistent with our ICAT analysis results (5760). For example, bile acid CoA:amino acid N-acyltransferase (BAT) is responsible for the amidation of bile acids with the amino acids taurine and glycine. Immunoblot analysis of rat tissues detected rat liver BAT (rBAT) only in rat liver cytosol prepared with homogenization and ultracentrifugation. Subcellular localization of rBAT detected activity and immunoreactive protein in both cytosol and isolated peroxisomes (57). Rat bile acid CoA ligase (rBAL), the enzyme responsible for the formation of bile acid CoA esters, was detected only in rat liver microsomes (57). In the present study, bile acid CoA ligase and bile acid-CoA:amino acid N-acyltransferase have a ratio of PM:CM as 0.66 ± 0.09 and 0.71 ± 0.25, respectively.

Novel Proteins—
Thirty-seven novel proteins have been identified in the ICAT analysis. SIB BLAST2 Network Service (us.expasy.org/tools/blast/) was used for novel protein blast against the UniProt knowledgebase (Swiss-Prot + TrEMBL + TrEMBL_NEW). Twenty-one novel proteins show high identities (>70%) with known proteins, respectively, most of which with subcellular location annotation consistent with ICAT analysis results (Supplemental Table II). For example, novel protein 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 protein. Protein XP_224605.1, predicted as mitochondrial protein by all the five bioinformatics tools, with a ratio of PM:CM being 2.37, has been just confirmed as choline dehydrogenase and localized in mitochondrial (61). The novel protein XP_214838.1 predicted as mitochondrial protein by four bioinformatics tools, with ratio of PM:CM being 1.96, shows 88% identities with human HSCO protein, which has just been renamed as "ETHE1" and localized in mitochondrial matrix (62).

Thirteen novel proteins have a ratio of PM:CM <1.0, in which 10 (76.9% ,10/13) proteins have been predicted as nonmitochondrial by all the five bioinformatics tools (BT = 0) and are strongly implicated as nonmitochondrial proteins (Table II). For the 24 novel proteins with a ratio of PM:CM >1.0, 14 (58.3%) proteins have been predicted as mitochondrial by at least one bioinformatics tools (BT = 1), in which 7 (29.2%) proteins have been predicted as mitochondrial by at least three bioinformatics tools.

Functional Classification
The 169 proteins are categorized according to Swiss-Prot functional annotation (Fig. 4). As we know, many physiological activities such as amino acid metabolism, fatty acid metabolism, glycolysis, urea cycle, transcription, and replication are fulfilled in or associated with multi-organelles including mitochondria. It is not surprising to find proteins involved in those activities distribute in the two groups of proteins with a ratio of PM:CM <1.0 or >1.0. As expected, proteins involved in the oxidative phosphorylation (such as ATP synthase D chain and {gamma} chain, NADH-ubiquinone oxidoreductase 13-kDa-A subunit and 24-kDa subunit, ubiquinol-cytochrome c reductase complex 11-kDa protein and core protein 2, electron transfer flavoprotein-ubiquinone oxidoreductase, and electron transfer flavoprotein {alpha}-subunit), the tricarboxylic acid (TCA) cycle (such as succinyl-CoA ligase {alpha}-chain and ß-chain, isocitrate dehydrogenase, and malate dehydrogenase), and ketone body metabolism (such as HMG-CoA synthase and acetoacetyl-CoA thiolase), all have a ratio of PM:CM >1.0 (Fig. 4B). On the other hand, proteins that function in interactions between cells and the extracellular matrix (such as {alpha}-1 catenin, sulfated glycoprotein 1 precursor (SGP-1), fibronectin, thrombospondin 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 acid N-acyltransferase), and ribosomal proteins (such as 60S ribosomal protein L10a (CSA-19), L30, L12, and 60S acidic ribosomal protein P1), all have a ratio of PM:CM <1.0 (Fig. 4A).



View larger version (34K):
[in this window]
[in a new window]
 
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.

 
As we can see, in accordance with multifunction of mitochondria (7–13), proteins with a ratio of PM:CM >1.0 include many proteins that function in amino acid metabolism, fatty acid metabolism, glycolysis, the oxidative phosphorylation, the TCA cycle, ketone body metabolism, urea cycle, and transcription and replication. Moreover, many novel proteins have been implicated their mitochondrial location.

Evaluation of the Efficiency to Ascertain Mitochondrial Proteins by ICAT Analysis and a Series of Bioinformatics Tools
The 125 proteins with subcellular location information have been used as a test dataset to evaluate the efficiency to ascertain mitochondrial proteins by ICAT analysis and the five bioinformatics tools (Table II). When PSORT II and SubLoc are used in the winner-takes-all mode without setting a specificity cut-off for targeting, the sensitivity and specificity to predict mitochondrial proteins is 0.50 and 0.89 for PSORT and 0.60 and 0.67 for SubLoc, respectively. Based on a probability cut-off of >0.50, the sensitivity and specificity to predict mitochondrial proteins is 0.73 and 0.76 for TargetP (mTP), 0.83 and 0.70 for MitoProtII, and 0.73 and 0.60 for Predotar, respectively. Moreover, TargetP (mTP) and MitoProtII show increased specificity based on the probability cut-off of >0.70 or >0.85, while Predotar shows low specificity 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 proteins is 1.00 and 0.79, respectively, almost higher than every bioinformatics tools. Moreover, according to the ratio of PM:CM <1.0, the sensitivity and specificity to ascertain nonmitochondrial proteins is 0.90 and 0.97, respectively, also higher than every bioinformatics tools based on a probability cut-off of <0.50. So ICAT analysis, as a high-throughput proteomics experimental strategy, has shown great superiority in ascertaining mitochondrial proteins than the most widely used bioinformatics tools such as PSORT, SubLoc, TargetP, MitoProtII, and Predotar.

The different sensitivity and specificity of the bioinformatics tools would favor in combinational usage of those bioinformatics tools to predict mitochondrial proteins. In the test dataset, prediction based on at least one bioinformatics tool (BT = 1) shows sensitivity high up to 0.88 with specificity as 0.53. At the same time, the more bioinformatics tools used combinationally, the higher specificity for the prediction of mitochondrial proteins. For prediction based on all the five bioinformatics tools (BT = 5), the specificity is high up to 1.00. Interestingly, when in combination with the ICAT analysis, the specificity increased from 0.53 to 0.86 for the prediction based on at least one bioinformatics tool (BT = 1).


    DISCUSSION
 TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
More recently, the combination of LC, stable ICAT, and MS/MS has emerged as an alternative quantitative proteomics technology (47). In ICAT analysis, two pools of proteins, labeled with light and heavy reagent, respectively, are chemically identical and therefore serve as a good internal stand for accurate quantification. The method has been proved complementary to traditional 2D-PAGE (63, 64) and widely applied in comparative proteomics research such as quantification of microsomal proteins in differentiated versus undifferentiated HL-60 cells and quantification of protein expression in rat myc-null cells versus myc-plus cells (48, 65).

In the present study, we apply such a high-throughput comparative proteomic experimental strategy, the ICAT technique, to analyze rat liver mitochondria fractions with different degrees of purity, prepared with traditional centrifugation or further purified with a Nycodenz gradient, in the aim to ascertain mitochondrial proteins and distinguish contaminant proteins.

A total of 169 different proteins were identified and quantified convincingly. Ninety proteins have a ratio of PM:CM >1.0, while 79 proteins have a ratio of PM:CM <1.0. According to Swiss-Prot annotation, bioinformatics prediction, and literature reports, almost all the proteins with a ratio of PM:CM >1.0 are mitochondrial proteins, while proteins annotated as extracellular or secreted, cytoplasmic, ribosome, endoplasmic reticulum, and lysomal have a ratio of PM:CM <1.0 (Figs. 2 and 3; Table I). Thus, such a comparative proteome experimental strategy has been proven effective in ascertaining mitochondrial proteins in a high-throughput way. Many novel proteins, known proteins without subcellular location annotation and even known proteins that have been annotated as other locations have been strongly indicated for their mitochondrial location. Especially, protein catalse and AP endonuclease 1, which have been known as peroxisomal and nuclear, respectively, have shown a ratio of PM:CM >1.0 (1.41 ± 0.26 and 4.55, respectively) (Supplemental Figs. 1 and 2), confirming the reports about their mitochondrial location (37, 5356). Functional study of those proteins will promote our understanding on mitochondria structure and function.

In all eukaryotic cells, peroxisomes and mitochondria share a great variety of enzymatic reactions that are catalyzed by isozymes present in both organelles. For some of these enzymes it is known that they can be cotargeted to different organelles, for example, {delta}3,5-{delta}2,4-dienoyl-CoA isomerase and long-chain acyl-CoA synthetase 2 (LACS 2), which are responsible for fatty acid ß-oxidation, have been annotated as multilocation in both mitochondria and peroxisome. A most recent example for such a cotargeting has been given for the yeast peroxisomal citrate synthase Cit2p, an enzyme of the TCA cycle that contains a cryptic amino-terminal signal sequence that functions in both peroxisomal and mitochondrial targeting (66). Besides enzymes that catalyze related reactions within the fatty acid ß-oxidation, TCA, and the glyoxylate cycle, enzymes involved in the detoxification of oxygen radicals are also present in both peroxisome and mitochondria. Peroxiredoxins (Prxs) form a recently discovered large family of antioxidant enzymes that act as peroxidases reducing hydrogen peroxide and alkyl hydroperoxides to water or the corresponding alcohol, respectively (67). Peroxiredoxin 5 has been annotated as mitochondrial, peroxisomal, and cytoplasmic. In the present study, in addition to catalase, ATP-binding cassette subfamily D member 3 (1.75), peroxisomal multifunctional enzyme type 2 (MFE-2) (1.46 ± 0.37), NADP-retinol dehydrogenase (1.38 ± 0.03), and 2-hydroxyphytanoyl-CoA lyase (2.72), which were annotated as peroxisomal, all have a ratio of PM:CM >1.0 and have been implicated their location in mitochondria as multilocation proteins.

Comparison of the results from the five bioinformatics tools and Swiss-Prot annotation and ICAT analysis have shown the limitations of the bioinformatics tools (45) and even the faulty nature of Swiss-Prot annotation (Fig. 2, Table II). The ICAT analysis coupled with combinational usage of different bioinformatics tools can effectively ascertain mitochondrial proteins with high sensitivity and specificity (Table II). Moreover, the inconsistence between ICAT analysis and bioinformatics prediction could implicate mitochondria-associated multilocation. For example, in the present study, {delta}3,5-{delta}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 and four bioinformatics tools, respectively. AP endonuclease 1 and catalase, which have been predicted as nonmitochondrial proteins by all five bioinformatics tools, have a ratio of PM:CM as 4.55 and 1.41 ± 0.26, respectively. All four proteins have been annotated as mitochondria-associated multilocation proteins according to Swiss-Prot database or literature reports (37, 5356).

In summary, we have applied a high-throughput comparative proteome experimental strategy, the ICAT approach performed with 2D-LC-MS/MS, coupled with combinational usage of different bioinformatics tools, to study the proteome of rat liver mitochondria, with the major problem of contamination in subcellular proteomics research effectively circumvented. Concerning the limitation of the ICAT approach for analyzing proteins lacking cysteine residues, acidic proteins, or proteins with low molecular mass (63, 64), other comparative proteomics approaches, such as traditional 2D-PAGE, DIGE (68, 69), and stable isotope labeling with amino acids in cell culture (SILAC) (70, 71), should be used as complementary methods. Such a comparative proteomics strategy should be widely used in subcellular proteomics research to provide more subcellular proteome data with high quality.


    FOOTNOTES
 
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

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

* This work was supported by National High-Technology Project (2002BA711A11) and Basic Research Foundation (2001CB210501, 2002CB713807). Back

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

{ddagger} To whom correspondence should be addressed: Research Centre for Proteome Analysis, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 YueYang Road, Shanghai 200031, China. Tel.: 86-21-54920170; Fax: 86-21-54920171; E-mail: zr{at}sibs.ac.cn


    REFERENCES
 TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Jung, E., Heller, M., Sanchez, J. C., and Hochstrasser, D. F. (2003) Proteomics meets cell biology: The establishment of subcellular proteomes. Electrophoresis 21, 3369 –3377[CrossRef]

  2. Dreger, M. (2003) Subcellular proteomics. Mass Spectrom. Rev. 22, 27 –56[CrossRef][Medline]

  3. Dreger, M. (2003) Proteome analysis at the level of subcellular structures. Eur. J. Biochem. 270, 589 –599[Abstract/Free Full Text]

  4. Huber, L. A., Pfaller, K., and Vietor, I. (2003) Organelle proteomics: Implications for subcellular fractionation in proteomics. Circ. Res. 16, 962 –968[CrossRef]

  5. Taylor, S. W., Fahy, and E., Ghosh, S. S. (2003) Global organellar proteomics. Trends Biotechnol. 21, 82 –88[CrossRef][Medline]

  6. Brunet, S., Thibault, P., Gagnon, E., Kearney, P., Bergeron, J. J., and Desjardins, M. (2003) Organelle proteomics: Looking at less to see more. Trends Cell Biol. 13, 629 –638[CrossRef][Medline]

  7. Mootha, V. K., Bunkenborg, J., Olsen, J. V., Hjerrild, M., Wisniewski, J. R., Stahl, E., Bolouri, M. S., Ray, H. N., Sihag, S., Kamal, M., Patterson, N., Lander, E. S., and Mann, M. (2003) Integrated analysis of protein composition, tissue diversity, and gene regulation in mouse mitochondria. Cell 115, 629 –640[CrossRef][Medline]

  8. Fossati, G., Moulding, D. A., Spiller, D. G., Moots, R. J., White, M. R., and Edwards, S. W. (2003) The mitochondrial network of human neutrophils: Role in chemotaxis, phagocytosis, respiratory burst activation, and commitment to apoptosis. J. Immunol. 170, 1964 –1972[Abstract/Free Full Text]

  9. La Piana, G., Marzulli, D., Consalvo, M. I., and Lofrumento, N. E. (2003) Cytochrome c-induced cytosolic nicotinamide adenine dinucleotide oxidation, mitochondrial permeability transition, and apoptosis. Arch. Biochem. Biophys. 410, 201 –211[CrossRef][Medline]

  10. Jambrina, E., Alonso, R., Alcalde, M., Rodriguez, M. M., Serrano, A., Martinez-A. C., Garcia-Sancho, J., and Izquierdo, M. (2003) Calcium influx through receptor-operated channel induces mitochondria-triggered paraptotic cell death. J. Biol. Chem. 278, 14134 –14145[Abstract/Free Full Text]

  11. Bota, D. A., and Davies, K. J. A. (2001) Protein degradation in mitochondria: Implications for oxidative stress, aging, and disease: A novel etiological classification of mitochondrial proteolytic disorders. Mitochondrion 1, 33 –49[CrossRef]

  12. Attardi, G. (2002) Role of mitochondrial DNA in human aging. Mitochondrion 2, 27 –37[CrossRef]

  13. Sun, J., Folk, D., Bradley, T. J., and Tower, J. (2002) Induced overexpression of mitochondrial Mn-superoxide dismutase extends the life span of adult Drosophila melanogaster. Genetics 161, 661 –672[Abstract/Free Full Text]

  14. Wallace, D. C. (1999) Mitochondrial diseases in man and mouse. Science 283, 1482 –1488[Abstract/Free Full Text]

  15. Swerdlow, R. H., and Kish, S. J. (2002) Mitochondria in Alzheimer’s disease. Int. Rev. Neurobiol. 53, 341 –385[Medline]

  16. Sherer, T. B., Betarbet, R., and Greenamyre, J. T. (2002) Environment, mitochondria, and Parkinson’s disease. Neuroscientist 8, 192 –197[Abstract/Free Full Text]

  17. Pandolfo, M. (2002) Iron metabolism and mitochondrial abnormalities in Friedreich ataxia. Blood Cells Mol. Dis. 29, 536 –547[CrossRef][Medline]

  18. Maassen, J. A. (2002) Mitochondrial diabetes: Pathophysiology, clinical presentation, and genetic analysis. Am. J. Med. Genet. 115, 66 –70[CrossRef][Medline]

  19. De Marcos Lousa, C., Trezeguet, V., Dianoux, A. C., Brandolin, G., and Lauquin, G. J. (2002) The human mitochondrial ADP/ATP carriers: Kinetic properties and biogenesis of wild-type and mutant proteins in the yeast S. cerevisiae. Biochemistry 41, 14412 –14420[CrossRef][Medline]

  20. Rabilloud, T., Strub, J. M., Carte, N., Luche, S., Van Dorsselaer, A., Lunardi, J., Giege, R., and Florentz, C. (2002) Comparative proteomics as a new tool for exploring human mitochondrial tRNA disorders. Biochemistry 41, 144 –150[CrossRef][Medline]

  21. Chinnery, P. F. (2002) Inheritance of mitochondrial disoders. Mitochondrion 2, 149 –155[CrossRef]

  22. Tomlinson, I. P., Alam, N. A., Rowan, A. J., Barclay, E., Jaeger, E. E., Kelsell, D., Leigh, I., Gorman, P., Lamlum, H., Rahman, S., Roylance, R. R., Olpin, S., Bevan, S., Barker, K., Hearle, N., Houlston, R. S., Kiuru, M., Lehtonen, R., Karhu, A., Vilkki, S., Laiho, P., Eklund, C., Vierimaa, O., Aittomaki, K., Hietala, M., Sistonen, P., Paetau, A., Salovaara, R., Herva, R., Launonen, V., and Aaltonen, L. A. (2002) The Multiple Leiomyoma Consortium. Germline mutations in FH predispose to dominantly inherited uterine fibroids, skin leiomyomata and papillary renal cell cancer. Nat. Genet. 30, 406 –410[CrossRef][Medline]

  23. Chinnery, P. F., Howell, N., Andrews, R. M., and Turnbull, D. M. (1999) Clinical mitochondrial genetics. J. Med. Genet. 36, 425 –436[Abstract/Free Full Text]

  24. Terkeltaub, R., Johnson, K., Murphy, A., and Ghosh, S. (2002) The mitochondrion in osteoarthritis. Mitochondrion 1, 301 –309[CrossRef]

  25. Fountoulakis, M., Berndt, P., Langen, H., and Suter, L. (2002) The rat liver mitochondrial proteins. Electrophoresis 23, 311 –328[CrossRef][Medline]

  26. Ohlmeier, S., Kastaniotis, A. J., Hiltunen, J. K., and Bergmann, U. (2004) The yeast mitochondrial proteome, a study of fermentative and respiratory growth. J. Biol. Chem. 279, 3956 –3979[Abstract/Free Full Text]

  27. Taylor, S. W., Fahy, E., Zhang, B., Glenn, G. M., Warnock, D. E., Wiley, S., Murphy, A. N., Gaucher, S. P., Capaldi, R. A., Gibson, B. W., and Ghosh, S. S. (2003) Characterization of the human heart mitochondrial proteome. Nat. Biotechnol. 21, 281 –286[CrossRef][Medline]

  28. Sickmann, A., Reinders, J., Wagner, Y., Joppich, C., Zahedi, R., Meyer, H. E., Schonfisch, B., Perschil, I., Chacinska, A., Guiard, B., Rehling, P., Pfanner, N., and Meisinger, C. (2003) The proteome of Saccharomyces cerevisiae mitochondria. Proc. Natl. Acad. Sci. U S A. 100, 13207 –13212[Abstract/Free Full Text]

  29. Jiang, X. S., Zhou, H., Zhang, L., Sheng, Q. H., Li, S. J., Li, L., Hao. P., Li. Y. X., Xia, Q. C., Wu, J. R., and Zeng, R. (2004) A High-throughput approach for subcellular proteome: Identification of rat liver proteins using subcellular fractionation coupled with two-dimensional liquid chromatography tandem mass spectrometry and bioinformatic analysis. Mol. Cell. Proteomics 3, 441 –455[Abstract/Free Full Text]

  30. Heazlewood, J. L., Howell, K. A., Whelan, J., and Millar, A. H. (2003) Towards an analysis of the rice mitochondrial proteome. Plant Physiol. 132, 230 –242[Abstract/Free Full Text]

  31. Bardel, J., Louwagie, M., Jaquinod, M., Jourdain, A., Luche, S., Rabilloud, T., Macherel, D., Garin, J., and Bourguignon, J. (2002) A survey of the plant mitochondrial proteome in relation to development. Proteomics 2, 880 –898[CrossRef][Medline]

  32. Millar, A. H., Sweetlove, L. J., Giege, P., and Leaver, C. J. (2001) Analysis of the Arabidopsis mitochondrial proteome. Plant Physiol. 127, 1711 –1727[Abstract/Free Full Text]

  33. Gibson, B. W. (2004) Exploiting proteomics in the discovery of drugs that target mitochondrial oxidative damage. Sci. Aging Knowledge Environ. 17, pe12

  34. Rappsilber, J., Ryder, U., Lamond, A. I., and Mann, M. (2002) Large-scale proteomic analysis of the human spliceosome. Genome Res. 12, 1231 –1245[Abstract/Free Full Text]

  35. Dziembowski, A., and Seraphin, B. (2004) Recent developments in the analysis of protein complexes. FEBS Lett. 556, 1 –6[CrossRef][Medline]

  36. Dunkley, T. P., Dupree, P., Watson, R. B., and Lilley, K. S. (2004) The use of isotope-coded affinity tags (ICAT) to study organelle proteomes in Arabidopsis thaliana. Biochem. Soc. Trans. 32, 520 –523[CrossRef][Medline]

  37. Kikuchi, M., Hatano, N., Yokota, S., Shimozawa, N., Imanaka, T., and Taniguchi, H. (2004) Proteomic analysis of rat liver peroxisome: presence of peroxisome-specific isozyme of Lon protease. J. Biol. Chem. 279, 421 –428[Abstract/Free Full Text]

  38. Zischka, H., Weber, G., Weber, P. J., Posch, A., Braun, R. J., Buhringer, D., Schneider, U., Nissum, M., Meitinger, T., Ueffing, M., and Eckerskorn, C. (2003) Improved proteome analysis of Saccharomyces cerevisiae mitochondria by free-flow electrophoresis. Proteomics 3, 906 –916[CrossRef][Medline]

  39. Murphy, R. F., Boland, M. V., and Velliste, M. (2000) Towards A systematics for protein subcellular location: Quantitative description of protein localization patterns and automated analysis of fluorescence microscope images. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8, 251 –259[Medline]

  40. Emanuelsson, O., and Heijne, G. V. (2001) Prediction of organellar targeting signals. Biochim. Biophys. Acta 1541, 114 –119[CrossRef][Medline]

  41. Nakai, K., and Horton, P. (1999) PSORT: A program for detecting sorting signals in proteins and predicting their subcellular localization. Trends Biochem. Sci. 24, 34 –35[CrossRef][Medline]

  42. Emanuelsson, O., Nielsen, H., Brunak, S., and von Heijne, G. (2000) Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J. Mol. Biol. 300, 1005 –1016[CrossRef][Medline]

  43. Hua, S., and Sun, Z. (2001) Support vector machine approach for protein subcellular localization prediction. Bioinformatics 17, 721 –728[Abstract/Free Full Text]

  44. Claros, M. G., and Vincens, P. (1996) Computational method to predict mitochondrially imported proteins and their targeting sequences. Eur. J. Biochem. 241, 779 –786[Abstract]

  45. Fen, Z. P. (2002) An overview on predicting the subcellular location of a protein. In Silico Biol. 2, 291 –303[Medline]

  46. Nakai, K. (2001) Review: Prediction of in vivo fates of proteins in the era of genomics and proteomics. J. Struct. Biol. 134, 103 –116[CrossRef][Medline]

  47. Gygi, S. P., Rist, B., Gerber, S. A., Turecek, F., Gelb, M. H., and Aebersold, R. (1999) Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 17, 994 –999[CrossRef][Medline]

  48. Han, D. K., Eng, J., Zhou, H., and Aebersold, R. (2001) Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry. Nat. Biotechnol. 19, 946 –951[CrossRef][Medline]

  49. Zhou, H., Ranish, J. A., Watts, J. D., and Aebersold, R. (2002) Quantitative proteome analysis by solid-phase isotope tagging and mass spectrometry. Nat. Biotechnol. 20, 512 –515[CrossRef][Medline]

  50. Krogh, A., Larsson, B., von Heijne, G., and Sonnhammer, E. L. L. (2001) Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes. J. Mol. Biol. 305, 567 –580[CrossRef][Medline]

  51. Kyte, J., and Doolittle, R. F. (1982) A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157, 105 –132[CrossRef][Medline]

  52. Chance, B., Sies, H., and Boveris, A. (1979) Hydroperoxide metabolism in mammalian organs. Physiol. Rev. 59, 527 –605[Free Full Text]

  53. Radi, R., Turrens, J. F., Chang, L. Y., Bush, K. M., Crapo, J. D., and Freeman, B. A. (1991) Detection of catalase in rat heart mitochondria. J. Biol. Chem. 266, 22028 –22034[Abstract/Free Full Text]

  54. Petrova, V. Y., Drescher, D., Kujumdzieva, A. V., and Schmitt, M. J. (2004) Dual targeting of yeast catalase A to peroxisomes and mitochondria. Biochem. J. 380, 393 –400[CrossRef][Medline]

  55. Murayama, K., Fujimura, T., Morita, M., and Shindo, N. (2001) One-step subcellular fractionation of rat liver tissue using a Nycodenz density gradient prepared by freezing-thawing and two-dimensional sodium dodecyl sulfate electrophoresis profiles of the main fraction of organelles. Electrophoresis 22, 2872 –2880[CrossRef][Medline]

  56. Tell, G., Crivellato, E., Pines, A., Paron, I., Pucillo, C., Manzini, G., Bandiera, A., Kelley, M. R., Di Loreto, C., and Damante, G. (2001) Mitochondrial localization of APE/Ref-1 in thyroid cells. Mutat. Res. 485, 143 –152[Medline]

  57. He, D., Barnes, S., and Falany, C. N. (2003) Rat liver bile acid CoA:amino acid N-acyltransferase: Expression, characterization, and peroxisomal localization. J. Lipid Res. 44, 2242 –2249[Abstract/Free Full Text]

  58. Sagara, Y., Ishida, C., Inoue, Y., Shiraki, H., and Maeda, Y. (1998) 71-kilodalton heat shock cognate protein acts as a cellular receptor for syncytium formation induced by human T-cell lymphotropic virus type 1. J. Virol. 72, 535 –541[Abstract/Free Full Text]

  59. Friedman, J., Trahey, M., and Weissman, I. (1993) Cloning and characterization of cyclophilin C-associated protein: A candidate natural cellular ligand for cyclophilin C. Proc. Natl. Acad. Sci. U S A. 90, 6815 –6819[Abstract/Free Full Text]

  60. Davis, P. K., and Wu, G. (1998) Compartmentation and kinetics of urea cycle enzymes in porcine enterocytes. Comp. Biochem. Physiol. B. Biochem. Mol. Biol. 119, 527 –37[CrossRef][Medline]

  61. Huang, S., and Lin, Q. (2003) Functional expression and processing of rat choline dehydrogenase precursor. Biochem. Biophys. Res. Commun. 309, 344 –350[CrossRef][Medline]

  62. Tiranti, V., D’Adamo, P., Briem, E., Ferrari, G., Mineri, R., Lamantea, E., Mandel, H., Balestri, P., Garcia-Silva, M. T., Vollmer, B., Rinaldo, P., Hahn, S. H., Leonard, J., Rahman, S., Dionisi-Vici, C., Garavaglia, B., Gasparini, P., and Zeviani, M. (2004) Ethylmalonic encephalopathy is caused by mutations in ETHE1, a gene encoding a mitochondrial matrix protein. Am. J. Hum. Genet. 74, 239 –252[CrossRef][Medline]

  63. Schmidt, F., Donahoe, S., Hagens, K., Mattow, J., Schaible, U. E., Kaufmann, S. H., Aebersold, R., and Jungblut, P. R. (2004) Complementary analysis of the Mycobacterium tuberculosis proteome by two-dimensional electrophoresis and isotope-coded affinity tag technology. Mol. Cell. Proteomics 3, 24 –42[Abstract/Free Full Text]

  64. Patton, W. F., Schulenberg, B., and Steinberg, T. H. (2002) Two-dimensional gel electrophoresis: Better than a poke in the ICAT? Curr. Opin. Biotechnol. 13, 321 –328[CrossRef][Medline]

  65. Shiio, Y., Donohoe, S., Yi, E. C., Goodlett, D. R., Aebersold, R., and Eisenman, R. N. (2002) Quantitative proteomic analysis of Myc oncoprotein function. EMBO J. 21, 5088 –5096[Abstract/Free Full Text]

  66. Lee, J. G., Cho, S. P., Lee, H. S., Lee, C. H., Bae, K. S., and Maeng, P. J. (2000) Identification of a cryptic N-terminal signal in Saccharomyces cerevisiae peroxisomal citrate synthase that functions in both peroxisomal and mitochondrial targeting. J. Biochem. 128, 1059 –1072[Abstract]

  67. Schroder, E., and Pointing, C. P. (1998) Evidence that peroxiredoxins are novel members of the thioredoxin fold superfamily. Protein Sci. 7, 2465 –2468[Abstract/Free Full Text]

  68. Unlu, M., Morgan, M. E., and Minden, J. S. (1997) Difference gel electrophoresis: A single gel method for detecting changes in protein extracts. Electrophoresis 18, 2071 –2077[Medline]

  69. Lilley, K. S., Razzaq, A., and Dupree, P. (2002) Two-dimensional gel electrophoresis: Recent advances in sample preparation, detection and quantitation. Curr. Opin. Chem. Biol. 6, 46 –50[CrossRef][Medline]

  70. Ong, S. E., Blagoev, B., Kratchmarova, I., Kristensen, D. B., Steen, H., Pandey, A., and Mann, M. (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1, 376 –386[Abstract/Free Full Text]

  71. Ong, S. E., Foster, L. J., and Mann, M. (2003) Mass spectrometric-based approaches in quantitative proteomics. Methods 29, 124 –130[CrossRef][Medline]