Advertisement
MCP
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Originally published In Press as doi:10.1074/mcp.M700261-MCP200 on October 12, 2007.
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Supplemental Data
Right arrow All Versions of this Article:
M700261-MCP200v1
7/1/46    most recent
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Glossary
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Wang, X.
Right arrow Articles by Huang, L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Wang, X.
Right arrow Articles by Huang, L.
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?

Molecular & Cellular Proteomics 7:46-57, 2008.
© 2008 by The American Society for Biochemistry and Molecular Biology, Inc.

Research

Identifying Dynamic Interactors of Protein Complexes by Quantitative Mass Spectrometry*,S

Xiaorong Wang and Lan Huang{ddagger}

From the Departments of Physiology & Biophysics and Developmental & Cell Biology, University of California, Irvine, California 92697-4560


   ABSTRACT
TOP
 ABSTRACT
EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 
Dynamically interacting proteins associate and dissociate with their binding partners at high on/off rates. Although their identification is of great significance to proteomics research, lack of an efficient strategy to distinguish stable and dynamic interactors has hampered the efforts toward this goal. In this work, we developed a new method, MAP (mixing after purification)-SILAC (stable isotope labeling of amino acids in cell culture), to quantitatively investigate the interactions of protein complexes by mass spectrometry. In combination with the original SILAC approach, stable and dynamic components were effectively distinguished by the differences in their relative abundance ratio changes. We applied the newly developed strategies to decipher the dynamics of the human 26 S proteasome-interacting proteins. A total of 67 putative human proteasome-interacting proteins were identified by the MAP-SILAC method among which 14 proteins would have been misidentified as background proteins due to low relative abundance ratios in standard SILAC experiments and 57 proteins have not been reported previously. In addition, 35 of the 67 proteins were classified as stable interactors of the proteasome complex, whereas 16 of them were identified as dynamic interactors. The methods reported here provide a valuable expansion of proteomics technologies for identification of important but previously unidentifiable interacting proteins.

Protein-protein interactions are one of the major mechanisms for controlling protein functions in various cellular processes. To fully understand these functions, global mapping of protein-protein interactions has become a major goal in current proteomics research. MS in combination with affinity purification in particular has evolved as a powerful tool for deciphering protein interaction networks at the proteome level (1–6). Although conventional affinity purification can preserve stable interactions under native conditions, we have shown recently that affinity purification coupled with in vivo cross-linking can extend identification of interacting proteins by capturing weak affinity or transient interactors (7). In contrast to stably interacting proteins, dynamically interacting proteins associate and dissociate with their binding partners at high on/off rates and are likely to be more susceptible to rapid regulation in response to cellular cues. Their identification is of great significance to proteomics research but is considerably challenging as efficient strategies to distinguish between stable and dynamic components of protein complexes are currently lacking. A new development that adds the ability to describe protein complex dynamics is thus needed for the advancement of interactive proteomics.

Identification of specific protein interactions has been successfully achieved by quantitative MS using stable isotope labeling such as the stable isotope labeling of amino acids in cell culture (SILAC)1 strategy, which allows effective discrimination against purification backgrounds (7–11). The approach relies on the relative abundance ratios measured by MS of proteins detected after affinity purification of a tagged bait protein compared with control samples. The abundance of a bona fide interaction partner in a sample containing a tagged bait is significantly greater than in a control sample; the comparable ratios of nonspecifically bound background proteins are close to 1. However, we recently noticed in our studies using the SILAC strategy that this mechanism is generally valid for stable interactions but not necessarily for dynamic interactions. Typical SILAC protocols involve metabolic labeling of cells, cell lysis, lysate mixing, and purification prior to mass spectrometric analysis of the resulting samples (11, 12). In this process, protein purification is carried out after mixing the cell lysates from two types of cells that have been differentially labeled. Because all proteins are present in both the light and heavy labeled forms during the purification, the fast on/off rates of dynamically interacting proteins will result in an equilibrium between the two forms of the proteins that are bound to the bait. The relative abundance ratios of these interactors will be similar to those of background proteins after a certain time interval that depends on the kinetic parameters of the individual binding partners. As a result, specific but dynamic interactors cannot be effectively distinguished from background proteins through the relative abundance ratio measurements with the standard SILAC approach.

We report here a new mass spectrometry-based quantitative strategy to efficiently identify dynamic components of multiprotein complexes. To evaluate this strategy, we chose to investigate the dynamics of the human 26 S proteasome-interacting proteins (PIPs). The 26 S proteasome is a multicatalytic proteinase complex responsible for ubiquitin/ATP-dependent degradation of most intracellular proteins, including proteins crucial to cell cycle regulation, programmed cell death, or apoptosis (13). Recognition and degradation of ubiquitinated substrates by the 26 S proteasome is tightly regulated to maintain normal cell growth. A global map of the human 26 S proteasome interacting network will help to further understand mechanisms of function and regulation of the human 26 S proteasome complexes. Studies in yeast that primarily focused on capturing and identifying proteins interacting with the proteasome have already demonstrated the value of this approach (7, 14, 15). Similar studies in higher eukaryotes are necessary to expand our understanding of the human proteasome.


   EXPERIMENTAL PROCEDURES
TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
RESULTS
 DISCUSSION
 REFERENCES
 
Chemicals and Reagents—
ImmunoPure streptavidin, HRP-conjugated antibody, and Super Signal West Pico chemiluminescent substrate were from Pierce. Sequencing grade trypsin was purchased from Promega Corp. (Madison, WI). The culture medium, EMEM (deficient in lysine and arginine), was obtained from Sigma. [13C615N4]Arginine and [13C615N2]lysine were purchased from Cambridge Isotope Laboratories (Andover, MA). [12C614N4]Arginine and [12C614N2]lysine were from Sigma. Anti-Rpt6 and anti-{alpha}7 were purchased from BioMol. Anti-{alpha}6 was a generous gift from Dr. Klavs B. Hendil. Anti-FLAG and anti-HA were bought from Sigma. All other general chemicals for buffers and cell culture media were purchased from Fisher or VWR.

Cell Culture and Metabolic Stable Isotope Labeling Using SILAC—
The stable cell line expressing Rpn11-HTBH (16) was grown in EMEM supplemented with 28 µg/ml [12C614N4]arginine, 73 µg/ml [12C614N2]lysine, 10% fetal bovine serum, and 1% penicillin/streptomycin (light medium). The stable cell line expressing HTBH was grown in EMEM supplemented with 28 µg/ml [13C615N4]arginine, 73 µg/ml [13C615N2]lysine, 10% fetal bovine serum, and 1% penicillin/streptomycin (heavy medium). Cell lines were grown for more than seven cell doublings in the labeling media to ensure complete labeling. The cells were then grown to confluence prior to cell lysis as described previously (16).

PAM (Purification after Mixing)-SILAC and MAP (Mixing after Purification)-SILAC Experiments—
For PAM-SILAC experiments, equal amounts of cell lysates from Rpn11-HTBH-expressing cells and HTBH-expressing cells were mixed. After mixing, affinity purification was carried out by binding to streptavidin resin followed by TEV elution (16). In addition to the proteasome complexes, their interacting partners that survive during the purification will be captured. In the PAM-SILAC experiment, the optimal incubation time, i.e. 2 h, was used, whereas in time-controlled (Tc)-PAM-SILAC experiments, two additional incubation times (20 min and 1 h) were utilized. The purified complexes were precipitated by 25% TCA for subsequent analysis (16). For MAP-SILAC experiments, purification of the samples was carried out individually under the same experimental conditions, and a 2-h incubation time was chosen. After purification, the isolated protein complexes were mixed and precipitated by 25% TCA. For each experiment, 10 150-mm plates of each type of cells were used. Each experiment was performed at least twice, and the results were reproducible.

Mass Spectrometric Analysis-LC MS/MS—
The pellets after TCA precipitation were resolubilized in 25 µl of 50 mM NH4HCO3 with 8 M urea and digested with endopeptidase Lys-C and then trypsin as described previously (16). The two-dimensional LC MS/MS analysis of the digested mixture was similar to that described previously using a quadrupole-orthogonal-time-of-flight tandem mass spectrometer (QSTAR XL, Applied Biosystems/MDS Sciex) (28). Briefly the digests were first separated by strong cation exchange chromatography using a 2.1-mm x 10-cm polysulfoethyl A column (Nest Group) at a flow rate of 200 µl/min using AKTA Basic 10 (GE Healthcare). Peptide elution was achieved by salt gradient, and 15 fractions were manually collected based on UV absorbance at 215 nm. All of the strong cation exchange fractions were desalted off line using Vivapure C18 microspin columns (Vivascience) prior to LC MS/MS. LC MS/MS was carried out by nanoflow reverse phase liquid chromatography (Dionex) coupled on line to QSTAR XL MS. Reverse phase liquid chromatography was performed using a capillary column (75-µm inner diameter x 150 mm long) packed with Polaris C18-A resin (Varian Inc.), and the peptides were eluted using a linear gradient of 0–35% B in 80 min at a flow of 250 nl/min (solvent A: 98% H2O, 2% acetonitrile, 0.1% formic acid; solvent B: 98% acetonitrile, 2% H2O, 0.1% formic acid). The MS/MS was operated in an information-dependent mode in which each full MS analysis was followed by three MS/MS acquisitions where the three most abundant peptide molecular ions were dynamically selected for CID to generate tandem mass spectra. To increase the number of MS/MS spectra acquired from the sample and improve the dynamic range of mass spectrometric analysis, two or three LC MS/MS runs were performed on the same sample with exclusion of the ions sequenced from the previous LC MS/MS runs.

Database Searching for Protein Identification and Quantification—
Monoisotopic masses of both parent ions and corresponding fragment ions, parent ion charge states, and ion intensities from the MS/MS were obtained by using an automated version of the Mascot script (version 1.6b18) from Analyst QS (version 1.1 QS, MDS Sciex/Applied Biosystems) within the development version of Protein Prospector (version 4.25.2, University of California, San Francisco). The parameters for peak extraction were set as default except as follows. 1) Mass tolerance for MS/MS spectra group was 0.2 Da. 2) Deisotoping of MS/MS data was disabled. 3) Only +2, +3, +4, and +5 charged precursor ions were extracted. The Batch-tag program within Protein Prospector (version 4.25.2) was used for database searching. Trypsin was selected as the enzyme, and the maximum number of missed tryptic cleavages was set as 2. Chemical modifications such as protein amino-terminal acetylation, methionine oxidation, amino-terminal pyroglutamine, and deamidation of asparagine were selected as variable modifications during the search using Batch-tag in Protein Prospector. These modifications, except protein amino-terminal acetylation, were chosen because of their frequent occurrence during sample preparation. For SILAC experiments, 13C615N4-labeled Arg and 13C615N2-labeled Lys were also chosen as variable modifications. No fixed modifications were selected. The mass accuracy for parent ions and fragment ions were set as ±200 and 300 ppm, respectively. The UniProt (July 21, 2006) database and its reverse database were compiled using Protein Prospector software to generate a concatenated UniProt database for database searching. Such concatenated databases have been shown to be advantageous for estimating false positives for peptide and protein identifications (17). Because the samples were purified from human cell lines, Homo sapiens was selected as the restricted species. A total of 139,013 protein entries in the concatenated UniProt database (July 21, 2006) including trypsin and TEV protease were queried to identify the purified proteins. The Search Compare program in Protein Prospector was used for summarization, validation, and comparison of results. Search Compare gives a peptide score (18) and an expectation value (19) for each peptide. To determine the threshold for peptide identification, a plot of false positive rate against expectation value was obtained using the Search Compare program (see supplemental Figs. 1–4). The false positive rate is equal to 2 x (number of total peptide hits in reverse database/(number of total peptide hits in (reverse + real databases)) (17, 20). To achieve virtually no protein false positive hits, a peptide false positive rate of <0.25% was chosen, corresponding to an expectation value cutoff of 0.01. General protein identification was based on at least two peptides. If a protein was identified by multiple peptides from more than two preparations, it would be considered as a hit in a preparation identified by only one peptide. The proteins identified by one or two peptides were confirmed by manual inspection of the MS/MS spectra. If peptides matched to multiple members of a protein family, criteria used in the Search Compare program for selecting the ones to report are as follows. 1) Only proteins with unique peptide identification are selected. 2) Proteins with more coverage are selected over proteins with less. 3) A Swiss-Prot accession number is selected over a UniProt accession number. 4) The accession number is reported with the alphabetically first accession number. The same criteria were also used for reporting the isoforms of a protein family. The homologous proteins with unique peptide identifications were examined manually and included in protein and peptide reports. For the SILAC experiments, the Search Compare program was also used to calculate the relative abundance ratios of Arg/Lys-containing peptides based on ion intensities of monoisotopic peaks observed in the LC MS spectra at the time when the peptides were sequenced and subsequently identified during database searching (7, 21). Signal to noise ratio ≥2 was required for peaks to be considered for quantitation. The SILAC ratios were further validated by checking all of the raw spectra within the Protein Prospector Search Compare program. The ratio outliers (with >30% standard deviations) were easily visualized on the ratio plots in Protein Prospector. If the peptide peaks were mixed with other peptide peaks or buried in the noise peaks, they were excluded for quantification. The SILAC ratios reported here were average values, and the accuracy and significance of the measurements were evaluated by calculating standard deviations. The ratios obtained from duplicated experiments were examined manually, and the results were reproducible within experimental errors less than 30%.

Transfection of FLAG-ADRM1, HA-UCH37 for Quantitative Western Blotting—
The 293 cells stably expressing HTBH alone were transiently transfected with pcDNA3-HA-UCH37 or pcDNA/FRT-ADRM1-FLAG. Twenty-four hours after transfection, the cells were washed three times in PBS and lysed in lysis buffer A (50 mM Tris, pH 7.5, 10% glycerol, 1 x phosphotase inhibitors, 0.5% NP40, 1 mM dithiothreitol, 5 mM MgCl2, 5 mM ATP, 1 µg/ml phenylmethylsulfonyl fluoride, leupeptin, aprotinin, pepstatin) as described previously (16). The lysates were centrifuged at 13,000 rpm for 15 min to remove cell debris. Cleared lysate was divided into four aliquots (lysate A). An equal amount of 293 cells stably expressing Rpn11-HTBH was grown until the cells reached confluence and processed as described above. The lysate was divided into four aliquots (lysate B). In Tc-PAM experiments, equal amounts of lysates A and B were combined and incubated with streptavidin resin for 20 min, 1 h, or 2 h at 4 °C, respectively. In MAP experiments, equal amounts of lysates A and B were subjected to purification separately by incubating with streptavidin resin for 2 h. The streptavidin beads were washed with 50 bed volumes of the lysis buffer A followed by a final wash with 30 bed volumes of TEB (50 mM Tris-HCl (pH 7.5) and 1 mM ATP). Beads were incubated in 2 bed volumes of TEB with 1% TEV at 30 °C for 1 h. The 26 S proteasome complex was eluted from the beads with TEB and stored at –20 °C. The purified samples from MAP experiments were mixed for subsequent analysis.

Validation of Dynamic Interactions Using Quantitative Western Blotting—
The proteasome complex eluates were separated by one-dimensional SDS-PAGE. Proteins was transferred to a PVDF membrane and analyzed by immunoblotting. HA-UCH37 protein was detected using a mouse anti-HA antibody (1:500) followed by HRP-conjugated anti-mouse IgG (1:10,000). ADRM1-FLAG protein was detected using a mouse anti-FLAG antibody (1:2000) followed by HRP-conjugated anti-mouse IgG (1:10,000). The blots were stripped and reprobed with anti-Rpt6 (1:1000) followed by HRP-conjugated anti-mouse IgG (1:10,000). For the quantitative Western analysis, Cy5-conjugated anti-mouse IgG (1:10,000) was used as the secondary antibody. The intensity of the HA-UCH37 bands, ADRM1-FLAG bands, or Rpt6 bands was quantified using an Odyssey infrared scanning system (LI-COR Biosciences, Lincoln, NE). The ratio of HA-UCH37 and Rpt6 or ADRM1-FLAG and Rpt6 was plotted with incubation time.

Expression, Transfection of the Ubiquitin-like Domain-containing CTD Phosphatase 1 (UBLCP1), and Purification of UBLCP1-containing Complex—
UBLCP1 was amplified using PCR from a human cDNA library with forward primer 5'-GGAATTCGCTCTCCCTATCATTGTAAAATGGG-3' and reverse primer 5'-GCTCTAGACTACTGTCCTTGCTTCTTTGAGAG-3'. The UBLCP1 DNA fragment containing an EcoRI site at the 5'-end and a XbaI site at the 3'-end was cloned into pcDNA3-N-terminal TAP vector. The 293 cells were transfected with pcDNA3-NTAP-UBLCP1 or left untransfected as the negative control. After 24 h, cells were harvested and lysed in a buffer containing 100 mM sodium chloride, 50 mM sodium phosphate, 10% glycerol, 5 mM ATP, 2 mM DTT, 1x protease inhibitor mixture (Roche Applied Science), 1x phosphatase inhibitor mixture, and 0.5% Nonidet P-40 (pH 7.5). The lysates were centrifuged at 13,000 rpm for 15 min to remove cell debris. The lysate was incubated with IgG-Sepharose (MP Biomedicals, Inc.) for 2 h at 4 °C. The bound IgG beads were then washed with 25 bed volumes of the lysis buffer followed by a final wash with 25 bed volumes of TEB. Beads were incubated in 2 bed volumes of TEB with 1% TEV at 30 °C for 1 h. The purified protein complex was eluted from the beads with TEB and stored at –20 °C. The purified UBLCP1-containing complex and the control were subjected to Western blotting using antibodies against a 19 S proteasome subunit (Rpt6) and two 20 S proteasome subunits ({alpha}6 and {alpha}7). In addition, the purified complexes were also digested with trypsin and analyzed by LC MS/MS as described above.


   RESULTS
TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
 RESULTS
DISCUSSION
 REFERENCES
 
Quantitative Strategies for Identification of Specific PIPs—
The SILAC strategy (8, 11, 12) was used for initial identification of specific human PIPs and is referred to here as the PAM-SILAC approach because protein purification is carried out after the mixing of equal amounts of cell lysates from cells that are differentially labeled (scheme shown in Fig. 1A). Previously we reported the use of a new histidine and biotin tag for rapid and effective purification of human 26 S proteasomes from stable cell lines under native conditions (16). In this work, we used a similar purification strategy using cells expressing an affinity-tagged proteasome subunit for the purification of the 26 S proteasomes and cells expressing the affinity tag alone as the control sample. The affinity purification mixture containing lysates from the tagged proteasome sample and the control sample was incubated overnight for maximum binding. However, when shorter incubation times were used, we noticed that the SILAC ratios for some of the co-purified proteins increased significantly. This observation prompted us to speculate that this phenomenon may be a result of dynamic interactions and that it could be used to probe and identify dynamically interacting proteins.


Figure 1
View larger version (34K):
[in this window]
[in a new window]

  		FIG. 1. Quantitative strategies to identify specific human PIPs using SILAC. Cells stably expressing Rpn11-HTBH were grown in light isotope-labeled (L) medium containing [12C614N4]Arg/]12C614N2]Lys (pink), whereas cells stably expressing the HTBH alone were grown in heavy isotope-labeled (H) medium containing [12C615N4]Arg/[13C615N2]Lys (blue). Two experimental schemes are depicted. A, PAM-SILAC and Tc-PAM-SILAC. B, MAP-SILAC approach. The relative abundance of proteins present in two different samples was quantified based on the ratios of mass spectral peak intensities of the observed peptide pairs colored in pink (light form) and blue (heavy form). Four types of proteins are classified here based on characteristic patterns of their SILAC ratios.

 
To test whether the quantitative differences observed here upon changes in incubation time reflect dynamic protein interactions, we compared interaction data from the two new approaches: the Tc-PAM-SILAC, which runs a series of PAM-SILAC experiments with varied incubation times (Fig. 1A), and MAP-SILAC (Fig. 1B). MAP-SILAC is different from PAM-SILAC in that the purification of each sample is performed separately prior to mixing, and proteins obtained from these two purifications are then mixed for digestion and MS analysis. While mapping the human 26 S PIPs, four types of proteins were observed based on their SILAC ratios (Fig. 1). Three common types of proteins (i.e. Type I, stable proteasome subunits; Type II, stable PIPs; and Type III, background proteins) were identified by the PAM-SILAC approach. In addition, a new class of proteins was identified and classified as Type IV. If only PAM-SILAC ratios were used, this group of proteins appeared to be background because their ratios were similar to those obtained for background proteins (close to 1). However, Tc-PAM-SILAC and MAP-SILAC measurements revealed that Type IV interactors behaved quite differently. All Type IV proteins had significantly increased SILAC ratios when analyzed by the MAP-SILAC approach, whereas none of the other proteins behaved in this manner. This strongly suggested that Type IV proteins dynamically interact with the proteasome (i.e. the bait protein) with high on/off rates. These proteins switch associations between the light and heavy forms of binding partners when both forms are present, leading to their misidentification in the PAM-SILAC approach. The MAP-SILAC approach, on the other hand, eliminates such mixing-based exchanges because the purification is carried out prior to mixing. The detailed description of the results is shown below for identification of human PIPs.

Identification of Human PIPs Using PAM-SILAC Strategy—
The PAM-SILAC approach was first utilized to map the human PIPs. In total, 110 proteins were identified (supplemental Table 1). The SILAC ratios for commonly known proteasome subunits and four activator proteins (i.e. PA28 {alpha}, β, and {gamma} and PA200) were high, indicating that the proteasome complexes form stable interactions. Fig. 2A1 shows the TOF MS spectrum of a representative tryptic peptide corresponding to the proteasome subunit Rpt5, which was observed as a singlet with no heavy counterpart. Aside from the known proteasome components, 42 proteins were classified as putative PIPs (SILAC ratios >1.5), and 31 proteins were classified as potential background proteins (SILAC ratios ≤1.5). A SILAC ratio of 1.5 was chosen as the cutoff value for PIPs based on previous results where interactions were reanalyzed and confirmed by immunoprecipitation experiments (7). The representative TOF MS spectra of a putative PIP (Hsp70-1; L/H ~ 3.5) and a potential background protein (40 S ribosomal protein SA; L/H ~ 1.3) are shown in Fig. 2, B1 and C1, respectively.


Figure 2
View larger version (48K):
[in this window]
[in a new window]

  		FIG. 2. Representative TOF MS spectra of peptides derived from four different types of proteins identified in PAM-SILAC (A1–F1), Tc-PAM-SILAC (A2–F2 and A3–F3), and MAP-SILAC (A4–F4) experiments as described in Fig. 1."{circ}" and "•" represent the light and heavy forms of the peptide, respectively. The SILAC ratios (L/H) for each peptide are shown in the corresponding spectra. A1–A4, TOF MS spectra of a tryptic peptide (MH33+ 617.33, VIAATNRVDILDPALLR) matched to a proteasome subunit, Rpt5 (Type I). B1–B4, TOF MS spectra of a tryptic peptide (MH22+ 807.90, AFYPEEISSMVLTK) matched to Hsp70-1 (Type II). C1–C4, TOF MS spectra of a tryptic peptide (MH22+ 849.93, FTPGTFTNQIQAAFR) matched to 40 S ribosomal protein (RP) SA, a background protein (Type III). D1–D4, TOF MS spectra of a tryptic peptide (MH22+ 766.39, acetyl-TTSGALFPSLVPGSR) matched to ADRM1/hRpn13 (Type IVa). E1–E4, TOF MS spectra of a tryptic peptide (MH33+ 540.29, TLAEHQQLIPLVEK) matched to UCH37 (Type IVa). F1–F4, TOF MS spectra of a tryptic peptide (MH33+ 710.75, QIIQQNPSLLPALLQQIGR) matched to hHR23B (Type IVb).

 
It is interesting to note that a recently assigned proteasome subunit, ADRM1/hRpn13 (Fig. 2D1) had a rather low ratio, ~1.7. The low SILAC ratio for ADRM1/hRpn13, in contrast to the high ratios of the other known proteasome components, suggested that ADRM1/hRpn13 may interact with the core proteasome complex dynamically. In addition, three of the known PIPs, UCH37 (Fig. 2E1), hHR23B (Fig. 2F1), and UBE3C, had surprisingly low ratios and were thus grouped into the background proteins based on the standard SILAC ratio cutoff (≤1.5). These misidentifications indicate that PAM-SILAC ratios may not be reliable to distinguish the specific but dynamic PIPs from nonspecifically bound background. A better strategy is required to unambiguously identify the full spectrum of interactors in particular dynamically interacting components.

Identification of Dynamic PIPs Using Tc-PAM-SILAC Approach—
The Tc-PAM-SILAC was developed based on two assumptions. First, dynamic interacting proteins can exchange between the light and heavy forms during purification when both are present; and second, the range of dynamic interaction swapping depends on interaction dynamics and the incubation time. To test this hypothesis, Tc-PAM-SILAC experiments were performed with 20-min, 1-h, and 2-h incubation times. The SILAC ratios of the proteasome component Rpt5 remained high in all three purifications (Figs. 2, A1–A3). Similarly for any proteins with high SILAC ratios obtained from the 2-h PAM purification, shorter incubation time did not result in any changes in their SILAC ratios. This further supports the notion that these proteins interact stably with the Rpn11-HTBH-containing complexes and do not undergo interaction swapping. In total, 35 of the putative PIPs with SILAC ratios >1.5 had purification time-independent SILAC ratios, and we thus classified them as stable proteasome interactors. This is exemplified by the TOF MS spectra of the peptide matched to Hsp70-1 (Fig. 2, B2 and B3). Importantly we found that the SILAC ratios of ADRM1 and UCH37 increased dramatically with shorter incubation times from 1.7 to 6.6 and 1.3 to 7.2, respectively (Fig. 2, D1–D3 and E1–E3). An additional six proteins showed such characteristic inverse relationships between incubation time and SILAC ratios, supporting the idea that dynamic protein interactions are present and demonstrating that this strategy can distinguish binding characteristics. The detailed reports for protein and peptide identifications are summarized in Supplemental Tables 2 and 3.

Surprisingly one of the known PIPs, namely hHR23B, did not exhibit an increased SILAC ratio (~1.4) even when the incubation time was decreased to 20 min (Fig. 2, F1–F3). We speculate that the on/off rate of the interaction between hHR23B and the proteasome complex is very fast so that much shorter incubation times would be needed. Therefore, it is reasonable to believe that some of the dynamic PIPs may still be buried in the background protein group beyond the reach for clear identification with the Tc-PAM-SILAC approach.

Identification of Dynamic PIPs by the MAP-SILAC Strategy—
As discussed above, MAP-SILAC improves the identification power for protein interactions by performing individual purification of two samples prior to their mixing. The eluted protein complexes from both the control and tagged samples were mixed, precipitated, digested, and analyzed by LC MS/MS. In addition to the proteasome subunits, a total of 78 proteins were identified by the MAP-SILAC strategy, including 67 proteins with SILAC ratios >1.5 and 11 proteins with SILAC ratios ≤1.5 (supplemental Table 1b). The TOF MS spectra of representative identified proteins are illustrated in Fig. 2, A4–F4. The MAP-SILAC ratios of Rpt5, Hsp70, and 40 S ribosomal protein did not change in comparison with their PAM-SILAC ratios, whereas the MAP-SILAC ratios of ADRM1, UCH37, and hHR23B changed to high values. These results further confirm our classification of stable and dynamic components of the purified protein complex bound to the proteasome subunit Rpn11. The results of Tc-PAM-SILAC and MAP-SILAC experiments are summarized in Fig. 3. In the Tc-PAM-SILAC the number of putative PIPs increased with shorter incubation times, whereas the background concomitantly decreased. Direct comparison with the MAP-SILAC approach suggests that 14 proteins classified as background by Tc-PAM-SILAC are potential PIPs. Some of these putative PIPs may undergo extremely dynamic interactions and might only be identified by substantially shortened incubation times. This, however, becomes experimentally infeasible because of the required incubation time for affinity purification. Thus, MAP-SILAC demonstrates significantly unique and high capability and is more advantageous for unambiguous identification of both stable and dynamic components of protein complexes. Furthermore comparison of MAP and PAM-SILAC data distinguishes stable and dynamic binding partners of the proteasome complexes. All PIPs identified in this work and their classifications are summarized in Table I and Supplemental Table 5.


Figure 3
View larger version (23K):
[in this window]
[in a new window]

  		FIG. 3. Comparison of the distribution of proteins with different ratios identified using Tc-PAM-SILAC and MAP-SILAC. A, 2-h PAM-SILAC: 20 of 31 proteins with 2-h PAM-SILAC ratios <1.5 had increased MAP-SILAC ratios (>1.5). B, 1-h PAM-SILAC: 17 of 28 proteins with 1-h PAM-SILAC ratios <1.5 had increased MAP-SILAC ratios (>1.5). C, 20-min PAM-SILAC: 14 of 25 proteins with 20-min PAM-SILAC ratios <1.5 had increased MAP-SILAC ratios (>1.5). D, MAP-SILAC. For all experiments, the total number of proteins shown here does not include the proteasomal components.

 

View this table:
[in this window]
[in a new window]

  		TABLE I Quantifying proteasome-interacting proteins by MAP-SILAC and Tc-PAM-SILAC

Pep no., number of peptides; AVG, average; n/a, not applicable. High indicates that only the light forms of the peptides were observed. Their heavy labeled forms were below noise level.

 
Validation of the Dynamics of PIPs—
To confirm the hypothesis that swapping of dynamic PIPs occurs during purification with PAM-SILAC, we used transfection and immunoblotting to validate the identified dynamic protein interactions. The dynamic PIPs such as ADRM1 and UCH37 were selected for our assay. Briefly control cells expressing either ADRM1-FLAG or HA-UCH37 but no tagged proteasome subunit were compared with Rpn11-HTBH-expressing cells that did not contain tagged versions of ADRM1 or UCH37. Cell lysates from control and Rpn11-HTBH cells were analyzed by either Tc-PAM or MAP. Rpn11-HTBH was affinity-purified, and bound protein complexes were subjected to immunoblotting using specific antibodies against either HA or FLAG tag. Because proteasomes were only purified from cells expressing no HA- or FLAG-tagged proteins, any co-purification of ADRM1-FLAG or HA-UCH37 must be a result of interactions formed in the mixed lysates during the incubation. This allowed us to determine the amount of exchanged interactions at different incubation times. Fig. 4, A and B, show the anti-FLAG Western blot to demonstrate the incorporation of ADRM1-FLAG into the purified proteasome complex. The intensity of incorporated ADRM1-FLAG increased with incubation time and was highest at 2 h, indicating that ADRM1-FLAG from the control cells was exchanged into the Rpn11-HTBH-containing proteasome complexes in the Tc-PAM approach. The quantitative changes detected by immunoblotting correlated well with the SILAC ratios of ADRM1 obtained earlier (Fig. 2, D1–D4). As expected, no incorporation of ADRM1-FLAG was observed using the MAP approach. Similar results were obtained with HA-UCH37-expressing control cells (Fig. 4, C and D). These results strongly support our hypothesis that dynamic interactions can be swapped when the light and heavy samples are mixed prior to purification as typically used in PAM-SILAC.


Figure 4
View larger version (24K):
[in this window]
[in a new window]

  		FIG. 4. Validation of interaction swapping of selected dynamic PIPs by immunoblotting. A, comparison of incorporation of ADRM1-FLAG expressed in the control cells into the purified Rpn11-HTBH-containing proteasome complexes during the purification with the Tc-PAM approach at three different incubation times (20 min, 1 h, and 2 h) and with the MAP approach (2 h). Samples were analyzed with anti-FLAG antibodies. B, representation of a quantitative plot of normalized intensities of ADRM1-FLAG bands in each sample obtained using fluorescence detection. A proteasome subunit, Rpt6, signal was used as the internal standard for normalization of proteasome loading. C, comparison of incorporation of HA-UCH37 expressed in the control cells with the purified Rpn11-HTBH-containing proteasome complexes during the purification with the Tc-PAM approach at three different incubation times (20 min, 1 h, and 2 h) and with the MAP-SILAC approach (2 h). Samples were analyzed with anti-HA antibodies. D, quantitative representation as in B.

 
Validation of a Newly Identified Dynamic PIP—
One of the newly identified dynamic PIPs revealed by MAP-SILAC in this work is UBLCP1. As illustrated in Fig. 5, A–D, its Tc-PAM-SILAC ratios ranged from 1.2 to 1.7, whereas its MAP-SILAC ratio was significantly higher, showing the characteristics of dynamic interactions. To validate its physical interaction with the proteasome complexes, a reverse affinity purification experiment was carried out using TAP-tagged UBLCP1. The purified complex was analyzed by immunoblotting using specific antibodies against a 19 S proteasome subunit (Rpt6) and two 20 S proteasome subunits ({alpha}6 and {alpha}7) (Fig. 5E). The human 26 S proteasome complex co-purified with TAP-UBLCP1. Subsequent mass spectrometric analysis of the purified TAP-UBLCP1-containing complex further confirmed the presence of the 26 S proteasome complex (data not shown), demonstrating that UBLCP1 is indeed a proteasome-interacting protein in vivo.


Figure 5
View larger version (25K):
[in this window]
[in a new window]

  		FIG. 5. A, TOF MS spectra of a tryptic peptide (MH33+ 410.54, LDDFLDLNHK) matched to UBLCP1 in the Tc-PAM-SILAC and MAP-SILAC approaches. The characteristics displayed in A identified UBLCP1 as a new dynamic PIP. B, validation of the physical interaction of UBLCP1 with the proteasome complex by immunoblotting. + indicates the affinity-purified protein complex containing TAP-UBLCP1, whereas – specifies the purification control from cells expressing UBLCP1 without TAP tag. Samples were analyzed by immunoblotting using anti-Rpt6 (a 19 S proteasome subunit) and anti-{alpha}6 and anti-{alpha}7 (two 20 S proteasome subunits) antibodies.

 

   DISCUSSION
TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
REFERENCES
 
In this work we report a new method for quantitative identification of dynamic interactors of protein complexes. Among the two new strategies developed, MAP-SILAC and Tc-PAM-SILAC, we demonstrate that MAP-SILAC permits unambiguous identification of all specific protein interactions including both stable and dynamic interactions, whereas Tc-PAM-SILAC can effectively identify a portion of the dynamic PIPs. In comparison with the conventional PAM-SILAC approach, MAP-SILAC has several advantages. First, no interaction exchange of labeled proteins occurs due to the separate purification processes of the samples. This preserves specificity of protein interactions during purification and therefore retains the high SILAC ratios for all specific interactions. Second, purification can be carried out under optimal incubation time for best purification efficiency. In contrast, although part of the dynamic interactions can be identified with Tc-PAM-SILAC, shorter incubation time often compromises the purification efficiency. Although the amount of affinity matrix can be increased to compensate for the efficiency (22), it can become quite costly for large scale purifications. If the dynamic interactions are very fast, the PAM-SILAC ratios could be very low within the experimentally allowed incubation times. By using quantitative analysis, we found that specific but dynamic protein interactions have the following characteristics. (i) Their PAM-SILAC ratios are usually low and close to those of background proteins (~1) when interaction dynamics are significantly faster than the incubation time. (ii) As a group, their Tc-PAM-SILAC ratios tend to increase when interaction swapping is limited by shortening incubation time. (iii) Their MAP-SILAC ratios are much higher than their PAM-SILAC ratios. We believe that these features lay a solid foundation for convenient identification of the dynamic protein interactions.

Although the main difference between MAP-SILAC and PAM-SILAC is sample preparation, MAP-SILAC allows easy identification of specific proteasome interaction partners based on their SILAC ratios regardless of the nature of their interactions. It should be noted that separate purification of two samples by the MAP strategy may introduce some variability during the purification process, and therefore caution needs to be taken to minimize the experimental variations between samples. In theory, the MAP strategy can be coupled to many mass spectrometry-based quantitative methods with or without stable isotope labeling (23–27) to provide similar results as compared with those from the MAP-SILAC approach. The MAP strategy is more versatile because it can be used with quantitative methods using labeling at peptide or protein levels, whereas the PAM strategy is limited to quantitative methods with labeling at the protein level. Therefore, labeling at the protein level is required for identification of dynamic interactors because both MAP and PAM strategies need to be carried out as demonstrated here.

We applied the Tc-PAM-SILAC and MAP-SILAC approaches to map the spectrum of the dynamic human PIPs. In addition to 35 subunits of the proteasome complexes and four activator proteins reported previously (16), 67 putative human PIPs were identified, including polyubiquitin, ubiquitin receptors, heat shock proteins, adaptor protein complex 3 (AP-3), and the chaperone-containing TCP1 (CCT) complex as well as proteins involved in translation, metabolism, and transcription. To the best of our knowledge, this report presents the first extensive list of human proteasome-interacting proteins using affinity purification and quantitative mass spectrometry because human PIPs in previous reports were mainly determined using the two-hybrid system and in vitro experiments (BioGRID database). Searching the existing data (28–31) (BioGRID database), only a few human PIPs identified here were found by alternative methods. This is not surprising because proteasome-interacting proteins are sensitive to the methods used for capturing them, cell or tissue type, developmental stage, environmental change, and purification condition (14). Among the putative PIPs identified here, 57 are novel interactions that have not been reported before.

In comparison with the known yeast PIPs identified by proteomics analysis (7, 15), only a fraction of the PIPs were in common, including heat shock proteins, histones, ubiquitin, deubiquitinating enzymes, 14-3-3 proteins, and elongation factor 1-{alpha}. Due to the complexity of the human proteome, additional isoforms of these proteins were also identified. SILAC ratios of 35 proteins were consistent (>1.5) in all experiments and were classified as stable PIPs (Table I). Among the stable PIPs, there are three types of chaperone proteins including heat shock protein and components of the AP-3 and CCT complexes. Consistent with these findings, various heat shock proteins have been shown to interact with yeast proteasomes (7, 15) and were suggested to facilitate delivery of aggregation-prone substrates for degradation (30, 32) or to play a role in the proteasome structural integrity and assembly (31). In contrast, the interactions of the AP-3 and CCT complexes with the human proteasomes have not been reported before. The AP-3 complex appears to be involved in the sorting of a subset of transmembrane proteins targeted to lysosomes and lysosome-related organelles (33). Because membrane receptors can be targeted by the ubiquitin-proteasome degradation pathway, AP-3 complex may help recruit the membrane-bound protein substrates to proteasomes for degradation. The CCT complex is a molecular chaperone that plays important roles in the folding of tubulin, actin, and other cytosolic proteins (34). Previously two subunits of goldfish CCT complex have been shown to interact with the 26 S proteasome directly (35). In mammalian cells, the CCT complex is suspected to be a substrate of the ubiquitin-proteasome pathway because CCT complex components accumulated after treatment with proteasome inhibitor (34). Therefore, it is possible that the human CCT complex might be either a degradation substrate or a mediator for the proteasomal degradation of unfolded proteins.

Aside from the stable PIPs, 16 proteins had MAP-SILAC ratios at least 2-fold higher than Tc-PAM-SILAC ratios, suggesting that they dynamically interact (Table I). Among them, eight proteins had significantly increased PAM-SILAC ratios with shorter incubation time, thus confirming their dynamic nature. In contrast, seven of them showed little change in their PAM-SILAC ratios over time and therefore cannot be easily identified as dynamic proteins based on their Tc-PAM-SILAC ratios, suggesting that they may represent the highly dynamic interactors. Interestingly the differences between the MAP-SILAC and Tc-PAM-SILAC ratios of these proteins are varied probably due to different kinetic parameters of these interactions, intracellular abundance, and the percentage of these interactors bound to the bait protein.

Among the identified dynamic PIPs, about half of them are known to be involved in the ubiquitin-proteasome degradation pathway including proteasome subunits, ubiquitin receptors, deubiquitinating enzymes, and ubiquitin ligases, which are susceptible to regulation because their special functions in the proteasomal degradation pathway. Interestingly a newly assigned proteasome subunit, ADRM1/hRpn13, was found to interact with the proteasome complex dynamically, whereas the interactions among other proteasome subunits were stable. Recent reports have demonstrated that the main deubiquinating enzyme in mammalian cells, UCH37, is recruited to proteasomes through ADRM1/hRpn13 (36–39). We found here that UCH37 interacts with the proteasome dynamically, confirming that UCH37 and ADRM1 interact with the proteasomes in a similar manner. Although the biological role of the dynamic interaction needs to be further elucidated, we speculate that the dynamic characteristic of the ADRM1/hRpn13 interaction with the proteasome may be required for fine regulation of its biological activities within the context of the proteasome. It is noteworthy that the interaction of hHR23B with the proteasome complex appears to be highly dynamic because its SILAC ratio remained low (~1.4) during Tc-PAM-SILAC experiments. This is not surprising because hHR23B is a ubiquitin receptor that transports ubiquitinated substrates to the proteasome for degradation. The dynamic interaction between hHR23B and the proteasome would allow it to quickly and efficiently deliver multiple substrates.

One of the novel and dynamic PIPs identified in this work is UBLCP1. We confirmed that this protein indeed interacts in vivo with the proteasome by both immunoblotting and mass spectrometry. UBLCP1 has been shown to dephosphorylate the CTD of RNA polymerase II in vitro (40). In addition, recent studies have physically and functionally linked the 26 S proteasome with RNA polymerase II in vivo (41). Together with the findings reported here, these results raise the possibility that UBLCP1 may be a shuttling factor that links the proteasome complex to transcriptional control.

In summary, we describe MAP-SILAC as a powerful method for quantitative analysis of protein interactions by mass spectrometry. Importantly the dynamic interacting partners can be effectively identified using this strategy in combination with PAM-SILAC. The methods can be generally applied to the study of other protein interaction networks. The work presented here demonstrates a new and unique application of the SILAC strategy for quantitative analysis of protein interaction dynamics. The MAP and PAM strategies can only be coupled to labeling methods at the protein level. Although chemical labeling at the protein level is feasible, metabolic labeling appears to be more advantageous because of less variability in sampling. Because protein interactions used by the cellular regulatory mechanisms are typically dynamic, we believe that the development of MAP-SILAC in combination with Tc-PAM-SILAC provides a valuable expansion of proteomics technologies to allow for the detection of biologically important but previously inaccessible protein interactions.


   ACKNOWLEDGMENTS
 
We are very grateful to Prof. Peter Kaiser for fruitful discussions and critical reading of the manuscript. We are indebted to Prof. Lee Bardwell and Wendell-Lamar Blackwell for generous help in quantitative Western analysis. We thank Prof. Robert E. Cohen and Dr. Tingting Yao for providing HA-UCH37 and ADRM1-FLAG plasmids, Profs. Ralph Bradshaw and Suzanne Sandmeyer for support and insightful suggestions, Prof. A. L. Burlingame for the developmental version of Protein Prospector, and Aenoch Lynn and Peter Baker for using Protein Prospector. We also thank the Huang laboratory members, especially Yingying Yang and Nelson Jen, for help during this work.


  FOOTNOTES
 
Received, June 4, 2007, and in revised form, September 10, 2007.

Published, MCP Papers in Press, October 12, 2007, DOI 10.1074/mcp.M700261-MCP200

1 The abbreviations used are: SILAC, stable isotope labeling of amino acids in cell culture; MAP, mixing after purification; PAM, purification after mixing; Tc, time controlled; PIP, proteasome-interacting protein; HRP, horseradish peroxidase; TAP, tandem affinity purification; TEV, tobacco etch virus; TEB, TEV elution buffer; HTBH, histidine tag-TEV cleavage sequence-biotin tag-histidine tag; L, light isotope-labeled ([12C614N4]Arg- and/or [12C614N2]Lys-labeled); H, heavy isotope-labeled ([13C615N4]Arg and/or [13C615N2]Arg-labeled); L/H, relative abundance ratio of light ([12C614N4]Arg/[12C614N2]Lys-labeled) to heavy ([13C615N4]Arg/[13C615N2]Arg-labeled) forms; EMEM, Eagle's modified essential medium; HA, hemagglutinin; AP-3, adaptor protein complex 3; CCT, chaperone-containing TCP1; UBLCP1, ubiquitin-like domain-containing CTD phosphatase 1; CTD, carboxyl-terminal domain. Back

* This work was supported by National Institutes of Health Grant GM-74830 (to L. H.), Department of the Army Grant PC-041126 (to L. H.), and a University of California, Irvine cancer center postdoctoral fellowship (to X. W.). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Back

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

{ddagger} To whom correspondence should be addressed: Depts. of Physiology & Biophysics and of Developmental & Cell Biology, University of California, Medical Science I, D233, Irvine, CA 92697-4560. E-mail: lanhuang{at}uci.edu


   REFERENCES
TOP
 ABSTRACT
 EXPERIMENTAL PROCEDURES
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Gavin, A., Bosche, M., Krause, R., Grandi, P., Bauer, A., Schultz, J., Rick, J. M., Michon, A. M., Cruciat, C. M., Remor, M., Hofert, C., Schelder, M., Brajenovic, M., Ruffner, H., Merino, A., Klein, K., Hudak, M., Dickson, D., Rudi, T., Gnau, V., Bauch, A., Bastuck, S., Huhse, B., Leutwein, C., Heurtier, M. A., Copley, R. R., Edelmann, A., Querfurth, E., Rybin, V., Drewes, G., Raida, M., Bouwmeester, T., Bork, P., Seraphin, B., Kuster, B., Neubauer, G., and Superti-Furga, G. (2002 ) Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141 –147[CrossRef][Medline]

  2. Ho, Y., Gruhler, A., Heilbut, A., Bader, G. D., Moore, L., Adams, S. L., Millar, A., Taylor, P., Bennett, K., Boutilier, K., Yang, L., Wolting, C., Donaldson, I., Schandorff, S., Shewnarane, J., Vo, M., Taggart, J., Goudreault, M., Muskat, B., Alfarano, C., Dewar, D., Lin, Z., Michalickova, K., Willems, A. R., Sassi, H., Nielsen, P. A., Rasmussen, K. J., Andersen, J. R., Johansen, L. E., Hansen, L. H., Jespersen, H., Podtelejnikov, A., Nielsen, E., Crawford, J., Poulsen, V., Sørensen, B. D., Matthiesen, J., Hendrickson, R. C., Gleeson, F., Pawson, T., Moran, M. F., Durocher, D., Mann, M., Hogue, C. W., Figeys, D., and Tyers, M. (2002 ) Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415, 180 –183[CrossRef][Medline]

  3. Bouwmeester, T., Bauch, A., Ruffner, H., Angrand, P. O., Bergamini, G., Croughton, K., Cruciat, C., Eberhard, D., Gagneur, J., Ghidelli, S., Hopf, C., Huhse, B., Mangano, R., Michon, A. M., Schirle, M., Schlegl, J., Schwab, M., Stein, M. A., Bauer, A., Casari, G., Drewes, G., Gavin, A. C., Jackson, D. B., Joberty, G., Neubauer, G., Rick, J., Kuster, B., and Superti-Furga, G. (2004 ) A physical and functional map of the human TNF-{alpha}/NF-{kappa}B signal transduction pathway. Nat. Cell Biol. 6, 97 –105[CrossRef][Medline]

  4. Gavin, A. C., Aloy, P., Grandi, P., Krause, R., Boesche, M., Marzioch, M., Rau, C., Jensen, L. J., Bastuck, S., Dumpelfeld, B., Edelmann, A., Heurtier, M. A., Hoffman, V., Hoefert, C., Klein, K., Hudak, M., Michon, A. M., Schelder, M., Schirle, M., Remor, M., Rudi, T., Hooper, S., Bauer, A., Bouwmeester, T., Casari, G., Drewes, G., Neubauer, G., Rick, J. M., Kuster, B., Bork, P., Russell, R. B., and Superti-Furga, G. (2006 ) Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631 –636[CrossRef][Medline]

  5. Krogan, N. J., Cagney, G., Yu, H., Zhong, G., Guo, X., Ignatchenko, A., Li, J., Pu, S., Datta, N., Tikuisis, A. P., Punna, T., Peregrin-Alvarez, J. M., Shales, M., Zhang, X., Davey, M., Robinson, M. D., Paccanaro, A., Bray, J. E., Sheung, A., Beattie, B., Richards, D. P., Canadien, V., Lalev, A., Mena, F., Wong, P., Starostine, A., Canete, M. M., Vlasblom, J., Wu, S., Orsi, C., Collins, S. R., Chandran, S., Haw, R., Rilstone, J. J., Gandi, K., Thompson, N. J., Musso, G., St Onge, P., Ghanny, S., Lam, M. H., Butland, G., Altaf-Ul, A. M., Kanaya, S., Shilatifard, A., O'Shea, E., Weissman, J. S., Ingles, C. J., Hughes, T. R., Parkinson, J., Gerstein, M., Wodak, S. J., Emili, A., and Greenblatt, J. F. (2006 ) Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637 –643[CrossRef][Medline]

  6. Collins, S. R., Kemmeren, P., Zhao, X. C., Greenblatt, J. F., Spencer, F., Holstege, F. C., Weissman, J. S., and Krogan, N. J. (2007 ) Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae. Mol. Cell. Proteomics 6, 439 –450[Abstract/Free Full Text]

  7. Guerrero, C., Tagwerker, C., Kaiser, P., and Huang, L. (2006 ) An integrated mass spectrometry-based proteomic approach: quantitative analysis of tandem affinity-purified in vivo cross-linked protein complexes (QTAX) to decipher the 26 S proteasome-interacting network. Mol. Cell. Proteomics 5, 366 –378[Abstract/Free Full Text]

  8. Mann, M. (2006 ) Functional and quantitative proteomics using SILAC. Nat. Rev. Mol. Cell Biol. 7, 952 –958[CrossRef][Medline]

  9. Selbach, M., and Mann, M. (2006 ) Protein interaction screening by quantitative immunoprecipitation combined with knockdown (QUICK). Nat. Methods 3, 981 –983[CrossRef][Medline]

  10. Ranish, J. A., Yi, E. C., Leslie, D. M., Purvine, S. O., Goodlett, D. R., Eng, J., and Aebersold, R. (2003 ) The study of macromolecular complexes by quantitative proteomics. Nat. Genet. 33, 349 –355[CrossRef][Medline]

  11. Blagoev, B., Kratchmarova, I., Ong, S. E., Nielsen, M., Foster, L. J., and Mann, M. (2003 ) A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling. Nat. Biotechnol. 21, 315 –318[CrossRef][Medline]

  12. Ong, S. E., Kratchmarova, I., and Mann, M. (2003 ) Properties of 13C-substituted arginine in stable isotope labeling by amino acids in cell culture (SILAC). J. Proteome Res. 2, 173 –181[CrossRef][Medline]

  13. Pickart, C. M., and Cohen, R. E. (2004 ) Proteasomes and their kin: proteases in the machine age. Nat. Rev Mol. Cell Biol. 5, 177 –187[CrossRef][Medline]

  14. Leggett, D. S., Hanna, J., Borodovsky, A., Crosas, B., Schmidt, M., Baker, R. T., Walz, T., Ploegh, H., and Finley, D. (2002 ) Multiple associated proteins regulate proteasome structure and function. Mol. Cell 10, 495 –507[CrossRef][Medline]

  15. Verma, R., Chen, S., Feldman, R., Schieltz, D., Yates, J., Dohmen, J., and Deshaies, R. J. (2000 ) Proteasomal proteomics: identification of nucleotide-sensitive proteasome-interacting proteins by mass spectrometric analysis of affinity-purified proteasomes. Mol. Biol. Cell 11, 3425 –3439[Abstract/Free Full Text]

  16. Wang, X., Chen, C. F., Baker, P. R., Chen, P. L., Kaiser, P., and Huang, L. (2007 ) Mass spectrometric characterization of the affinity purified human 26S proteasome complex. Biochemistry 46, 3553 –3565[CrossRef][Medline]

  17. Higdon, R., Hogan, J. M., Van Belle, G., and Kolker, E. (2005 ) Randomized sequence databases for tandem mass spectrometry peptide and protein identification. Omics 9, 364 –379[CrossRef][Medline]

  18. Chalkley, R. J., Baker, P. R., Huang, L., Hansen, K. C., Allen, N. P., Rexach, M., and Burlingame, A. L. (2005 ) Comprehensive analysis of a multidimensional liquid chromatography mass spectrometry dataset acquired on a quadrupole selecting, quadrupole collision cell, time-of-flight mass spectrometer: II. New developments in Protein Prospector allow for reliable and comprehensive automatic analysis of large datasets. Mol. Cell. Proteomics 4, 1194 –1204[Abstract/Free Full Text]

  19. Lynn, A. J., Chalkley, R. J., Baker, P. R., Segal, M. R., and Burlingame, A. L. (2006 ) Protein Prospector and ways of calculating expectation values, in Proceedings of the 54th ASMS Conference on Mass Spectrometry , Seattle, May 28-June 1, 2006, p. 351 , American Society for Mass Spectrometry, Santa Fe, NM

  20. Elias, J. E., and Gygi, S. P. (2007 ) Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207 –214[CrossRef][Medline]

  21. Baker, P., Chalkley, R. J., Trinidad, J. C., and Burlingame, A. L. (2007 ) New protein level quantitation features in Protein Prospector, in Proceedings of the 55th ASMS Conference on Mass Spectrometry , Indianapolis, June 3–7, 2007, p. 181 , American Society for Mass Spectrometry, Santa Fe, NM

  22. Cristea, I. M., Williams, R., Chait, B. T., and Rout, M. P. (2005 ) Fluorescent proteins as proteomic probes. Mol. Cell. Proteomics 4, 1933 –1941[Abstract/Free Full Text]

  23. 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]

  24. Ross, P. L., Huang, Y. N., Marchese, J. N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S., Purkayastha, S., Juhasz, P., Martin, S., Bartlet-Jones, M., He, F., Jacobson, A., and Pappin, D. J. (2004 ) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics 3, 1154 –1169[Abstract/Free Full Text]

  25. Regnier, F. E. (2007 ) An isotope coding strategy for proteomics involving both amine and carboxyl group labeling. Methods Mol. Biol. 359, 125 –133[Medline]

  26. Schmidt, A., Kellermann, J., and Lottspeich, F. (2005 ) A novel strategy for quantitative proteomics using isotope-coded protein labels. Proteomics 5, 4 –15[CrossRef][Medline]

  27. Paoletti, A. C., Parmely, T. J., Tomomori-Sato, C., Sato, S., Zhu, D., Conaway, R. C., Conaway, J. W., Florens, L., and Washburn, M. P. (2006 ) Quantitative proteomic analysis of distinct mammalian Mediator complexes using normalized spectral abundance factors. Proc. Natl. Acad. Sci. U. S. A. 103, 18928 –18933[Abstract/Free Full Text]

  28. Schmidt, M., Hanna, J., Elsasser, S., and Finley, D. (2005 ) Proteasome-associated proteins: regulation of a proteolytic machine. Biol. Chem. 386, 725 –737[CrossRef][Medline]

  29. Ferrell, K., Wilkinson, C. R., Dubiel, W., and Gordon, C. (2000 ) Regulatory subunit interactions of the 26S proteasome, a complex problem. Trends Biochem. Sci. 25, 83 –88[CrossRef][Medline]

  30. Luders, J., Demand, J., and Hohfeld, J. (2000 ) The ubiquitin-related BAG-1 provides a link between the molecular chaperones Hsc70/Hsp70 and the proteasome. J. Biol. Chem. 275, 4613 –4617[Abstract/Free Full Text]

  31. Imai, J., Maruya, M., Yashiroda, H., Yahara, I., and Tanaka, K. (2003 ) The molecular chaperone Hsp90 plays a role in the assembly and maintenance of the 26S proteasome. EMBO J. 22, 3557 –3567[CrossRef][Medline]

  32. Wiederkehr, T., Bukau, B., and Buchberger, A. (2002 ) Protein turnover: a CHIP programmed for proteolysis. Curr. Biol. 12, R26 –R28[CrossRef][Medline]

  33. Simpson, F., Peden, A. A., Christopoulou, L., and Robinson, M. S. (1997 ) Characterization of the adaptor-related protein complex, AP-3. J. Cell Biol. 137, 835 –845[Abstract/Free Full Text]

  34. Yokota, S., Kayano, T., Ohta, T., Kurimoto, M., Yanagi, H., Yura, T., and Kubota, H. (2000 ) Proteasome-dependent degradation of cytosolic chaperonin CCT. Biochem. Biophys. Res. Commun. 279, 712 –717[CrossRef][Medline]

  35. Horiguchi, R., Dohra, H., and Tokumoto, T. (2006 ) Comparative proteome analysis of changes in the 26S proteasome during oocyte maturation in goldfish. Proteomics 6, 4195 –4202[CrossRef][Medline]

  36. Yao, T., Song, L., Xu, W., DeMartino, G. N., Florens, L., Swanson, S. K., Washburn, M. P., Conaway, R. C., Conaway, J. W., and Cohen, R. E. (2006 ) Proteasome recruitment and activation of the Uch37 deubiquitinating enzyme by Adrm1. Nat. Cell Biol. 8, 994 –1002[CrossRef][Medline]

  37. Qiu, X. B., Ouyang, S. Y., Li, C. J., Miao, S., Wang, L., and Goldberg, A. L. (2006 ) hRpn13/ADRM1/GP110 is a novel proteasome subunit that binds the deubiquitinating enzyme, UCH37. EMBO J. 25, 5742 –5753[CrossRef][Medline]

  38. Hamazaki, J., Iemura, S., Natsume, T., Yashiroda, H., Tanaka, K., and Murata, S. (2006 ) A novel proteasome interacting protein recruits the deubiquitinating enzyme UCH37 to 26S proteasomes. EMBO J. 25, 4524 –4536[CrossRef][Medline]

  39. Jorgensen, J. P., Lauridsen, A. M., Kristensen, P., Dissing, K., Johnsen, A. H., Hendil, K. B., and Hartmann-Petersen, R. (2006 ) Adrm1, a putative cell adhesion regulating protein, is a novel proteasome-associated factor. J. Mol. Biol. 360, 1043 –1052[CrossRef][Medline]

  40. Zheng, H., Ji, C., Gu, S., Shi, B., Wang, J., Xie, Y., and Mao, Y. (2005 ) Cloning and characterization of a novel RNA polymerase II C-terminal domain phosphatase. Biochem. Biophys. Res. Commun. 331, 1401 –1407[CrossRef][Medline]

  41. Gillette, T. G., Gonzalez, F., Delahodde, A., Johnston, S. A., and Kodadek, T. (2004 ) Physical and functional association of RNA polymerase II and the proteasome. Proc. Natl. Acad. Sci. U. S. A. 101, 5904 –5909[Abstract/Free Full Text]


Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati    What's this?


This article has been cited by other articles:


Home page
 J. Biol. Chem.Home page
K. M. Andersen, L. Madsen, S. Prag, A. H. Johnsen, C. A. Semple, K. B. Hendil, and R. Hartmann-Petersen
Thioredoxin Txnl1/TRP32 Is a Redox-active Cofactor of the 26 S Proteasome
J. Biol. Chem., May 29, 2009; 284(22): 15246 - 15254.
[Abstract] [Full Text] [PDF]

Home page
 Mol. Cell. ProteomicsHome page
M.-P. Bousquet-Dubouch, E. Baudelet, F. Guerin, M. Matondo, S. Uttenweiler-Joseph, O. Burlet-Schiltz, and B. Monsarrat
Affinity Purification Strategy to Capture Human Endogenous Proteasome Complexes Diversity and to Identify Proteasome-interacting Proteins
Mol. Cell. Proteomics, May 1, 2009; 8(5): 1150 - 1164.
[Abstract] [Full Text] [PDF]

Home page
 Mol. Cell. ProteomicsHome page
A. V. Gomes, G. W. Young, Y. Wang, C. Zong, M. Eghbali, O. Drews, H. Lu, E. Stefani, and P. Ping
Contrasting Proteome Biology and Functional Heterogeneity of the 20 S Proteasome Complexes in Mammalian Tissues
Mol. Cell. Proteomics, February 1, 2009; 8(2): 302 - 315.
[Abstract] [Full Text] [PDF]

Home page
 Mol. Biol. CellHome page
A. Kodani, I. Kristensen, L. Huang, and C. Sutterlin
GM130-dependent Control of Cdc42 Activity at the Golgi Regulates Centrosome Organization
Mol. Biol. Cell, February 1, 2009; 20(4): 1192 - 1200.
[Abstract] [Full Text] [PDF]

Home page
 JCBHome page
L. Trinkle-Mulcahy, S. Boulon, Y. W. Lam, R. Urcia, F.-M. Boisvert, F. Vandermoere, N. A. Morrice, S. Swift, U. Rothbauer, H. Leonhardt, et al.
Identifying specific protein interaction partners using quantitative mass spectrometry and bead proteomes
J. Cell Biol., October 20, 2008; 183(2): 223 - 239.
[Abstract] [Full Text] [PDF]

Home page
 Proc. Natl. Acad. Sci. USAHome page
C. Guerrero, T. Milenkovic, N. Przulj, P. Kaiser, and L. Huang
Characterization of the proteasome interaction network using a QTAX-based tag-team strategy and protein interaction network analysis
PNAS, September 9, 2008; 105(36): 13333 - 13338.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Supplemental Data
Right arrow All Versions of this Article:
M700261-MCP200v1
7/1/46    most recent
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Glossary
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Wang, X.
Right arrow Articles by Huang, L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Wang, X.
Right arrow Articles by Huang, L.
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
 All ASBMB Journals   Journal of Biological Chemistry 
 Journal of Lipid Research   ASBMB Today 
Advertisement
spacer
Advertisement
Advertisement