1 proteomics-based cullin-ring e3 substrate identification j. wade harper and meng-kwang

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Proteomics-based Cullin-RING E3 substrate identification

J. Wade Harper and Meng-Kwang Marcus Tan

Department of Cell Biology

Harvard Medical School

Boston MA 02115

Address correspondence to: [email protected]

Abstract

Protein turnover through the ubiquitin-proteasome pathway controls numerous developmental decisions and biochemical processes in eukaryotes. Central to protein ubiquitylation are ubiquitin ligases, which provide specificity in targeted ubiquitylation. With more than 600 ubiquitin ligases encoded by the human genome, many of which remain to be studied, considerable effort is being placed on the development of methods for identifying substrates of specific ubiquitin ligases. In this review, we describe proteomic technologies for identification of ubiquitin ligase targets with a particular focus on members of the cullin-RING E3 class of ubiquitin ligases, which use F-box proteins as substrate specific adaptor proteins. Various proteomic methods are described and are compared with genetic approaches that are available. The continued development of such methods is likely to have a substantial impact on the ubiquitin-proteasome field.

Introduction

The ubiquitin-proteasome system (UPS) is responsible for selective and timely protein turnover and is essential for proper cellular functions (1, 2). The ubiquitin system also controls the activity of numerous proteins through non-degradative mechanisms. Perturbations in the UPS are linked to many diseases such as cancers and neurodegeneration (3-5). Ubiquitylation occurs when a covalent isopeptide bond is formed between the C-terminal carboxyl group of 76 amino acid ubiquitin protein (referred to below as Ub) and the ε-amino-group of a Lys residue of the target protein. This process is catalyzed through an E1-E2-E3 cascade, wherein Ub is first activated via an E1 Ub activating enzyme in an ATP-dependent manner to form an Ub-adenylate, which is then transferred to a conserved cysteine residue within the E1 to generate a high-energy thioester bond (6). The activated Ub is subsequently transferred to one of ~2 dozen E2 conjugating enzymes, again forming a thioester. E2 enzymes play a major role in both the number of Ub molecules added to a protein as well as the chain linkage types that are generated on poly-ubiquitylated proteins(6).

Charged E2s transfer Ub to substrates via an E3 Ub ligase. Two major classes of Ub-E3s are found in eukaryotes: the Homologous to the E6AP Carboxyl Terminus (HECT) E3 ligases and the Really Interesting New Gene (RING) E3 ligases (7-9). HECT domain E3s receive Ub from the E2 as a thioester and subsequently transfer Ub to an associated substrate. Thus, HECT domains both have a conserved motif that associates with the E2 enzyme as well as a catalytic

MCP Papers in Press. Published on September 7, 2012 as Manuscript R112.021154

Copyright 2012 by The American Society for Biochemistry and Molecular Biology, Inc.

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cysteine residue. In contrast, RING domain proteins associate with charged E2s via their RING domain and then transfer directly to bound substrates. The RING E3 ligases can be divided into two types: the simple RING E3 ligases and the cullin-RING E3 ligases (CRLs) (9). CRLs are modular protein complexes comprising a substrate adaptor arm, a cullin protein that acts a scaffold, and the RING protein (9, 10). In contrast, simple RING E3s are typically single polypeptides that also contain substrate binding domains.

E3s add Ub to targets in 3 major ways: monoubiquitylation, multi-monoubiquitylation, and poly-ubiquitylation, and the exact type of modification is dictated by the combined activities and specificities of the E2 and the E3 (11, 12). In polyubiquitylation, a Ub molecule already linked to the substrate is the target for further ubiquitin conjugation. All seven lysine residues in Ub, as well as the N-terminal amino group, can receive Ub during the formation of a poly-ubiquitin chain. With the exception of lysine-63 (K63) linkages, all other lysine based linkages increase in abundance upon inhibition of the proteasome, suggesting that each of these linages may be acted upon by the proteasome (13, 14). However, K48, and possible K11, linked poly-ubiquitin chains are the best understood as proteasomally targeted devices. (11, 12).

Historically, the identification of substrate for specific ubiquitin ligases has been a challenge. This reflects, in part, the rapid turnover of most degradative ubiquitylation substrates, as well as the typically weak interactions between E3s and their substrates. Early landmark studies defining major classes of E3s and their targets often focused on genetic relationships for substrate identification, or in many cases, a particular protein of interest was known to be unstable and candidate or genetic approaches were used to identify the E3 of interest (15, 27, 28, 59). In addition, for a small number of well-studied E3s, conserved targeting sequences in substrates were identified, which made it possible to search for proteins containing such sequences as candidate substrates (16-19). Through the definition of functional domains in E3s, it is now appreciated that perhaps 600 or more individual E3s exist in the human genome, many of which are either completely unstudied or poorly understood (9). Thus, a major challenge for the field concerns the question how to identify substrates for poorly understood E3s. The majority of E3s bind only transiently to their targets and the inherent instability of many ubiquitylation targets increases the difficulty of their detection. However, in recent years, new proteomic approaches have come to the forefront, not only with respect to the identification of substrates for individual E3s, but also for global characterization of ubiquitylation sites on proteins as well as analysis of regulatory networks involving proteins in the ubiquitin system.

In this review, we discuss the major proteomic and genomic approaches that have been developed to identify substrates of the CRLs, a family of Ub-E3s with more than 200 members,as well as general approaches for identifying ubiquitylation sites in the proteome, and describe the impact this has had on our understanding of how protein stability is regulated globally (20). The SKP1-CUL1-F-box (SCF) complex is the founding member of the CRL super-family and has a substrate adaptor module consisting of two components: SKP1 and the F-box protein (FBP) (21-23). SKP1 acts as a bridge between CUL1 and the FBP, while the FBP is responsible for substrate binding and is referred as the specificity factor. CUL1 also associates with a key RING finger protein-RBX1 or -RBX2 which recruits ubiquitin-charged E2s (9, 20). Of the approximately 70 FBPs found in humans, less than half of them are characterized (24). However, from existing studies on FBPs, we know that they regulate numerous cellular processes including cell cycle progression, transcription, and cell survival (25, 26). The SCF complex serves as a paradigm for several other CRLs and similar methods for substrate identification are applicable for all CRLs (20). A key feature of substrate binding adaptor proteins is that they often contain conserved surfaces that selectively interact with particular sequence motifs in substrates, which are typically referred to as degron (16, 17, 19, 29, 60, 61).


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In some cases, the identification of degrons for a particular adaptor can provide a means by which to search for substrates either in whole genomes or in candidate substrates identified by proteomics (16, 19, 30).

Approaches for physical detection of SCF substrates

Immunoprecipitation followed by mass spectrometry (MS), referred to here as IP-LC/MS, has served as a primary route for the physical identification of targets for particular proteins of interest under largely physiological conditions. In this process, cell lines expressing most often an epitope-tagged version of the protein of interest are used for immunoprecipitation and interacting proteins identify by MS. However, there are several issues with this approach that limit its applicability to identification of substrates of E3s. First, because most substrates bind transiently with the E3 or have low binding energy, most substrates will not remain associated with the E3 during stringent washing of immune complexes. Second, because ubiquitylation itself most often promotes protein turnover, the levels of relevant in vivo substrates may be very low, limiting their detection. Third, many FBP-substrate interactions are signal dependent (for example, phosphorylation) (20). Because of these issues, the fraction of a substrate that is modified in the appropriate way for recognition by the FBP may be low, and this may limit the ability to detect an interaction with an FBP. Thus, the low abundance of candidate substrates that remain associated with substrate specificity factors after immunoprecipitation is often very low in comparison to the dozens or hundreds of contaminating proteins that are typically detected in a standard IP-MS experiment. As such, specialized approaches are typically needed to allow selective capture and identification of substrates in association with FBPs where contaminating proteins dominate proteomic datasets.

An early solution to the problem of substrate identification for SCF complexes developed by Michele Pagano and colleagues took advantage of a dual tagging strategy in which the FBP, and associated substrates, were immunoprecipitated and the immune complex used to perform in vitro ubiquitylation reactions using an epitope-tagged version of ubiquitin (31). The idea was that any particular FBP-associated protein that is also a substrate for that particular SCF complex would become ubiquitylated. By recovering ubiquitylated proteins via the epitope tag on the Ub molecule, it is possible to identify ubiquitylated proteins using proteomics. This approach was first applied to the FBP β-TrCP2, leading to the discovery of substrates such as Claspin, PCDC4, and REST (31-33). The identification of these proteins as substrates was aided by the fact that a consensus binding sequence for β-TrCP2 was known and could be searched for in any candidate substrate that emerged from the proteomics analysis (17, 60). However, this method has also been used in the identification of substrates for orphan F-box proteins, for example, identifying the circadian rhythm proteins CRY1 and CRY2 of FBXL3 (34).

While this approach, in principle, should provide a higher degree of selectivity, given that it is a two-step enrichment process, the often low-efficiency ubiquitylation reaction coupled with low recovery of ubiquitylated proteins render this approach more difficult than single-step approaches. However, single step approaches necessitate the need to successfully filter contaminants away from potential candidate interacting proteins that are also ubiquitylation targets. A solution to this problem has emerged with the development of the Comparative Proteomics Analysis Software Suite (CompPASS), which uses a large database of unrelated but parallel IP-LC/MS experiments to filter out contaminants (35). The algorithm uses abundance of interacting proteins based on spectral counts, the frequency of interaction across the database of parallel bait proteins, and the reproducibility of the interaction to provide empirical scores for each interacting protein. Proteins that pass a particular threshold for these scores are considered high confidence candidate interacting proteins (HCIPs). Typically, less than 3% of all


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proteins identified in association with a particular bait protein are regarded as HCIPs, pointing to the large number of contaminating proteins present in standard immune complexes (35).

The application of the CompPASS approach to the well-studied β-TRCP2 resulted in the identification of several known substrates that past the metrics for being an HCIP, including β-catenin, CDC25A, CDC25B, Claspin, REST, ATF4 and YAP1, although in several cases, between 2 and 5 peptides for a particular substrate were identified (Figure 1). Based on these results, the question to emerge is whether this approach can be used to identify candidate substrates for new E3s for which no candidate substrates are known, and can this approach be used to identify proteins with very low total peptide scans (for example, one or two spectral counts). The answer to this question came with the application of a comparative analysis of two IP-LC/MS experiments performed in parallel, wherein the specificity factor of interest is immunoprecipitated from cells that are untreated or from cells that are treated for 4 hours with proteasome inhibitors such as MG132 or Bortezomib. Proteins that are normally being degraded via the particular F-box protein complex would be stabilized and under these conditions, the steady-state abundance of substrates associated with the FBP would increase proportionally. Thus, by comparing spectral counts for proteins that pass the CompPASS metrics in the presence and absence of proteasome inhibition, it is possible to identify those proteins that both selectively associate with the FBP of interest and increase in abundance in response to proteasome inhibition. Such proteins have the characteristic of bona fide substrates. Thus far, this approach has been used to identify a new substrate for β-TRCP2 as well as the first substrate for the orphan FBP FBXO22. In the case of β-TRCP2, two spectral counts were identified for a novel candidate substrate, the mTOR inhibitor DEPTOR, in the presence of proteasome inhibitor while no DEPTOR peptides were found in the absence of proteasome inhibitor despite comparable spectral counts for β-TRCP2 itself (36-38). Functional studies and reciprocal proteomics revealed that DEPTOR is a bona fide substrate and the phosphodegron for recognition of DEPTOR by β-TRCP2 is generated by the dual action of both mTOR itself and casein kinase I (36, 38). Similarly, FBXO22 immune complexes contained 4 spectral counts for the lysine demethylase KDM4A specifically in the presence of proteasome inhibitor and follow-up studies validated this demethylase as the first substrate for this FBP (36, 37). Interestingly, most CRL substrates are present in protein complexes with other regulatory proteins and in many cases, these regulatory proteins also increase in abundance in the immune complex when the proteasome is inhibited, revealing that the complex containing the target is recognized. The CompPASS approach also allows proteins that function as regulators of a particular CRL, which stably bind to the adaptor protein in the presence or absence of proteasome inhibition. For example, analysis of FBW8 led to the identification of OSBL1 (Obscurin-like 1) and CCDC8 as subunits of the CUL7-SKP1-FBW8 complex. Previous studies had indicated that CUL7 is mutated in 3-M Syndrome, a disease characterized by short stature and other developmental disorders (39). Interestingly, both OSBL1 and CCDC8 are also mutated in 3-M disease but their physical link with CUL7 was unknown (40). Functional studies demonstrated that OSBL1 is required to localize the CUL7-SKP1-FBW8 to Golgi in neurons, where is promotes turnover of GRASP65 (40).

A further variation on this approach, sometimes referred to as “substrate trapping”, involves parallel analysis of IP-LC/MS results from cells expressing either the full-length wild-type FBP or the FBP in which the F-box has been mutated. FBPs lacking the F-box do not assemble with SKP1 and therefore cannot engage in an active CUL1 complex. When such FBP mutants are overexpressed, substrates are sequestered away from the endogenous FBP, and are thereby stabilized. As with the proteasome inhibitor method described above, this approach yields higher levels of candidate substrates associated with the F-box mutant protein. As with other interaction-based approaches, overexpression of the F-box mutant protein could lead to the


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identification of spurious false positives, and therefore downstream experiments are necessary to validate any particular interaction.

The "substrate trapping" approach can be performed either using spectral counting, or alternatively, in a SILAC based manner wherein cells expressing wild-type FBP are grown in light media and cells expressing the FBP mutant protein are grown in heavy media. The first example of the use of this approach came with an analysis of the FBXL5 FBP, leading to the identification of two iron-regulatory proteins IRP1 and IRP2 as substrates for FBXL5 (41, 42). A recent study has compared SILAC versus peptide counting using the substrate trapping approach for 3 FBPs – FBW7α, SKP2, and FBXL5 (43). This version of the substrate trapping approach is referred to as DiPIUS (differential proteomics-based identification of ubiquitylation substrates) in the SILAC mode and DiPIUS-NL (Non-Labeled) in the peptide counting mode. There are several interesting features of this analysis. First, with the exception of a small number of known substrates for these FBPs (c-MYC for FBW7α, p27 for SKP2, and IRP1 and 2 for FBXL5) little overlap was seen when the SILAC approach was compared to the peptide counting approach in HeLa cells. This could reflect rapid exchange between heavy and light substrate proteins present in the mixed lysate during the immunoprecipitation, which would tend to push potential substrate interacting proteins towards a heavy:light ratio approaching unity. In this case, actual substrates would be missed. Second, the sensitivity of detection may be an issue, as the total number of known substrates identified for FBW7α and SKP2 was low. Third, the method of subtraction used to remove false positives or non-specific interacting proteins relied only on 2 unrelated bait proteins, and if a particular protein was found with 2 of the 3 different FBPs under examination, the protein was considered a false positive. However, based on previous studies, this approach would be expected to substantially underestimate the actual number of non-specific interacting proteins, due to stochastic sampling in the mass spectrometry experiment (35). Previous studies suggest that a significantly larger number of unrelated bait proteins are required to survey the entire array of false positive proteins that may appear stocastically in association with any particular bait protein.

F-box proteins typically employ a conserved domain to interact with degrons on their targets, and mutations in key residues can result in loss of substrate binding. Thus, mutants of this type provide an alternative strategy for comparative proteomics, wherein proteins associated with wild-type but not mutant FBPs are candidate substrates. For example, the FBP Cyclin F contains a cyclin box fold, which has been shown to be important for substrate recognition (24, 44). By comparing peptide profiles of WT and cyclin-box deleted Cyclin F mutant, Pagano and colleagues identified two novel SCFCyclinF substrates, RRM2 and CP110, for Cyclin F (45, 46).

Finally, in instances where a particular protein is known to be degraded, for example in a signal-dependent process, one can screen for candidate E3s either using siRNA libraries targeting families of adaptor proteins, or alternatively, one can perform directed association studies using libraries of clones adaptor proteins. Such approaches have been used to identify REST and SPAR as substrates of β-TRCP, respectively (47, 48).

Quantitative Ubiquitin Remnant Profiling

Ubiquitin is conjugated to lysine residues via an isopeptide bond with its C-terminal glycine residue, which is preceded by an Arg-Gly sequence. Thus, trypsin digestion of ubiquitylated substrates leaves a Gly-Gly peptide conjugated to the lysine of interest and this modification can be identified based on its mass and fragmentation pattern. Early studies using this method for detection of ubiquitylated peptides identified relatively small numbers of ubiquitylation sites, and


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in these experiments, the low stoichiometry of ubiquitylation required that there be an enrichment step, for example using Ub-binding proteins to select ubiquitylated proteins or expressing epitope-tagged Ub (13). In principle, such approaches can present a bias in the number and types of proteins that are identified as being ubiquitylated, and any particular event may or may not represent an event present in the unperturbed cell. Moreover, while numerous proteins could be identified as being associated with Ub, the actual number of diGly-modified sites that could be identified was small. In order to overcome these limitations, antibodies that can be used to immunoprecipitate diGly-modified peptides from trypsinized extracts have been developed (49). Importantly, this approach can be used in combination with SILAC to identify diGly-modified sites that are altered in response to a particular cellular perturbation (Figure 2B). To date, more than 20,000 sites have been identified and in many cases, the increase in abundance of these peptides in response to proteasome inhibition has been determined (14, 49-51)

There are several noteworthy aspects of these studies. First, although a large number of sites have been identified, it appears that this approach still does not identify all ubiquitylated proteins in a given extract, a numerous known ubiquitylation targets are not present in the published datasets. This likely reflects the very low abundance of particular ubiquitylated proteins in vivo. Second, peptide identification is largely stochastic. In comparing biological duplicate IP-LC/MS experiments, a substantial fraction of the peptides are identified in only one of the 2 biological duplicates (14). This likely reflects the inability of the currently available antibodies to fully deplete diGLY-modified peptides from extracts. Third, while the number of sites that can be identified increases several fold upon prolonged proteasome inhibition (14, 51), the vast majority of these sites appear in a manner that depends upon ongoing protein synthesis (14, 51). Thus, it is possible that a significant fraction of the Ub-modified proteome reported thus far represents ubiquitylation events that occur in the context of Ub-proteasome stress, and may not represent physiological control mechanism. Importantly, it is possible to classify candidate ubiquitylation targets into those that are likely to be physiologically regulated versus those that are likely to be protein quality control substrates by examining the kinetics of ubiquitylation. Fourth, there does not seem to be a strong bias for particular peptide sequences in the case where it has been examined in detail (14). Thus, no specific ubiquitylation consensus sequence emerged from these studies, unlike the situation with sumoylation. Fifth, in addition to Ub, two other Ub-like proteins – NEDD8 and ISG15 – are also conjugated to lysine residues in proteins and when cleaved with trypsin, generate a diGly remnant peptide. One study (14) examined the contribution of peptides from these modifications using several approaches and found that a maximum of 6% of diGly containing peptides are derived from ISG15 or NEDD8 in HCT116 cells. This number is likely to be much lower, given that the methods used would not completely differentiate between neddylation and ubiquitylation but this also suggests that a large fraction of the Ub-modified proteome that has been identified thus far is derived uniquely from ubiquitin. Interestingly, this study also demonstrated that ubiquitin depletion resulting from proteasome inhibition leads to miss charging of ubiquitin E1 by NEDD8 (14). NEDD8 is then transferred to what would normally be Ub substrates apparently through the action of Ub-E2s. This indicates that conditions that alter the endogenous balance of Ub and NEDD8 can lead to a loss in fidelity of the Ub conjugation and transfer system. Similar results were found by Hjerpe et al (52).

Nevertheless, it is possible to employ quantitative diGly profiling to identify bona fide substrates of CRLs (Figure 3). Physiologically regulated substrates accumulate diGly modified peptides rapidly in a time course in response to proteasome inhibition. If the activity of the E3 responsible for physiological ubiquitylation is inhibited, then the accumulation of specific diGly modified peptides would not increase even when cells are incubated with proteasome inhibitor. Thus, this provides a means by which to search for substrates of a particular ubiquitin ligase by looking for


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peptides that increase in abundance in the presence of proteasome inhibitor but not when the E3 is inhibited either using chemical inhibitors or RNAi. This approach not only allows the identification of candidate substrates of an E3, but also provides the identity of lysine residues in a substrate that are ubiquitylated. One potential complication is that treatment of cells with proteosomal inhibitor may increase the amount of defective ribosomal products and misfolded proteins represented in the diGly-modified protein collection. However, in practice this issue can be minimized by treating both the control sample and the pathway-perturbed sample with proteasome inhibitors.

This approach has been used to identify candidate substrates of CRLs using the small molecule inhibitor of the NEDD8-activation cascade (53). NEDDylation is a critical post-translational modification, which occurs on cullin molecules and is required for CRL-dependent ubiquitylation (54, 55). Addition of MLN4924, a specific inhibitor of the E1 enzyme to cells renders this pathway inactive and blocks CRL-dependent ubiquitylation as well as substrate turnover. Experiments profiling diGly sites on proteins either in the presence of proteasome inhibitor or proteasome inhibitor plus MLN4924 led to the identification of 300-500 diGly-modified peptides whose accumulation was blocked by MLN4924 (14, 50). Among these were numerous known sites for individual CRL family members, and there was a ~30% overlap in the substrates identified in the two published studies, with a major difference being the cell line used, which could contribute to the low overlap. Importantly, biological duplicates in the same cell line demonstrated a 70% overlap in proteins that scored as candidate CRL targets, indicating that the method is reasonably reproducible (14). However, it should be noted that the stochasticity issue noted above means that many low abundance peptides may not be identified in 2 biological duplicates. An important advantage of the diGly-capture approach is that it allows the immediate identification of ubiquitylated lysine residues in substrates. This can be useful in many ways – first and foremost in the design of non-ubiquitylatable mutant proteins that can be used to test the hypothesis that ubiquitylation of a particular protein is important for a specific biological process. In addition, the identification of ubiquitylation sites can also aid in the identification of motifs that are either recognized by the E3 or the E2, and therefore may be important for specificity, as well as exposed regions of the target protein.

Quantitative global proteomics method to identify CRL substrates

Inhibition of an Ub ligase or a family of Ub ligases would be expected to result in an increase in the abundance of its substrates that are normally degraded by the proteasome. Thus, in principle, SILAC-based approaches wherein light cells have the E3 pathway intact while heavy cells have the E3 pathway inhibited would be expected to identify proteins whose abundance is increased, assuming that sufficient depth can be reached in order to identify and quantify low abundance proteins that preferentially make up E3 targets. To date, a single study of this type has been reported for identification of substrates of CRLs (56). In this experiment, A375 melanoma cells were used for SILAC and cells were either untreated or treated with the general CRL inhibitor MLN4924 for various times. Cells were mixed prior to lysis and fractionation by SDS-PAGE/in-gel digests. In total 584 LC/MS/MS runs were collected, and ~6000 proteins were identified with at least 2 peptides for each time point with a false discovery rate of 1% (7689 total proteins over 8 samples). This experiment also included a labeling switch for one of the time-points. From this analysis, 120 proteins were found to increase in abundance by greater than 2 fold, suggesting that these may be CRL targets. Parallel transcriptional profiling revealed, however, that the mRNA abundance for 20 of these was also increased upon MLN4924 treatment, indicating an indirect affect on protein levels. Of the remaining 100 proteins, 7 were known CRL substrates. Thus, this experiment potentially identified numerous new CRL substrates, although further studies are required to validate these results and to identify the


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particular CRL involved. Importantly, given the small number of known CRL substrates that were identified in this analysis, it would appear that sufficient depth of analysis was not achieved to globally identify CRL substrates using this approach. This likely reflects the fact that most substrates of CRLs are of very low abundance and therefore difficult to detect even in an experiment of this scale.

Genetic approaches for CRL substrate identification in mammalian cells

As an alternative approach, the Elledge laboratory has developed a novel methodology referred to as Global Protein Stability (GPS) profiling, which, in principle, allows the relative stability of proteins to be determined in a near genome-wide manner. GPS profiling is a fluorescence-based reporter system that combines fluorescence-activated cell sorting (FACS) with DNA microarray deconvolution to systematically examine changes in protein stability in live cells, for example in response to inhibition of the CRL pathway. GPS vectors express a single transcript encoding both the DsRed open reading frame (ORF) and a GFP-ORF (representing a particular human gene) separated by an IRES, and since these proteins are produced from a single transcript, the GFP/RFP ratio serves as an indirect readout of the stability of the GFP-ORF protein in individual cells. By making ORF libraries representing a large fraction of the human genome, it is possible to screen a substantial fraction of the genome for proteins whose abundance is altered by various stimuli. Screening is performed by FACS sorting cells into 8 bins that reflect the apparent stability of the particular GFP-ORF clone (50, 57). Upon perturbation (for example addition of the CRL neddylation inhibitor MLN4924 or upon expression of a dominant negative cullin) cells expressing GFP-ORF proteins that are stabilized will have a higher GFP/RFP ratio and will be shifted to a distinct bin. The distribution of ORF clones in individual bins before and after perturbation is determined by PCRing out the ORFs from each bin and individually hybridizing probes to custom microarrays that detect all the ORFs in the library. ORFs whose distribution changes toward a higher GFP/RFP ratio are candidate substrates, which can be validated using conventional approaches. The initial GPS system reported (v1.0) contained 8000 human proteins while the v2.0 library contains ~12,500 proteins. These systems have been used to screen for substrates of CUL1, CUL3, and CUL4-based E3s, as well as for CRL targets using MLN4924 (50, 57).

With the v2.0 system, inhibition of CRLs globally with MLN4924 led to the identification of 244 proteins whose abundance increased significantly in the GPS system (50). Of the 190 cullin-interacting proteins present in the v2.0 library, 90 of these displayed an increase in abundance upon MLN4924 addition, confirming that the GPS method can identify CRL substrates. In addition to using chemical perturbation, this approach can also be used in combination with shRNA or dominant negative mediated pathway inhibition. Overexpression of dominant negative CUL4 and CUL3 vectors resulted in the identification of 279 and 188 candidate substrates, of which ~75% in each screen were also found in the MLN4924 screen (50). Thus, there is significant overlap in the identification of candidate CRL substrates using the GSP approach and different means of perturbing the pathway. Even more powerful in identifying candidate CRL substrates is to merge quantitative diGly proteomics with the GPS system. For example, of the 295 genes in the v2.0 GPS library that correspond to proteins identified in parallel using the diGly capture approach in the presence of MLN4924, 108 (37%) were also identified in the GPS


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screen (50). Further studies are required to develop more inclusive libraries and to validate the very large number of substrates that have emerged using this approach together with diGly profiling.

Strengths and weaknesses of proteomic and genetic approaches for CRL substrate identification

Each of the approaches described in this review has specific advantages, and it is likely that no single technique will routinely identify all CRL substrates in a given cell type. First, diGly, interaction proteomics, including the CompPASS approach, and global proteomics may miss proteins that are of particularly low abundance or are insoluble under standard lysis conditions, whereas GPS profiling may be able to detect these, assuming the relevant genes are represented in the library. Alternatively, the GPS system may fail to identify proteins that cannot be epitope tagged and maintain regulation, or are not present in the library, whereas the proteomic approaches detect endogenous proteins. A further complementary aspect of these approaches is that diGly capture directly identifies ubiquitylation sites, which can then be immediately employed in further design of functional studies. Moreover, proteomic approaches, unlike the approach GPS approach, do not directly filter out transcriptional effects of perturbations, thus requiring significant further validation. In addition, while the GPS system is useful for finding ubiquitylation targets that are degraded by the proteasome, the diGly and interaction proteomic approaches have the potential to identify ubiquitylation substrates that are not acted on by the proteasome. This is important in the context of CRL biology as it was recently reported that the CUL3-KLHL12 complex mono-ubiquitinate its substrate SEC31 in order to alter its activity, rather than promoting its poly-ubiquitylation (58). Finally, it is evident that some cullin complexes have stably associated binding partners that play important regulatory roles. Interaction proteomics, unlike the other approaches, is uniquely suited for the identification of stable regulatory factors, which can be very important for placing CRLs and substrates into biological pathways, as evidenced by our identification of OBSL1 as a Golgi targeting subunit for the CUL7-SKP1-FBW8 complex (40). Thus, a complete understanding of CRL targets and biology will likely require a combination of approaches.

Conclusion

Ubiquitin proteomics has emerged as a major area of research in the Ub field. Nevertheless, there remain several challenges with respect to the identification of not only substrates of CRLs, but ubiquitin ligases in general. A central challenge concerns the identification of requisite signals that initiate the encounter of a particular substrate with an E3, including phosphorylation, hydroxylation, and glycosylation. Conserved motifs in both the substrate and the E3 will no doubt be critical for elucidating these such signals and in extrapolating such motifs to build interaction networks and identify additional targets. Depending upon the cell type being employed, only a fraction of the potential targets of a particular CRL may actually be identifiable due to the absence of the requisite signal. In addition, the amplitude of any particular response may have ramifications for the amount of any particular substrate that can be captured via a proteomics approach. A major limitation in the current toolbox of techniques is the absence of methods for identification of substrates in tissues. The approaches described above exclusive


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employ tissue culture cells, but lineage and context-specific targets likely means that many substrates of particular E3s will not be identified using current procedures. Thus, the use of quantitative deep proteome analysis may need to be developed to identify candidate substrates from tissues, for example, where a particular E3 has been inactivated by deletion. Alternatively, a more complete understanding of CRL targets, and E3s generally, may be achieved through the development of larger collections of cell line systems that mimic a wide cross-section of tissue functionality. An additional area that is ripe for future development are protein arrays. While this approach has not been used for CRL, previous work has shown that protein arrays can be used to identify substrates of HECT-domain E3s (62,63). One potential limitation that needs to be addressed is that the majority of CRLs are thought to require substrate modification for efficient interaction. In their purified form, the proteins on arrays may not be properly modified or alternatively, the stoichiometry of modification may be too low to allow for detection. In cases where the proper signal is known, it may be possible to pre-treat proteins on the array with the relevant modification enzymes. Clearly, new and improved proteomic techniques and instrumentation will continue to have a major impact on our understanding of the CRL system and its role in health and disease.

Acknowledgments

Work on the ubiquitin system in the Harper lab is supported by the National Institutes of Health (AG011085, GM054137, and GM070565), and by Millennium Pharmaceuticals. M.-K.M.T is supported by a predoctoral fellowship from the Agency of Science, Technology and Research (A*STAR), Singapore.

Conflict of interest statement

J.W.H. is a consultant for Millennium Pharmaceuticals.

Figure Legends

Figure 1: Application of CompPASS-based proteomics for identification of substrates and binding partners of SCFβ

-TRCP2. Dot plot depicting known β-TrCP2 interactors, including substrates, identified after IP-LC/MS and CompPASS analysis. Dots in red indicates proteins with increased spectral counts in proteasome inhibitor (Bortezomib) treated sample compared to the untreated sample. Proteins that were not detected in the absence of Bortezomib are indicated in bold.

Figure 2: Methods for enrichment of substrates in association with F-box proteins for identification by IP-LC/MS.

A) Diagram showing the ubiquitylation and subsequent degradation of SCF substrates.


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B) Diagram showing that inhibiting the proteasome by treating cells with proteasomal inhibitors such as MG132 or Bortezomib results in the accumulation of SCF substrates and increases the fraction of substrates bound to the cognate FBP.

C) Diagram showing how the introduction of an FBP lacking the F-box domain can increase the cellular pool of its substrate, allowing for more substrates to bind this mutant FBP.

Figure 3: Quantitative MS approach using SILAC.

A) Diagram showing how different treatments can alter the abundance of and the number of diGly remnants found on tryptic peptides for a ubiquitylated CRL substrate.

B) Outline of the quantitative MS approach using SILAC together with the anti-diGly antibody.

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0.01

0.1

1

10

100

-2 0 2 4 6 8 10 12 14

NFE2L1

FANCM

CLSPN

CDC34

YAP1HIVEP1

HIVEP2

REST

NFKB2OGT

BTRC

UBE2R2USP37 WEE1

CTNNB1 GSK3B SIPA1L2

FBXO5

IFNAR1 DLG1

NW

D s

core

Z score

CDC25A

CDC25B

β-TrCP2 (Treated with Bortezomib)

Figure 1 Harper and Tan


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SKP1

CUL1

RBX1

E2

Ub

Substrate

TagF-box protein

Proteasomal degradadtion

SKP1

CUL1

RBX1

E2

Ub

Substrate

TagF-box protein

Proteasomal degradadtion

More substrates available for binding to F-box proteins.

SKP1

CUL1

RBX1

E2

Ub

Substrate

TagF-box protein

SKP1

CUL1

RBX1

E2

Ub

Substrate

TagF-box protein

SKP1

CUL1

RBX1

E2

UbSubstrate

TagMutant F-box

protein

Substrate

TagMutant F-box

protein

Substrate

TagMutant F-box

proteinSubstrate

Substrate

More substrates available for binding to tagged mutant F-box proteins and more

mutant F-box proteins bound to substrates

A)

B) C)

Substrate

Substrate

Ubiquitylated Substrate

Ubiquitylated Substrate


Substrate

TagMutant F-box

protein

Substrate

TagMutant F-box

proteinSubstrate

Substrate


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GG

GGGG

GG

GGGG

Tryptic peptides for a ubiquitylated CRL substrate obtained from culture:

Untreated Proteasome Inhibition Proteasome Inhibitionplus MLN4924

Increase in protein abundance WITH an

increase in tryptic peptides with diGly

signature

Increase in protein abundance WITHOUT an

increase in tryptic peptides with diGly

signature

GG

Mix cells and lyse

Cells grown in in “light” media

Cells grown in in “heavy” media

+ Proteasome Inhibitor + Proteasome Inhibitor+ MLN4924

GG

GG

GG

GG

GG

LC-MS/MS

m/z

Compare heavy: light ratio for each

peptide

Enrich for diGly containing tryptic petides

anti-diGly antibody

A)

B)



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1 proteomics-based cullin-ring e3 substrate identification j. wade harper and meng-kwang

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