Global Identification of Modular Cullin-RING Ligase Substrates

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Global Identification of Modular Cullin-RING Ligase Substrates Michael J. Emanuele,1,2,6 Andrew E.H. Elia,1,2,3,6 Qikai Xu,1,2,6 Claudio R. Thoma,1,2,6 Lior Izhar,1,2,6 Yumei Leng,1,2,6 Ailan Guo,4 Yi-Ning Chen,5 John Rush,4 Paul Wei-Che Hsu,5 Hsueh-Chi Sherry Yen,1,2,5,6,* and Stephen J. Elledge1,2,6,* 1Division

of Genetics, Brigham and Women’s Hospital, Boston, MA 02115, USA of Genetics, Harvard Medical School, Boston, MA 02115, USA 3Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA 4Cell Signaling Technologies, Danvers, MA 01923, USA 5Institute of Molecular Biology, Academia Sinica, Taipei 115, Taiwan 6Howard Hughes Medical Institute *Correspondence: [email protected] (H.-C.S.Y.), [email protected] (S.J.E.) DOI 10.1016/j.cell.2011.09.019 2Department

SUMMARY

Cullin-RING ligases (CRLs) represent the largest E3 ubiquitin ligase family in eukaryotes, and the identification of their substrates is critical to understanding regulation of the proteome. Using genetic and pharmacologic Cullin inactivation coupled with genetic (GPS) and proteomic (QUAINT) assays, we have identified hundreds of proteins whose stabilities or ubiquitylation status are regulated by CRLs. Together, these approaches yielded many known CRL substrates as well as a multitude of previously unknown putative substrates. We demonstrate that one substrate, NUSAP1, is an SCFCyclin F substrate during S and G2 phases of the cell cycle and is also degraded in response to DNA damage. This collection of regulated substrates is highly enriched for nodes in protein interaction networks, representing critical connections between regulatory pathways. This demonstrates the broad role of CRL ubiquitylation in all aspects of cellular biology and provides a set of proteins likely to be key indicators of cellular physiology. INTRODUCTION Ubiquitin-dependent proteolysis is a major mechanism for posttranslational reorganization of the proteome. Ubiquitylation occurs through a cascade of three enzymes termed E1, E2, and E3, with the E3 imparting substrate specificity. Ubiquitylation can alter substrate activity or target it for degradation. We previously identified the SCF, a modular class of E3 ubiquitin ligases that use an interchangeable set of substrate adaptors termed F box proteins (Bai et al., 1996). The SCF utilizes CUL1 as a scaffold, recruiting the F box family of substrate specificity factors through an adaptor, SKP1 (Bai et al., 1996; Feldman et al., 1997; Skowyra et al., 1997). CUL1 also binds the RING domain protein RBX1 that, in turn, recruits an E2 (Ohta et al.,

1999; Seol et al., 1999; Skowyra et al., 1999; Tan et al., 1999). The human genome encodes nine cullins (Cul1, 2, 3, 4A, 4B, 5, and 7, PARC, and APC2), and each utilizes a unique set of substrate specificity factors and adaptors (reviewed in Petroski and Deshaies, 2005; Willems et al., 2004). These ligases are collectively termed cullin-RING Ligases (CRLs). With the exception of the APC/C, all CRLs require neddylation for full activation (reviewed in Deshaies et al., 2010; Skaar and Pagano, 2009). Nedd8 is a small ubiquitin-like protein that attaches to substrates using similar E1, E2, and E3 enzymes. Neddylation occurs on the cullin subunit and allosterically activates ligase activity (Duda et al., 2008; Saha and Deshaies, 2008). The chemical MLN4924 inhibits the Nedd8 E1 enzyme, Nae1 (Brownell et al., 2010; Soucy et al., 2009), inhibiting all CRLs. Though transcriptional regulation of genes is frequently examined, little is known about posttranslational regulation of protein abundance despite the existence of 500 ubiquitin ligases in mammals and many more in plants. Furthermore, analysis of CRL substrates in multiple organisms has revealed that many are critical regulators of their respective pathways and often lie downstream of signal transduction pathways. The global identification of highly regulated CRL substrates should unveil critical regulatory nodes that will represent key intersections between pathways (Benanti et al., 2007; Yen and Elledge, 2008). To approach this problem, we recently described a genetic screening platform for identifying proteins with regulated stabilities, termed global protein stability profiling (GPS). GPS is a fluorescent-based reporter system that combines fluorescent-activated cell sorting (FACS) with DNA microarray deconvolution to systematically examine changes in protein stability in live cells (Yen et al., 2008). An alternative for identifying ubiquitin-regulated proteins is mass spectrometry (MS)-based proteomics using stable isotope labeling with amino acids in cell culture (SILAC) (Ong et al., 2002). This has been limited due to the low abundance of ubiquitinated peptides. Because the last three amino acids of ubiquitin are arginine-glycine-glycine (RGG), tryptic cleavage of ubiquitylated proteins results in the presence of a GG remnant on the ubiquitin-modified lysine of tryptic peptides. Antibodies Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc. 459

A

GPS Vector DsRed

IRES

iii.

EGFP-ORF Fusion

Isolate Genomic DNA and PCR Amplify ORFs from each Bin

Primers for gDNA PCR

iv.

i. Generate a GPS-ORFeome Library One GPS-ORF per Cell

Fluorescent Label Amplifed ORFs Hybridize to DNA Microarray (1 Array per Bin)

v.

Examine Signal Distribution Across Bins Compare Conditions

ii. FACS Sort Library- EGFP/DsRed Ratio

CRL Regulated Protein Relative Microarray Signal

Two Conditions: 1- Control (DMSO or Empty Vector) 2- Cullin Inhibited (MLN4924 or DN-Cullin)

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-MLN4924 Conc.

1 Percent Cells

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TRIP6

HBP1

- +

- +

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7 8

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0

+2

Delta PSI

USP18 CDC25

DAPK1

F-box Substrate Adaptors

IRS1

NFE2L2/NRF2

JUN ATF4

MLN4924

ORC1L PDCD4

CRL2/5

APBB1IP SAMSN1 THOC1

- +

- +

- +

- +

SETD8

CRL4

EPAS1/HIF2A SOCS Substrate Adaptors

RPS6

BTB-Kelch Substrate Adaptors

CDKN1A

DMSO 0.5 µM MLN4924 1 µM MLN4924 10 µM MLN4924

PDIA4

CRL3

CRL1

CDKN1B

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Negative Control MDH1

7

FancM

EGFP/DsRed

F

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Emi1

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0

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MCL1

GPS-RBM19 100

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SNAI3

GPS-NRF2 100

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DMSO- PSI

C 80

5 6 BIN #

R2 = 0.92 1

Vinculin

100

2 3 4

6 5 4 2

CDT1 (CRL4 target)

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CEP120 POLR2A FUT11

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Anti-EGFP (ORF)

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Ran

Figure 1. GPS Profiling and Chemical Genetic Inhibition of the Nedd8 Pathway Identifies CRL Substrates (A) Schematic representation of a GPS screen. The GPS viral vector expresses a single transcript containing both DsRed and an EGFP-ORF, separated by an IRES. (i) A GPS cell library expressing each protein encoded by the human ORFeome collection was constructed, expressing a single GFP-ORF per cell. (ii) The library, with and without cullin inhibition, is FACS sorted into bins based on its EGFP/DsRed ratio. (iii) Genomic DNA is isolated from sorted cells, and ORFs are PCR amplified using primers that target the viral backbone. (iv) PCR-amplified ORFs are transcribed, labeled, and hybridized to DNA microarrays containing probes to each ORF. (v) Probes are analyzed and graphed across the bins to identify probe distributions that shift in response to CRL inhibition. (B) HeLa and 293T cells were treated for 4 hr with increasing concentrations of MLN4924 (0, 0.1, 1, and 10 mM) and then immunoblotted for NRF2, CDT1, and Vinculin (loading control).

460 Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc.

generated against this remnant can specifically recognize this site, allowing for the enrichment of these tryptic peptides from ubiquitylated proteins (Xu et al., 2010). Using the approaches described above, we identified proteins regulated by CRL ligases. This set of nearly 500 proteins is enriched for highly connected hub proteins in interaction networks and represents a group of proteins that play key roles in cellular physiology. RESULTS The GPS screening platform utilizes an internally normalized fluorescent-based retroviral reporter system and combines FACS with DNA microarray deconvolution to systematically examine changes in protein stability (Figure 1A) (Yen et al., 2008). GPS vectors express a single transcript encoding DsRed and EGFP-ORFs separated by an internal ribosome entry site (IRES). We constructed a human tissue culture cell library expressing each of the proteins encoded in the human ORFeome collection, with each cell expressing a single EGFP-ORF. Importantly, the EGFP/DsRed ratio of each cell acts as an indirect reporter for the half-life of the expressed ORF. Screening is performed by FACS sorting the library into bins based on the EGFP/ DsRed ratio (low to high). Genomic DNA from each bin is harvested, and the ORFs are PCR amplified from each bin with common primers targeting the viral backbone. PCR-amplified ORFs are fluorescently labeled and hybridized to customdesigned DNA microarrays (one microarray per bin), and the intensity of each probe is measured and graphed across the sorted bins. When performed in a comparative manner, we can assess changes in protein stability based on changes in the probe distribution intensity across the sorted bins (Figure 1A). To improve GPS, we designed a lentiviral reporter vector (pGPS-LP) (Figure S1A available online) that can infect nondividing cells and has a larger packaging capacity (>10 kb). pGPS-LP contains a PGK-puromycin cassette for selection and a T7 promoter downstream of the ORF that significantly improves the efficiency of PCR. pGPS-LP showed a 50-fold increase in viral titer and a larger packaging capacity (Figure S1B). We constructed an updated GPS library using pGPS-LP and the latest CCSB human ORFeome collection, which includes 15,483 human ORFs covering 12,794 genes, and we found that the updated library has significantly improved preservation of larger ORFs (Figure S1C). To improve the accuracy of GPS, we designed multiple probes for each ORF with increased stringency (Figures S1D and S1E and Table S1). These microarrays contain 46,000 probes, with an average of
3.3 probes per gene, and 80% of genes have R 2 probes. We also developed a scoring system that

considers changes in protein stability index (DPSI), hybridization intensity, agreement among multiple probes, and percentage of cells with altered EGFP/DsRed ratios after treatment (percent shift). GPS Screen for CRL Substrates Using MLN4924 We utilized MLN4924 to interrogate the roles of CRLs on the proteome using the second-generation GPS platform. MLN4924 treatment stabilized known CRL substrates, including the CRL3 target NRF2 and the CRL4 target CDT1 (Figure 1B). 293T cells stably expressing GPS-NRF2, GPS-RBM19, or GPS-CDC34 showed an increased EGFP/DsRed ratio when treated with MLN4924, whereas the GPS library and GPS-negative controls (GPS-Empty and GPS-RPS2) were unaffected (Figure 1C and Figure S2C). We ultimately screened our GPS library with 1 mM MLN4924 for 4 hr, conditions that do not affect the cell cycle (Figure S2A). We GPS screened a 293T lentiviral GPS library treated with either DMSO or MLN4924 (Figure 1A). We hybridized PCR-amplified ORFs (sorted versus unsorted) onto secondgeneration DNA microarrays, using a single microarray for each sorted bin in each condition (16 total). For each probe, we determined the protein stability index (PSI; approximates the statistical mean of the distribution) and graphed the probe distribution across the eight bins, comparing treated and untreated conditions (see Figure 1Av). The second-generation microarrays showed strong agreement between probes for a single gene. The standard error between probes for genes with multiple probes is less than 0.1 for > 90% of genes. Comparison of probe PSI between MLN4924- and DMSO-treated samples showed a linear relationship, with an R2 value of 0.92 (Figure 1D). When comparing treated and untreated conditions within a single bin, each individual bin showed an R2 value R 0.91. Figure S3 shows three randomly chosen probes for a subset of validated proteins (see below) that are stabilized by MLN4924. The probe distributions across the eight bins are almost indistinguishable, suggesting strong agreement and low crossreactivity. Probes were ranked according to their DPSI, and thousands of high-priority graphs were visually inspected. Proteins with multiple corresponding probes (when available) that showed a significant positive shift after MLN4924 treatment were considered putative CRL substrates, yielding 244 high-priority candidates (Table S2). Importantly, the MLN4924-GPS screen identified a large number of well-characterized CRL substrates, including hypoxia-inducible factor (HIF), NRF2, CDC25, the CDK inhibitors CDKN1A and CDKN1B (p21/CIP1 and p27/ KIP1, respectively), ATF4, CYCLIN D, numerous substrate adaptors (including F box and Kelch-BTB proteins), STAT1,

(C) The indicated 293T GPS cell lines were treated with MLN4924 for 4 hr and analyzed by flow cytometry. Histograms show the EGFP/DsRed ratio for cells expressing each of the specified ORFs or an empty vector control. (D) (Left) Scatter plot of the PSI for each probe in the screen under the two conditions (DMSO versus MLN4924). (Right) Histogram of the DPSI for each probe analyzed in the screen. (E) Schematic representation of a subset of the known substrates that were identified in this screen. (F) 293T cells expressing the indicated EGFP-tagged candidate substrate were immunoblotted to examine protein stabilization following 4 hr treatment with MLN4924. See also Figures S1, S2, S3, and S6 and Tables S1, S2, and S7.

Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc. 461

JUN, and PDCD4 (Figure 1E). A recent study examining cullin interactors using IP-MS/MS (Bennett et al., 2010) identified specificity factors coprecipitating with specific cullins. Because many specificity factors are ubiquitylated by their cognate ligase, we examined this data set. Of the 190 unique cullin-interacting proteins present in the ORFeome, 96 (50%) showed a positive shift in their probe distribution in the GPS screen, indicating that many were stabilized by MLN4924 treatment and confirming that this screen identified CRL substrates. To validate the reproducibility of the screen, we individually tested ORFs for their response to MLN4924. We individually subcloned 74 unique ORFs into pGPS-LP. Cell lines expressing individual ORFs were treated with MLN4924 or DMSO and analyzed by FACS to assess changes in the EGFP/DsRed ratio. Forty-seven (65%) had an increased ratio after MLN4924 treatment, suggesting CRL-dependent stabilization. To confirm that this reflected an increased abundance of the tagged protein, we immunoblotted 18 EGFP-tagged ORF cell lines and all showed increased protein levels (Figure 1F). We also examined the endogenous levels of 10 proteins and found that 8 increased in abundance following MLN4924 treatment (Figure S3B). The cumulative results of our extensive validation analysis with MLN4924 are summarized in Table S2, and a subset of FACS-validated proteins is shown in Figure S3A. Proteomic Identification of CRL Substrates To further identify substrates of CRL ligases, we employed a peptide IP proteomic strategy. We utilized an immunoaffinity reagent specific for tryptic ubiquitin remnants, the PTMScan ubiquitin remnant motif antibody. This antibody specifically recognizes a diglycine tag that remains on ubiquitylated lysine residues after trypsin digestion of proteins into peptides and enriches them
1,000-fold from lysates. To identify ubiquitylation sites that are specifically CRL dependent, we utilized a quantitative approach based on SILAC-MS that we refer to as quantitative ubiquitylation interrogation (QUAINT). Light cells were treated with MG132 alone and heavy with MG132 and MLN4924 (Figure 2A). MG132 was included to capture ubiquitylated substrates that would otherwise be degraded by the proteasome. The 4 hr incubation in MLN4924 did not affect the HeLa cell cycle (Figure S2B). Three independent replicates identified 9,957 unique peptides, corresponding to 2,814 proteins, at a false discovery rate of 0.11% (Figure 2B and Table S3). Internal validation of peptide identification was provided by the fact that 5,114 (>50%) peptides overlapped between at least two experiments (Figure 2B). Because MLN4924 leads to CRL inactivation, it reduces the heavy/light ratio (H/L) for peptides that contain lysines ubiquitylated by a CRL. Overall, 1,015 unique peptides were quantitatively reduced more than 2-fold in at least one replicate (Figure 2C). The H/L average and standard deviation for all 5,114 unique peptides appearing in multiple replicates was calculated. We selected 364 peptides displaying an average H/L reduction of 2-fold between multiple replicates and added 448 peptides that were reduced more than 2-fold yet quantified in only one replicate. This corresponds to 812 peptides (from 410 proteins), which we designate as our QUAINT-MLN4924-regulated CRL candidates (Table S3). Importantly, the individual values con462 Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc.

tributing to the mean H/L ratios were very similar across replicates, as 77% of the 364 peptides were reduced R 2-fold and 94% trended (reduced R 1.5-fold) in a second replicate. This yielded many known substrates, including SETD8, NF-kB, cyclin D, CDT1, HIF1A, POLR2A, YBX1, CDC25A, ORC1, b-catenin, and many CRL adaptors. The enrichment for known substrates and the overlap between replicates suggests that QUAINT proteomics has identified a large number of CRL substrates with a high degree of confidence. We compared the overlap between the QUAINT and GPS MLN4924 screens. Of these top 410 QUAINT scoring proteins, 295 (72%) are present in the human ORFeome collection and 108 (37%) had a shifted probe distribution in the MLN4924 GPS screen (p < 1050). This suggests that at least 37% of the QUAINT-regulated proteins represent bona fide CRL-regulated proteins. Fusion to GFP and a very high initial stability (preMLN4924) can preclude the identification of some substrates in GPS, suggesting that this is likely to be an underestimate. The 108 proteins that overlapped in both screens are depicted in Figure 2D. Known substrates, substrate adaptors, proteins with known interactions with CRLs, those validated by endogenous immunoblot, and proteins that scored in other GPS screens for CRL substrates (see below) are colored. Strikingly, these account for 63 (58%) of the proteins on the QUAINT-GPS overlap list, suggesting that our complementary approaches identified a high confidence list of both known and previously unrealized CRL substrates. CRL4 GPS Screen The successful identification of CRL substrates prompted us to interrogate specific ligases with GPS. Following DNA damage, CRL4 causes the rapid degradation of p21CIP1 (CDKN1A), CDT1, and SETD8/SET8 (reviewed in Abbas and Dutta, 2011). The substrate specificity factors for CRL4, termed DCAFs (DDB1 and Cul4-associated factors), contain a WDxR motif. We coexpressed dominant-negative Cul4A and Cul4B (DNCul4) to disrupt CRL4 and found that it prevented the destabilization of CDT1 following treatment with UV light or the UV mimetic 4NQO, confirming its inhibition (Figure 3A). We also generated a GPS-p21CIP1-expressing cell line (CDT1 fusion to EGFP prevents its recognition by CRL4). GPS- p21CIP1 is destabilized following UV, and this is blocked by DN-Cul4 (Figure 3B). Conditions were optimized for the maximum duration of DN-Cul4 treatment that would not affect the overall stability of the library. A 293T-GPS library was treated with DN-Cul4 or a control empty vector-expressing virus for 20 hr, and both conditions were treated with 4NQO for 2 hr prior to FACS, as the degradation of some CRL4 substrates is triggered by DNA damage. The PSI for probes in DN-Cul4 and control conditions showed a linear relationship (R2 = 0.91; Figure S4B), suggesting that the screen data are of high quality. After DPSI ranking and inspecting probe graphs, we identified 279 high-priority candidates and successfully validated 113 by individually retesting (37%) using FACS. Twenty of these validated candidates were assayed for stabilization by immunoblot of EGFPtagged proteins after DN-Cul4, and all were validated (Table S4). Importantly, reanalysis of the MLN4924-GPS screen graphs for each of the 279 candidates revealed that substrates shifting

A “Heavy” Media

+ MG132 + MLN4924

“Light” Media

Mix heavy and light lysates 1:1 Digest with Trypsin

IP peptides with ubiquitin remenant antibody

LC-MS/MS

+ MG132 G G K

B

9,957 Unique Ubiquitylated Peptides 429

Replicate 1 5,244 Unique 1,995 Peptides

1,264

C Replicate 2 5,811 Unique Peptides

1,015 Unique Regulated Peptides

Replicate 1 537 Unique Peptides

2,253

163

39 362 98

1,865 567

Replicate 2 419 Unique Peptides

119 38 1,584

196 Replicate 3 451 Unique Peptides

Replicate 3 6,269 Unique Peptides

D

MLN4924 GPS

ACBD5 AFF4 AKR1C3 AMBRA1/DCAF3 ARHGEF7 BHLHE40 BNIP3 BTBD1 C10ORF119 CALCOCO2 CARS CCT6A CDC25A CDC34 COPS3 COPS7A COX7A2 CPNE3 CPSF3

CRELD2 CUL4A CUL4B DCUN1D1 DDX1 DHCR24 DKC1 DSCC1 DTL EBNA1BP2 EEF2 EID1 ELAVL1 ENO1 EPAS1/HIF2A EPN1 ERGIC1 FBXO3 FBXO7

FZR1 GMPS GNAT2 GNL3L H2AFY HACL1 HBP1 JUP KBTBD4 KBTBD6 KCTD9 KLHL13 KPNA6 LAPTM4A LMNA LRRC41/MUF1 LTV1 LZTR1 MCM2

MCM3 MCM5 MCM6 MCM7 MYO1C NDUFA1 OPTN ORC1L PEBP1 PFKFB3 PLRG1 POLR2A PPIL5 PPP1CA PPP1CC PSMA4 PSMD3 RAB7A RAC1

RIMKLB RPL13A RPL14 RPL15 RPL19 RPL3 RPL35 RPL4 RPS16 RPS3 RRM2 SAMHD1 SDHA SETD8 SETX SMARCE1 SNRPA1 SOD1 TARS

TDG TMCO3 TNFAIP1 TPM3 TRPC4AP UBA3 USP15 USP48 VPRBP WDR3 WSB1 ZER1 ZNF451

MLN4924 QUAINT

Figure 2. Mass Spectrometry Identification of CRL Substrates Using QUAINT (A) Schematic representation of the QUAINT experimental design to identify CRL-regulated proteins. (B) Overlap between peptides identified in three QUAINT replicates. (C) Overlap between MLN4924-regulated peptides in the three QUAINT replicates. (D) Graphic depiction of the overlap between the MLN4924 GPS and QUAINT screens. (Green) Known substrate adaptors; (blue) known substrates; (red) proteins with known interactions with CRL ligase components; (purple) proteins that were identified in additional DN-Cul GPS screens; (orange) proteins validated in this study by endogenous immunoblot. See also Figures S2, S3, and S6 and Tables S3 and S7.

in both CRL4 and MLN4924 GPS screens validated at a rate of 72% (Figure 3E and Table S4), suggesting that cross-referencing overlapping screens can reduce the false-positive rate of GPS. A subset of validated CRL4 candidate substrates is shown in Figure 3C. High-confidence hits scoring in both the MLN4924 and CRL4 GPS screens and validated when re-examined by flow cytometry are depicted in Figure 3E. To assess cell type specificity, we tested a subset of validated proteins by FACS in additional lines (HeLa and U2OS). Ferritin is the primary iron uptake and storage protein in cells, and its dysfunction has been associated with neurodegenerative dis-

ease. Ferritin heavy chain (FTH1) scored in both the MLN4924 and CRL4 GPS screens and was validated in 293T and HeLa cells (Figures 3C and S4C). FUT11 is a cytoplasmic fucosyltransferase enzyme that scored in the CRL4 and MLN4924 GPS screens and was validated in 293T, HeLa, and U2OS cells (Figures 3C and S4C). All 10 proteins tested in HeLa and U2OS cells (FUT11, FTH1, KIAA0101/PAF15, TM2D1, ITM2A, KIAA1680, LDHB, MAPK6, FAM53A, and MCM7) validated in at least one of those cell types. Several solute carriers scored in both the MLN4924 and CRL4 GPS screens, including SLC38A2, SLC29A2, SLC17A3, Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc. 463

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Domain WD40 Protein kinase ZF SH2 APH Ank AAA

Descrip on WD domain, G-beta repeat

P-value << 1E-10

Protein kinase domain

4.74E-09

Zinc finger SH2 domain Phosphotransferase enzyme family Ankyrin repeat ATPase family associated with various cellular ac vi es (AAA)

2.86E-06 1.66E-02 1.66E-02 1.66E-02 1.66E-02

Cluster nucleus DNA metabolic process Endoplasmic re culum DNA replica on Golqi apparatus Nucleo de binding DNA repair Posi ve requla on of cell migra on Regula on of cell death Cell cycle

Enrichment Score 2.56 2.29 2.05 1.95 1.56 1.54 1.31

P-value 2.10E-03 9.70E-05 5.60E-01 3.40E-02 1.20E-02 8.60E-02 3.90E-02

1.31

3.90E-02

1.11 1.04

8.50E-02 3.50E-05

E

CRL4 GPS Screen

ABCA8 ABCE1 ADAM7 ANKRD50 AP3M1 ARMET ASB9 BCOR C10ORF84 C14ORF49 C16ORF89 C17ORF71 C1ORF173 C3ORF49 C9ORF30 CCDC90B CCNH CD300A CD70

CDKN1A CFDP1 CHRD CREB3L1 CSNK1A1L CTPS2 DPYD EFHC1 EHMT2 EID1 EME1 EML2 ETS2 FAM53A FTH1 FUT11 HBP1 HDAC3 HSCB

ILK ILVBL INTS4 KIAA0101 KIAA0141 KITLG KLF6 KLHL13 KLHL18 LIST LTV1 MAP2K5 MAP3K7 MCM2 MCM5 MCM7 MICA MLL5 MUS81

NEK11 NFE2L2 NUDT6 PRIM1 PTP4A1 RBM34 RWDD4A SGMS1 SH3YL1 SLC38A2 ST6GAL2 STAT1 SYT17 TAF9B TEKT2 THUMPD2 TM2D1 TMEM47

TRPC4AP TTC19 UBE2H USP30 USP37 WBP5 WDR32 WDR41 XYLT2 ZSCAN16

MLN4924 GPS Screen

Figure 3. GPS Screen to Identify CRL4 Substrates (A) 293T cells expressing either FLAG-DN-Cul4 or an empty vector control were treated with UV or 4NQO to induce DNA damage. Vinculin was blotted as a loading control. DN-Cul4 was detected using Flag antibodies. (B) 293T cells expressing GPS-CDKN1A were treated with UV light in the presence and absence of DN-Cul4. (C) A subset of validated candidate substrates that were tested in 293T cells using flow cytometry. (D) Functional category and protein family enrichment analysis for the proteins validating in the CRL4 GPS screen. (E) The overlap between CRL4 and MLN4924 GPS screens. See also Figures S4 and S6 and Tables S4 and S7.

464 Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc.

and SLC39A13. SLC38A2 is a sodium-dependent amino acid transporter, and SLC29A2 is a nucleotide transporter. In addition, lactate dehydrogenase b (LDHB), which catalyzes the interconversion of lactate and pyruvate, and NAD and NADH in the glycolytic pathway scored in the CRL4 screen and the QUAINT MLN4924 screen and validated in 293T and HeLa cells (Figures 3C and S4C). Taken together, this suggests a role for CRL4 in regulating various aspects of metabolism and cellular homeostasis. Numerous nuclear proteins, particularly transcriptional regulators, scored in the CRL4 screen. CCNH-encoding cyclin H, a component of both the CDK-activating kinase (CAK) and the regulator of RNA Pol II (TFIH), scored in both the CRL4 and MLN4924 GPS screens and validated by endogenous immunoblotting after MLN4924 treatment. FAM53A is a nuclear protein involved in dorsal neural tube development, which was stabilized in the CRL4 and FAM53A GPS screens and validated with DNCul4 (Figures 3C and S4C) (Jun et al., 2002). ETS2, which scored in the CRL4 and MLN4924 GPS screens and validated with DNCul4, is a winged helix-turn-helix transcription factor that is involved in telomere maintenance through hTERT transcriptional regulation and in maintenance of trophoblast and colonic stem cells (Figure 3C) (Mu´nera et al., 2011; Wen et al., 2007; Xu et al., 2008). HDAC3 interacts with SMRT and N-CoR in a nuclear corepressor complex and scored and validated in both DN-Cul4 and MLN4924 screens (Figure 3C) (Karagianni and Wong, 2007). Endogenous HDAC3 was also validated by cycloheximide chase following treatment with DN-Cul4 (Figure S4D). INTS3 and INTS4 are components of the integrator complex, which binds to the C-terminal domain of RNA polymerase to aid in processing of small nuclear RNAs. INTS3 and INTS4 scored in the MLN4924 and CRL4 GPS screens, and INTS3 validated by FACS in 293T cells (Table S4). Together, this strongly argues that CRL4 plays an important role in regulating a variety of transcription factors. The MCM2-7 helicase complex unwinds DNA ahead of the replicative DNA polymerase. MCM2, MCM5, and MCM7 scored in the DN-Cul4 GPS screen, and MCM2, MCM3, MCM5, and MCM7 scored in the MLN4924 GPS (Figure S4E). All components showed regulated ubiquitylation in the QUAINT analysis and were validated by DN-Cul4 in 293T cells (Figure 4F and Table S4). GPS-MCM7 also validated in HeLa cells following DN-Cul4 treatment (Figure S4E). Though very little is known about potential MCM complex ubiquitylation, an increase in ubiquitin-dependent turnover of MCM3 in G1 phase has been observed (Cheng et al., 2002). CRL3 GPS Screen CRL3KEAP1 constitutively degrades the oxidative stress response transcription factor Nrf2 (Cullinan et al., 2004; Kobayashi et al., 2004). Following oxidative damage, Keap1 is inhibited, allowing NRF2 to rapidly accumulate and initiate transcription. CRL3 utilizes Kelch-BTB proteins (Bric-a-Bric, Tamtrack, and Broad) as substrate specificity factors (Xu et al., 2003). To confirm that DN-Cul3 inhibited CRL3, we immunoblotted treated cells for NRF2 (Figure 4A). GPS-NRF2 was stabilized following DN-Cul3 treatment to the same extent as strong oxidative stress induced by TBHQ treatment (tert-Butylhydroquinone; Figures 4C and 4D).

Comparing the PSI for probes from DN-Cul3-treated and control-treated samples revealed an R2 value of 0.91. The screen identified the well-characterized substrates NRF2, DAPK1, and DVL1. After ranking probes and inspecting graphs, we identified 188 high-priority candidate substrates (Table S5). We crossreferenced the high-priority CRL3 candidates against the probe graphs for the MLN4924-GPS screen and identified 88 proteins that overlap in both screens, which we predict will validate at a high rate (70%–80% based on the results of our SCF and CRL4 screens; Figure 4F and Table S5). This list is enriched for proteins containing the BTB-Kelch fold found in CRL3 specificity factors (Figure 4G). Based on our analysis and validation of the CRL4 and SCF (below) screens and the identification of numerous substrate specificity factors as well as known substrates, we predict that this overlapping list contains many CRL3 substrates. SCF GPS Screen We previously applied the first-generation GPS system to the identification of SCF substrates utilizing DN-Cul1 to inhibit ligase activity (SCF-GPS.1) (Yen and Elledge, 2008). We used the second-generation GPS library, with conditions optimized from our first screen, to identify additional substrates (SCF-GPS.2). The 293T-GPS library was treated with either a lentivirus expressing DN-Cul1 or empty vector. Comparison of the PSI for all probes between the two conditions yielded an R2 value of 0.95. Validation was performed by individually retesting ORFs under the conditions of the screen and yielded a validation rate of
59% (80 out of 139 high-priority candidates tested; Table S6). This was an improvement over SCF-GPS.1, which had a 47% validation rate. In addition, all of the SCF-GPS.1-validated proteins that were individually tested in the second-generation lentiviral pGPS-LP vector recapitulated stabilization in response to DN-Cul1. Performing this screen with the updated pGPS-LP library validated 67 additional putative SCF substrates not recovered in our original screen (Table S6), such as TRIM9, BZW1, ZNF238, HFM1, MICALL2, and SH3BP5L. TRIM9 interacts with the F box protein b-TRCP by yeast two-hybrid (Rual et al., 2005) and contains the b-TRCP degron sequence (DSGxxS), strongly suggesting that it is controlled by SCFb-TRCP. Validated proteins that overlap between the MLN4924 and SCF GPS screens are shown in Figure 5B. Proteins scoring in both SCF and MLN4924 screens validated at a rate of
81% (Table S6). Because the identification of a protein in multiple screens is a strong predicator of its likelihood to validate, we have generated a summary table of 472 proteins that either individually validated in GPS or scored in one GPS and a second GPS or QUAINT screen (Table S7). Identifying Specific E3 Ligases: NUSAP1 Is a Substrate of SCFCyclin F Because phosphorylation can drive proteolysis, we cross-referenced our overlap lists with phosphoproteomic cell-cycle and DNA damage screens (Dephoure et al., 2008; Matsuoka et al., 2007) and identified NUSAP1, which shows regulated phosphorylation in mitosis and in response to DNA damage. NUSAP1 is a cell-cycle-regulated microtubule-binding protein with roles in chromosome congression and segregation (Raemaekers et al., Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc. 465

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ADAD1 ADH4 ADRB2 AQP8 C14ORF100 C1QL2 C3ORF49 C5ORF35 C8ORF76 CBX7 CCDC90B CD72 CDC16 CDC23 CDCA7 CDK7 CEP57 CHEK2 COQ3

KLHL17 DCN KLHL22 DLAT KRT15 DMKNDOK4 LANCL2 DPP10 LEF1 EBF3 MED1 FAM86B1 MGC16121 FBXO7 MMP2 FCGR1A MTX2 GABPB2 NEFL GALNT7 NFE2L2 GNG4 GORAB NOL12 NSBP1 HBP1 NXPH1 HEATR6 PDLIM1 HERV-FRD PDS5B HJURP PIWIL4 IKZF3 PPP1R11 KBTBD6 KBTBD7 PRDM15

G Domain ZF BTB TPR Protein Kelch Filament

Descrip on Zinc finger BTB/POZ domain Tetratricopep de repeat Protein kinase domain Kelch mo f Intermediate filament

P-value << 1E-10 8.20E-04 7.21E-02 7.21E-02 7.21E-02 7.21E-02

PRKACB PRPH RNF180 RFX6 Rhobtb1 RNF24 RNF8 SCG2 SCUBE3 SELV SLC25A46 SOCS2 SPIB SPOP SVOPL TAF9B THUMPD2 TMEM22 TMEM55A

TMX1 TOR1A TPM3 TRIM39 TRIM46 TSTA3 TTC19 WSB1 XPO7 YY1 ZKSCAN5 ZNF511

Cluster Kelch related Intracellular non-membranebounded organelle intracellular organelle lumen nucleus structural molecule acvity chromosome Zinc-finger Cell cycle transcripon regulator acvity cell surface receptor linked signal transducon

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Figure 4. GPS Screen to Identify CRL3 Substrates (A) 293T cells treated with empty vector or DN-Cul3 were immunoblotted for NRF2 and Ran (loading control). (B) 293T cells expressing GPS-CDKN1A (negative control), GPS-NRF2, or the GPS library were treated with DN-Cul3 for 18 hr and analyzed by flow cytometry. (C) GPS-NRF2 cells were treated with DMSO or the oxidizing agent TBHQ for 4 hr. (D) 293T cells expressing GPS-NRF2 were treated with DN-Cul3 for increasing amounts of time and were analyzed by flow cytometry. (E) Shown is a subset of validated candidate substrates that were tested in 293T cells using flow cytometry. (F) The overlap between CRL3 and MLN4924 GPS screens. (G) Functional category and protein family enrichment analysis for protein overlap between the CRL3 and MLN4924 GPS screens. See also Tables S5 and S7.

466 Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc.

A Domain F-box ZF SH3 Protein kinase WD40 LRR DEAD

Descrip on F-box domain Zinc finger Variant SH3 domain Protein kinase domain WD domain, G-beta repeat Leucine Rich Repeat DEAD/DEAH box helicase

B

SCF GPS Screen

ABI2 ACBD5 ADSS AFAP AP1GBP1 APBB1IP ARMCX6 BNIP3 BTRC CASQ2 CBX2 CDC25A CDC34 CDK2AP1 CDK4 CDK6

Cluster Proteolysis Protein ubiqui na on Cytoskeleton Cell projec on Cell type Cell Junc on Anterior/posterior pa ern forma on

P-value << 1E-20 7.08E-03 2.78E-02 2.78E-02 8.48E-02 8.48E-02 2.02E-01

CDKN1A CDKN1B CTNNBIP1 DNALI1 EPN3 FBXL14 FBXL2 FBXL5 FBXO28 FBXO3 FBXO34 FBXO39 FBXO5 FBXO7 FBXW11 FBXW7

FLJ25421 FLJ39155 HDAC6 JUP KIAA1720 LANCL2 LOC164045 MAP2K2 MAPRE1 MGC87631 NFE2L1 PDCD4 PPP4R2 RBM19 SEPHS2 SH3BP4

SKP1A SLBP SNPH SPIRE2 TDRD3 TMEM183A TRIM9 TRIP4 TULP1 UBE2R2 UPF2 ZCCHC7 ZNF238

Enrichment Score 13.6 5.03 3.62 2.97 2.35 1.21

P-value 3.40E-13 1.60E-04 4.80E-04 2.60E-04 1.40E-05 3.50E-03

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Figure 5. GPS Screen to Identify SCF Substrates (A) Functional category and protein family enrichment analysis for the proteins that validated in the CRL4 GPS screen. (B) The overlap between CRL4 and MLN4924 GPS screens. (C) A subnetwork demonstrating the high degree of betweenness for proteins regulated by the SCF. SCF candidate substrates are shown in cyan, and circle size corresponds to the degree of connectivity within the network. See also Figure S6 and Tables S6 and S7.

2003; Ribbeck et al., 2006, 2007). To identify the specific ligase controlling NUSAP1, we treated cells with MLN4924 or DN-Cul. MLN4924 confirmed the CRL dependency (Figure 6A), and only DN-Cul1 produced a significant increase in NUSAP1 levels (Figure 6B). To identify the F box protein for NUSAP1, we performed

an siRNA screen of all 69 known F box proteins. U2OS cells were transfected with siRNA pools targeting each of the different F box proteins, and 72 hr posttransfection, cells were harvested and immunoblotted for NUSAP1. We found that depletion of cyclin F, the founding member of the F box family, increased Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc. 467

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468 Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc.

Vinculin

the levels of NUSAP1 (Figure 6C). To date, CEP110 is the only known SCFCyclin F substrate (D’Angiolella et al., 2010). To confirm specificity, we tested four independent cyclin F siRNAs and found cyclin F depletion inversely correlated with NUSAP1 levels (Figure S5A). To test whether this regulation was posttranslational, cyclin F was depleted from 293T cells expressing GPSNUSAP1 or GPS-MDH1 (negative control). Cyclin F depletion caused an increase in the EGFP/DsRed ratio of GPS-NUSAP1 cells (Figure 6D), reflecting an increase in EGFP-NUSAP1 stability relative to the siRNA control (siFF). Because SCFCyclin F ligase activity is cell cycle regulated, we examined NUSAP1 proteins levels throughout the cell cycle. NUSAP1 accumulated during S and G2 phase following release from synchronization at the G1/S boundary and was destroyed at the end of mitosis. Its destruction in late mitosis and G1 is similar to that of PAF15 and cyclin B (Figure 6E). This was expected because NUSAP1 has been reported to be an APC/C substrate (Song and Rape, 2010), similar to PAF15 and cyclin B (Emanuele et al., 2011; King et al., 1995). Following release from a double thymidine block, cyclin B and PAF15 levels were relatively high, increasing
20% between the time of release and their maximal level achieved in mitosis. NUSAP1 levels were low throughout S and abruptly increased in G2 (Figure 6E, graph). We asked whether cyclin F controlled NUSAP1 during S and G2. U2OS and HeLa cells treated with cyclin F siRNA were synchronized at the G1/S boundary and released into the cell cycle. Cyclin F depletion increased the levels of NUSAP1 during S and G2 phase following only 24 hr of siRNA depletion (Figures 6F and S5B). Importantly, cyclin F depletion does not affect cell-cycle timing following release (Figures 6C and S5B). To confirm that cyclin F and NUSAP1 interact, Flag-cyclin F was immunoprecipitated from HeLa cells. Immunoblotting of the precipitates showed that full-length cyclin F, but not a truncation lacking the cyclin box (cyclin F 1–270), precipitated endogenous NUSAP1 (Figure 6G). We conclude that SCFCyclin F targets NUSAP1 for degradation during S and G2 phases of the cell cycle.

NUSAP1 Maintains Resistance to Antitubulin Therapeutics Nusap1 resides on chromosome 15q15.1, a region frequently deleted in a wide variety of cancers. The role of NUSAP1 in regulating microtubules prompted us to examine the potential sensitivity of NUSAP1-depleted cells to antitubulin chemotherapeutics. We found that depletion of NUSAP1 with three independent siRNA made both U2OS and HCT116 cells highly sensitive to treatment with the antitubulin cancer therapeutic taxol relative to siFF-treated controls (Figures 7E and S5G) without perturbing the cell-cycle distribution (Figures S5E and S5F). This phenotype was rescued by reintroduction of a NUSAP1 ORF lacking the 30 UTR following depletion with the siRNA targeting the 30 UTR (Figure S5H). We also observed a similar degree of sensitivity to nocodazole, which disrupts the microtubule cytoskeleton through an alternative mechanism, in U2OS and HCT116 cells (Figures 7E and S5G). This suggests that the presence of NUSAP1 makes cells more resistant to the toxic effects of antitubulin chemotherapeutics and might predict taxol sensitivity in tumors deleted for NUSAP1.

NUSAP1 Is Degraded in Response to UV Irradiation NUSAP1 is phosphorylated on S124 by the ATM/ATR kinases following DNA damage (Matsuoka et al., 2007; Xie et al.,

CRLs Have Nonoverlapping Functions Functional categorization and domain analysis of proteins identified by QUAINT and GPS are shown in Tables S1 and S2 and

2011). Based on this information, we examined NUSAP1 protein levels following DNA damage with ultraviolet light (UV), the UV mimetic 4NQO, and ionizing radiation (IR). We found that UV and 4NQO, but not IR, caused NUSAP1 degradation in U2OS, HeLa, and 293T cells (Figures 7A, S5C, and S5D). Degradation was observed at 1 hr following UV treatment and with as little as 10 J/m2 UV. Importantly, cells treated with MG132 or MLN4924 could not effectively degrade NUSAP1, demonstrating both proteasome and CRL dependence (Figure 7B). To map the ligase responsible for NUSAP1 degradation, we employed a panel of dominant negatives targeting each of the cullins. We found that SCF inhibition prevented NUSAP1 degradation (Figure 7C). However, depletion of cyclin F had no effect on the UV-induced NUSAP1 degradation, indicating that it is likely to be a substrate of two distinct SCF ligases (Figure 7D).

Figure 6. NUSAP1 Is an SCFCyclin F Substrate (A) 293T cells were treated with MLN4924 for 2, 4, and 8 hr and immunoblotted with antibodies to endogenous NUSAP1. (B) U2OS cells expressing the indicated DN-Cul were immunoblotted for NUSAP1. (C) U2OS cells were transfected with siRNA targeting firefly luciferase (siFF) or the F box proteins Fbxw7 and cyclin F, incubated for 48 hr, and immunoblotted for NUSAP1, cyclin B, and Vinculin (loading control). This experiment was performed with and without UV treatment, and the full experiment is shown in Figure 7D. Here, we show a lighter exposure of the untreated portion of the NUSAP1 immunoblot relative to 7D to better illustrate the increase in NUSAP1 following siRNA depletion of cyclin F. (D) 293T cells expressing GPS-NUSAP1 were treated with siFF or siCyclin F for 48 hr and analyzed by flow cytometry. (E) HeLa cells were synchronized using a double thymidine block and released. Lysates were collected at the indicated times and immunoblotted. NUSAP1, pH3S10, PAF15, and the first Vinculin blot (top four panels) were probed from the same immunoblot membrane. Cyclin B and the second Vinculin blot (bottom two panels) were probed on a second membrane. A semiquantitative analysis of NUSAP1, cyclin B, and PAF15 protein levels (relative to loading controls), derived from the western blot shown, is graphed. (The extracts analyzed here are identical to those utilized in Figure 1C of Emanuele et al., 2011.) (F) U2OS cells were synchronized using a double thymidine block and release. After the first thymidine block, cells were transfected with siRNAs to cyclin F or siFF. Following release, cells were analyzed by immunoblot with the indicated antibodies (asterisk notes a cross-reacting band in the cyclin F immunoblot). A semiquantitative analysis of NUSAP1 protein levels (relative to loading controls), derived from the western blot shown, is graphed. (G) Flag-tagged cyclin F or cyclin F (1–270) was transfected into HeLa cells for 24 hr. Cells were treated with 5 mM MG132 for 2 hr, harvested, and precipitated with anti-Flag agarose. The precipitates were immunoblotted for NUSAP1. See also Figure S5.

Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc. 469

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confirm the role of CRLs in a wide swath of cellular processes. We performed a similar analysis on the validated substrates from each individual GPS screen. CRL4-regulated proteins were enriched for involvement in nuclear, Golgi, and endoplasmic reticulum function, as well as DNA metabolism, replication, and repair 470 Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc.

(Figure 3D). In addition, domain analysis revealed enrichment for protein kinases, zinc fingers, and WD40 repeat proteins, which serve as substrate adaptors for CRL4 (Figure 3D). The enrichment for replication and repair was expected from the known role of CRL4; however, a role in most other categories has not been

previously established for CRL4. Domain and functional category analyses for putative CRL3 and SCF substrates are shown in Figures 4F and 5A, respectively. Domain analyses for putative CRL3 and SCF substrates revealed a significant enrichment for their respective adaptors, Kelch-BTB and F box proteins. In addition, SCF substrates showed the expected enrichment for proteolysis but an unexpected enrichment for cytoskeleton and cell projection, suggesting a role for the SCF in cell migration. Most importantly, the functional analysis for each ligase is distinct. This suggests that each CRL evolved in a specialized fashion to regulate specific aspects of cellular physiology. CRL Substrates Are Highly Enriched for ‘‘Betweenness’’ We performed an interaction analysis for proteins that validated for regulation by CRLs in our screens to determine the degree to which these proteins participated in protein interaction networks. The network that emerged from this analysis was analyzed for a property called ‘‘betweenness,’’ which is a statistical measure of a protein’s centrality within an interaction network. A higher degree of betweenness indicates a greater degree of interconnectivity within a network. The CRL candidate list of 472 proteins, which scored in at least two overlapping screens, was mapped onto the most current BioGRID human protein-protein interaction network (Table S7). This analysis demonstrated that CRL-regulated proteins show a high degree of betweenness (p = 3.96 3 1015; Figure S6), indicating that they are highly connected within protein interaction networks. In addition, proteins scoring with greater than a 2-fold change by QUAINT (Table S3) and those that overlapped between the MLN4924 GPS and QUAINT screens (Figure 2D) also showed a high degree of betweenness (p = 1.1 3 1022 and 2.08 3 109, respectively; Table S7). Graphs demonstrating the increased protein interactions for CRL candidate substrates are shown in Figure S6D. Based on these results, we analyzed the individual validation lists for the SCF, CRL4, and MLN4924 GPS screens. As expected, the validated substrate lists all showed a statistically significant degree of betweenness (p = 3.7 3 103, p = 3.2 3 103, and p = 1.72 3 106, respectively; data sets in Table S7). Network diagrams showing the betweenness centrality of putative substrates from these screens are depicted in Figure S6. A subnetwork for SCF is also shown in Figure 5C. Thus, the proteins regulated by CRL ligases represent central hubs within networks and pathways. By regulating these critical junctures, we hypothesize that CRLs could have a maximal impact on a particular pathway. DISCUSSION Regulated ubiquitin-mediated proteolysis through E3 ligases is a critical aspect of cellular homeostasis. Functionally, E3 ligases are equivalent to micro-RNAs in the hierarchy of regulated gene expression. Just as micro-RNAs are sequence-specific adaptors that target RNA molecules for destruction to filter the transcriptome, E3 ligases are sequence-specific adaptors that target proteins for destruction to filter the proteome. Their ability to reshape the proteome in response to stimuli is of vital importance to both development and physiological responses to

stimuli in eukaryotes. Thus, it is critically important to identify the substrates of ubiquitin ligases at a systems level. Comparison of GPS and QUAINT Technologies Here, we have applied two emerging techniques, GPS and QUAINT, to identify ubiquitylation substrates of the CRL family of E3s. Of the known SCF substrates (Skaar et al., 2009) in the ORFeome library,
22% were identified by GPS. This is an underestimate of the potential of GPS for substrate identification, as known substrates were identified from a variety of cell types and we performed GPS analysis in only one. Because cell lines vary with respect to the constellation of substrate adaptors and activity of different signaling pathways, complete overlap is not expected. Of the known SCF substrates, QUAINT identified 14%, which is also an underestimate. QUAINT and GPS have different strengths. GPS measures protein abundance and directly interrogates changes in protein stability independent of their endogenous abundance or expression in a specific cell type. Despite these strengths, GPS suffers from a reliance on an N-terminal GFP fusion, which can affect protein localization and degron activity in certain cases. We are in the process of reconstructing libraries with alternative N- and C-terminal tags to circumvent some of these issues. In addition, expression levels can affect our ability to detect substrates, evidenced by the fact that some of the validated SCF substrates from first-generation screens did not rescore in the current screen. GPS currently relies on ORFeome collections that are not sequence verified and contain truncated and mutant proteins. Advances in ORFeome collections and the assembly of a nonredundant, sequenced verified ORFeome collection will enhance the GPS system. The strength of QUAINT is its quantitative nature and ability to recognize endogenous proteins. It is also able to identify ubiquitylation events, such as monoubiquitylation, that do not result in protein degradation. Moreover, it identifies ubiquitylation sites that could provide mechanistic insights and inform substrate mutational analysis. However, QUAINT cannot distinguish whether the ubiquitin modification reflects a change in the entire protein population of a substrate or a small fraction, the biological significance of which is less certain. Furthermore, QUAINT cannot distinguish between ubiquitin, ISG15, and Nedd8 modification because all three modifiers leave a GG-lysine after trypsin proteolysis. In addition, it is possible that ubiquitylated proteins that change transcriptionally in response to stimuli, such as MLN4924, may appear to have altered ubiquitylation that does not reflect a change in ligase activity. QUAINT cannot distinguish between monoubiquitylation and polyubiquitylation, only the latter of which could affect protein stability. Finally, QUAINT is biased toward more abundant substrates. Both GPS and QUAINT strategies identified known CRL substrates missed by the other. For example, QUAINT identified b-catenin and CDT1, which are missed by GPS—b-catenin because it is not encoded by the current ORFeome collection and CDT1 because conjugation with GFP interferes with its degron. Conversely, the GPS-MLN4924 screen identified a number of well-characterized substrates missed by our proteomic efforts, including but not limited to NRF2, DVL1, PDCD4, CDKN1A, CDKN1B, FBXO5/EMI1, and MCL1. With ongoing Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc. 471

ORFeome development and C-terminal tagging strategies, we expect the GPS system to continue to improve. Importantly, the combination of these emerging techniques has given us a very deep snapshot of the regulated protein stability and modification landscapes that will only improve in the future. NUSAP1 Is an SCF Substrate As a proof of principle, using layered genetic screens, we have identified the precise ligase for one CRL-regulated protein. We discovered that NUSAP1 is a substrate of SCFCyclin F. NUSAP1 and cyclin F interact with one another, and cyclin F depletion specifically affects the stability of NUSAP1. Cyclin F has only one other known substrate, CEP110 (D’Angiolella et al., 2010). CEP110 localizes to centrosomes and regulates their duplication cycle (Ou et al., 2002). CEP110, like NUSAP1, is involved in regulating the microtubule cytoskeleton. Because CEP110 and NUSAP1 depletion cause chromosome segregation defects, this suggests that the upstream regulation of these two factors by cyclin F is essential for maintaining chromosome stability. NUSAP1 is also destabilized upon treatments that activate the ATR/ATRIP pathway. Importantly, this destabilization is Cul1 dependent but cyclin F independent. Because NUSAP1 is also an APC/C substrate, it is likely to be regulated by multiple cullin-based E3 ubiquitin ligases: APC/C, SCFCyclin F, and a third SCF-based ligase. Nusap1 is located in a focal deletion region for many tumor subtypes, and as we found that cells depleted for NUSAP1 were sensitive to taxol, tumors with reduced NUSAP1 might be responsive to taxol. Further, if the degradation of NUSAP1 in response to chemotherapeutic agents that crosslink DNA is a general finding, then tumor cells containing NUSAP1 but lacking a functional G2/M checkpoint might be especially sensitive to a combination therapy consisting of the proper class of DNA damaging agent and taxol. These findings may have important implications for cancer therapies based on neddylation inhibition. Functional Divergence and Betweenness Functional category enrichment analysis for individual CRLs showed that each ligase evolved in a specialized manner to control distinct physiological pathways. Moreover, we find the CRL substrate list is strongly enriched for a property known as betweenness, being highly enriched as hubs within interaction maps. Thus, they are much more highly connected than the average protein in the database (akin to the highly connected individual in a social network). Highly connected nodes are positioned to control the flow of information across a network, and their removal will have the highest impact across the network due to their inherent connectivity. In yeast, highly connected proteins are three times more likely to be essential than their non-node counterparts (Jeong et al., 2001; Jonsson and Bates, 2006). We have reanalyzed the yeast data with the more complete interaction network in BIOGRID and have found that our set of CRL substrates is as highly enriched for betweenness as the set of essential genes in S. cerevisiae (Figure S6D). The biological interpretation of our observation that CRL substrates represent nodes is that cells have evolved mechanisms to regulate the abundance of the most essential components of networks. Because signal transduction pathways control regulated ubiquitylation by CRLs, these pathways turn on or off key 472 Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc.

nodes to maximally impact cellular physiology. Together, these observations indicate that the CRL substrates that we have identified represent a collection of key regulatory proteins. We predict that these proteins are highly enriched for indicators of the physiological state of the cell. This study, together with anticipated future studies, will allow us to gain a systems-level understanding of the dynamics of protein stability in the proteome. EXPERIMENTAL PROCEDURES Tissue Culture, Reagents, and Procedures A complete description of experimental procedures is available in the Extended Experimental Procedures. Cells were transfected with plasmids using TransIT transfection reagent (Mirus). Retroviruses and lentiviruses were packaged in 293T cells using standard techniques. Cells were transfected with siRNA oligonucleotides using RNAiMAX (Invitrogen). Flag IPs were performed on lysates from a single 10 cm plate of HeLa cells 24 hr after transfection and 3 hr after treatment with 5 mM MG132. Cells were lysed in NETN buffer-containing protease and phosphatase inhibitors. Clarified, combined nuclear and cytoplasmic lysates were precipitated with Flag-M2 agarose beads for 3 hr at 4 C. Beads with immune complexes were washed four times and boiled in SDS-PAGE sample buffer. Cell viability was measured using Cell Titer Glo Reagent (Promega) according manufacturer’s protocols. For viability assays following NUSAP1 depletion, cells were treated with siRNA for 16–18 hr before replating in 24-well plates. Four hours after replating, media was supplemented with taxol or nocodazole and viability was assayed 72 hr later. All experiments were performed in triplicate, and data reported are a mean. GPS Screening and Scoring GPS screens were performed essentially as described (Yen and Elledge, 2008). For the MLN4924 screen, cells were treated for 4 hr with 1 mM drug. For the SCF, CRL3, and CRL4 screens, cells were treated with lentivirusexpressing DN-Cul1, DN-Cul3, and DN-Cul4A and DN-Cul4B. Control cells were treated with an empty vector-expressing lentivirus or DMSO. A viral titer of greater than 10 was used for all of the CRL screens. Microarray probe data was filtered and normalized, and for each probe, a DPSI value was calculated and a graph was drawn comparing the probe distribution across bins for treated and untreated samples (example in Figure 1Av). Graphs were rank ordered based on DPSI and were visually inspected one at a time to assess the significance of the probe shift for all graphs with a DPSI greater than 0.25. QUAINT SILAC Peptide IP HeLa cells were grown in DMEM containing heavy or light arginine and lysine, as described (Matsuoka et al., 2007). Cells were treated with 5 mM MG132 (Boston Biochem) and/or 10 mM MLN4924. Cells were harvested and lysed in denaturing buffer. Heavy and light lysates were mixed in a 1:1 ratio, and proteins were digested with trypsin, followed by desalting on a Sep-Pak C18 column (Waters). Peptides were dissolved in IP buffer (50 mM MOPS buffer [pH 7.2], 10 mM sodium phosphate, and 50 mM NaCl) and incubated with PTMScan Ubiquitin Remnant Antibody Beads (Cell Signaling Technology, Inc.). Beads were washed with IP buffer followed by water, and enriched ubiquitylated peptides were eluted with 0.15% TFA, followed by LC-MS/MS analysis using an LTQ Orbitrap Velos. Bioinformatic Analysis Protein interactions in Figure 2C were identified using BioGRID, except for SOD1, which has a genetic interaction with Cul1. We used Pfam 25.0 (http:// pfam.sanger.ac.uk/) to analyze protein domains. We used DAVID 6.7 to test the gene functions of all lists and used genes in the ORFeome library as a background for gene annotation enrichment analysis (Huang et al., 2009). To determine protein-protein interactions, we used online database BioGRID 3.1.77 (Stark et al., 2011). The interactions are displayed as

a graphical network using the open-source software Cytoscape 2.8.1 (Smoot et al., 2011). The betweenness centrality of each gene is a number between 0 and 1. The betweenness Gene A is computed as follows: if all connections pass through the Gene A, betweenness value of Gene A is 1; if Gene A is a terminal node in the network, the value is 0. We used the sign test to see whether the genes in a list have a higher betweenness value or not. Each network’s median is calculated in a sorted list of betweenness values, and the gene list will be separated into two parts: (1) less than or equal to median and (2) greater than median sets. Binomial distribution is used with a sign test to determine the p value.

homeostasis and mitotic fidelity through CP110 degradation. Nature 466, 138–142.

SUPPLEMENTAL INFORMATION

Emanuele, M.J., Ciccia, A., Elia, A.E.H., and Elledge, S.J. (2011). Proliferating cell nuclear antigen (PCNA)-associated KIAA0101/PAF15 protein is a cell cycle-regulated anaphase-promoting complex/cyclosome substrate. Proc. Natl. Acad. Sci. USA 108, 9845–9850.

Supplemental Information includes Extended Experimental Procedures, six figures, and seven tables and can be found with this article online at doi:10.1016/j.cell.2011.09.019. ACKNOWLEDGMENTS We thank the members of the Elledge lab for helpful discussions. We thank W. Harper for the Cul3 and Cul4 dominant-negative clones and F box siRNA; S. Gygi for access to and assistance with MS/MS analysis software; the IMB Bioinformatics Services Core of Academia Sinica for technical support; and Michele Pagano for cyclin F plasmids. We thank Marc Vidal and David Hill for the ORFeome library. M.J.E. is the Philip O’Bryan Montgomery Jr., MD fellow of the Damon Runyon Cancer Research Foundation (DRG-1996-08). A.E.H.E. is supported by fellowships from The Jane Coffin Childs Foundation and The American Society for Radiation Oncology. C.R.T. is funded by an EMBO and HFSP fellowship. Q.X. is supported by a fellowship from the American Cancer Society. H.-C.S.Y. was supported by an NSC grant (NSC100-2628-B-001-014-MY3). This work was supported by an NIH grant to S.J.E. S.J.E. is an Investigator with the Howard Hughes Medical Institute. Received: June 5, 2011 Revised: August 29, 2011 Accepted: September 16, 2011 Published online: September 29, 2011 REFERENCES Abbas, T., and Dutta, A. (2011). CRL4Cdt2: master coordinator of cell cycle progression and genome stability. Cell Cycle 10, 241–249. Bai, C., Sen, P., Hofmann, K., Ma, L., Goebl, M., Harper, J.W., and Elledge, S.J. (1996). SKP1 connects cell cycle regulators to the ubiquitin proteolysis machinery through a novel motif, the F-box. Cell 86, 263–274. Benanti, J.A., Cheung, S.K., Brady, M.C., and Toczyski, D.P. (2007). A proteomic screen reveals SCFGrr1 targets that regulate the glycolytic-gluconeogenic switch. Nat. Cell Biol. 9, 1184–1191. Bennett, E.J., Rush, J., Gygi, S.P., and Harper, J.W. (2010). Dynamics of cullinRING ubiquitin ligase network revealed by systematic quantitative proteomics. Cell 143, 951–965. Brownell, J.E., Sintchak, M.D., Gavin, J.M., Liao, H., Bruzzese, F.J., Bump, N.J., Soucy, T.A., Milhollen, M.A., Yang, X., Burkhardt, A.L., et al. (2010). Substrate-assisted inhibition of ubiquitin-like protein-activating enzymes: the NEDD8 E1 inhibitor MLN4924 forms a NEDD8-AMP mimetic in situ. Mol. Cell 37, 102–111.

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Supplemental Information EXTENDED EXPERIMENTAL PROCEDURES Tissue Culture, Reagents, and General Procedures HeLa, U2OS, and 293T cells were maintained in Dulbeccos Minimum Essential Media supplemented with Penicillin/Streptomycin and 10% fetal bovine serum (Invitrogen). 293T and HeLa cells were transfected using TransIT transfection reagent (Mirus). Retroviruses and lentiviruses were packaged in 293T cells transfected with the plasmid of interest and plasmids expressing VSVg, Gag-pol, TAT and Rev (All four for lentivirus, the first two for retrovirus) (Luo et al., 2009). Viral infections were supplemented with hexadimethrine bromide at 8 mg/ml. Transfection of siRNA oligonucleotides was performed with RNAiMAX (Invitrogen) lipid transfection reagent according to the manufacturer’s protocols. siRNA were purchased from Dharmacon as a ‘‘Set of 4 siGenome Upgrades.’’ A complete list of the siRNA sequences and immunological reagents used in this study are listed below. Flow cytometry was performed on a BD-LSRII Flow Cytometer (Becton Dickinson). Data was collected using BD FACS Diva software (Becton Dickinson) and analysis was performed on FloJo Software. Semiquantitative immunoblot analysis was performed on images collected with an air cooled CCD camera using ImageJ (NIH). Flag immunoprecipitations were performed on from 10 cm plates of HeLa cells 24 hr after transfection and 3 hr after treatment with 5 mM MG132. Cells were lysed for 15 min. in ice-cold LB (50 mM Tris, 100 mM NaCl, 1% NP-40, pH 7.5 supplemented with EDTA-free protease inhibitor tablet and Phos-Stop phosphatase inhibitor tablet: Roche) at 4
C. Lysates were clarified by centrifugation (14,000 rpm, 15 min., 4
C) and nuclear pellets were resuspended in LB, sonicated briefly and re-clarified. The combined whole cell extracts were immunoprecipitated with Flag-M2 agarose beads (Sigma), for 3 hr at 4
C. Beads with immune complexes were washed 4 times with LB and boiled in SDS-PAGE sample buffer. Cell viability was measured using Cell Titer Glo Reagent (Promega) according manufacturers protocols. Luminescence was assayed in black 96 well plates using a Victor X5 2030 multilabel plate reader (Perkin Elmer). For viability assays following NUSAP1 depletion, cells were treated with siRNA for 16-18 hr, before re-plating in 24 well plates. Four hours after replating, media was supplemented with taxol or nocodazole and viability was assayed 72 hr later (approximately 96 hr post siRNA transfection and 72 hr posttreatment with drug). All experiments were preformed in triplicate and data reported are a mean. Validation was performed by individually picking ORFeome clones and cloning them using gateway recombination into the GPS vector. This material was used to pack virus and generate cell lines expressing individual GPS-ORFs. Cells were treated as described for their individual screens. For immunoblot validation of endogenous proteins after MLN4924 treatment, cells were treated for 20 hr with the indicated drug concentration. GPS Screening GPS screens were performed essentially as described (Yen et al., 2008). For the MLN4924 screen cells were treated for 4 hr with 1 mM drug. For the SCF screen, cells were treated as described in (Yen and Elledge, 2008). For the DN-Cul3 screen, cells were treated with lentivirus-expressing DN-Cul3 for 18 hr. For the CRL4 screen, cells were treated with a combination of DN-Cul4A and DN-Cul4B together for 20 hr. Control cells were treated with empty vector expressing lentiviruses or DMSO, where appropriate. A viral titer of greater than 10 was used for all of the CRL screens. The general workflow of the GPS screen is as follows. 293T cell libraries were sorted into either 7 bins (for the DN-Cul3 and DN-Cul4 screen) or 8 bins (DN-Cul1 and MLN4924 screen), based on their EGFP/DsRed ratio. Approximately equal numbers of cells were sorted into eah bin, with the outer most bins receiving slightly fewer cells than those in the middle. We sorted a minimum of 1.5 million cells per bin. Sorting was carried at the Dana Farber Hematologic Neoplasia Flow Cytometry Facility with the assistance of John Daley and Suzan Lazo-Kallanian. Following sorting, cells were pelleted and frozen at 80
C. Pellets were subsequently thawed and lysed in 10 mM Tris pH 8.0, 10 mM EDTA, 0.5% SDS, 0.2 mg/ml Proteinase K at 55
C overnight with agitation. Genomic DNA (gDNA) was extracted using Phaselock tubes with Phenol:Chloroform and then Chloroform. RNase A was added to a final concentration of 25 mg/ml and following incubation overnight at 37
C, gDNA was extracted using Phaselock tubes, as above. DNA was ethanol precipitated (with glycogen), recovered by centrifugation, and washed three times with 75% ethanol. The dried, resuspended pellet was PCR amplified with Takara hot start polymerase (Takara- RR006B) using common PCR primers targeting the lentiviral backbone outside of the ORF (Primer 1- 50 -GGGATCACTCTCGGCATGGACGA: 30 - TAATACGACTCACTATAGGGAGAGG). Alternatively, PCR amplification was performed as described (Yen and Elledge, 2008). PCR amplified DNA was transcribed with T7 using Ambion MEGAscript and purified with Ambioin MEGAclear kits. RNA was labeled with a ULS labeling kit (Kreatech: unsorted samples-Cy5, sorted samplesCy3). Hybridization was performed on custom designed Agilent DNA microarrays printed 8 3 65K arrays according to manufacturer’s protocols. The probe sequences used are listed in the supplemental tables. Microarrays were read using an Agilent scanner. The CRL3 and CRL4 screens were performed using the original GPS vector and version 3.1 of the ORFeome collection. MLN4924 and SCF screens were done using the second generation lentiviral vector and version 5.1 of the ORFeome. PSI was calculated as described (Yen et al., 2008). Percent shift was calculated by summing absolute values of the subtracted relative abundance from probes across all bins. Statistical analysis and graphing was performed using the statistical analysis package R (v2.13). GPS Data Analysis Probes were initially filtered by removing probes with a low signal, generally those below 5-fold of the background signal and normalized probe signals to adjust for negative values at some probe spots. Microarrays from different bins were normalized within each screen based on their spike-in control signals. After filtering low signal probes and normalizing, we compared the EGFP signals

Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc. S1

for all probes within a single bin between treated and untreated conditions for every bin to confirm probe and hybridization reliability. We next calculated the PSI for each probe and compared those between treated and untreated conditions. These numbers are reported in the text. The average PSI of all probes, for each condition, was determined and normalization was performed as necessary. A DPSI was calculated by subtracting the two PSI numbers (treated-control). We rank ordered probes based on DPSI and used the statistical programming package R to graph the distribution for every probe across bins, comparing treated and untreated conditions on each graph, for every probe that past our low signal cut-off. Graphs were ranked based on the DPSI and were visually inspected one at a time to determine if there was a significant probe shift. We often found that a high DPSI could be attributed to erroneous noise in outside bins, making visual inspection essential. We examined additional graphs for high priority proteins, such as those that scored in other GPS screens as well. Genes that showed a significant shift from multiple probes (when available) were designated as putative hits, or high priority hits. A binomial distribution was used to calculate the p-value for the enrichment of overlapping candidates from QUAINT and GPS MLN4924 screens, assuming that 1000 proteins would have a shifted probe distribution in GPS, an extreme over-estimation, suggesting the actual p-value would actually be much more significant. Design of DNA Microarray Probes for the Human ORFeome Library DNA sequences of the 15483 ORFs in the library were blasted against each other. For each ORF, high-scoring segment pairs (HSP) from other ORFs were marked on its length. The least cross reacting sequence regions were determined on an ORF by counting how many times a given nucleotide was a part of a HSPs. These regions were used to select appropriate DNA probes of either 25 or 60 nucleotide length to minimize cross-reactivity. A nucleotide with the lowest hit score in a region was designated as the center of the probe oligo. A stack of 10 more oligos that were one nucleotide further extended toward either 50 or 30 of the center nucleotide were collected. We analyzed these 11 oligos to determine the oligo with an optimal GC percentage between 4060%, appropriate melting temperature, and no C-stack (longer than 4 consecutive Cs) in the first 10 nucleotides. Probe selection was slightly biased for the 30 of ORF sequences. We picked up to 10 25-mer probes and 5 60-mer probes for each ORF. Bioinformatic Analysis Protein interactions in Figure 2C were identified using BioGRID, except for SOD1, which has a genetic interaction with Cul1. We used Pfam 25.0 (http://pfam.sanger.ac.uk/) to analyze the protein domains in Cul1 and other gene lists. The gene sequences come from ORFeome library, we translated the sequences to proteins and search the domains in Pfam-A entries which are high quality, manually curated families (Finn et al., 2010). We used DAVID 6.7 (http://david.abcc.ncifcrf.gov/home.jsp) to test the gene functions of all lists and used genes in ORFeome library as background to make gene-annotation enrichment analysis (Huang et al., 2009). To determine protein-protein interactions, we used online database BioGRID 3.1.77 (Stark et al., 2011), which houses 39,931 physical interactions among 10,271 human proteins that have been curated from 12,526 publications. The interactions are displayed as a graphical network using the open source software Cytoscape 2.8.1 (Smoot et al., 2011). The betweenness centrality of each gene is a number between 0 and 1, the betweenness Gene A is computed as follows: B(gene A) = number of paths from gene X to gene Y passing through Gene A / number of paths from gene X to gene Y If all connections pass through the Gene A, betweenness value of Gene A is 1; if Gene A is a terminal node in the network, the value is 0. We used the sign-test to see if the genes in a list have a higher betweenness value or not. Each network’s median is calculated in a sorted list of betweenness values and the gene list will be separated into two parts: (a) less than or equal to median and (b) greater than median sets. Binomial distribution is used with a sign-test to determine the p-value. All lists have higher betweenness values with significant p-values. Cell Treatments for QUAINT HeLa cells were cultured in lysine and arginine-free DMEM containing 10% dialyzed FBS, 2 mM L-glutamine, penicillin, and streptomycin. Light media contained 50 mg/ml L-lysine and 40 mg/ml L-arginine, while heavy contained 50 mg/ml L-lysine-U-13C6-15N2 and 40 mg/ml L-arginine-U-13C6-15N4 (Cambridge Isotope Labs). For the experiments described in Figure 2, light cells were treated with 5 mM MG132 (Boston Biochem), and heavy cells with both 5 mM MG132 and 10 mM MLN4924, for 4 hr. An alternative washout experiment was also performed (Approach B, Figure 2SD), in which both heavy and light HeLa cells were initially treated with 1 mM MLN4924. After 4 hr, they were washed three times with pre-warmed PBS and once with media. Light cells were then replaced with media containing only 5 mM MG132, and heavy cells were maintained in 5 mM MG132 and 1 mM MLN4924 (Figure S2D) for 4 hr. To confirm that MLN4924 could be washed out, we pre-treated cells with 1 mM and 500 nM MLN4924 for 4 hr and followed the abundance of known substrates after washout. NRF2 and CDT1 protein levels, and the degree of CUL4 neddylation, all returned to pre-treatment levels following drug washout (Figure S2E). The data from this washout experiment is provided in Table S3. QUAINT SILAC Peptide IP Cells were harvested, washed with PBS, and lysed in denaturing buffer containing 8M urea, 20 mM HEPES pH 7.5, 1 mM b-glycerophosphate, 2.5 mM sodium pyrophosphate, and 1 mM sodium vanadate with sonication. Equal amounts of heavy and light lysates were combined in a 1:1 ratio, and proteins (35 mg) were digested with trypsin overnight at room temperature. The solution was acidified with trifluoroacetic acid (TFA) and clarified by centrifugation. The supernatant was desalted on a Sep-Pak C18 column (Waters), and lyophilized peptides were dissolved in IP buffer (50 mM MOPS pH 7.2, 10 mM sodium phosphate, 50 mM NaCl). Ubiquitylated peptides were enriched by overnight incubation with protein A agarose beads conjugated to a monocolonal antibody that was generated against the sequence CXXXXXXK(GG)XXXXXX (Cell Signaling Technology, Inc), where X is all amino acids except cysteine and tryptophan, and K(GG) is a digylcine moiety attached to the epsilon amino group of lysine. Beads were washed with

S2 Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc.

IP buffer followed by water, and enriched diGly-modified peptides were eluted with 0.15% TFA. Eluates were desalted by Stage tip chromatography and lyophilized before analysis by LC-MS/MS. Mass Spectrometry Lyophilized and di-Gly enriched peptides were dissolved in 5% acetonitrile / 5% formic acid. Using a Famos autosampler (LC Packings), they were loaded onto a reversed phase microcapillary column (100 mm I.D.) packed first with 5 mm of Magic C4 resin (5 mm, 100 A˚, Michrom Bioresources) followed by 20 cm of Maccel C18AQ resin (3 mm, 200 A˚, The Nest Group, Inc.). Peptides were separated using a gradient of 5%–27% acetonitrile in 0.125% formic acid over 180 min and detected in a hybrid linear ion trap-orbitrap mass spectrometer (LTQ-Orbitrap Velos, ThermoFisher). Peptide Identification and Quantification RAW files were converted into mzXML format and processed using a suite of software tools developed in the laboratory of Steven Gygi. Precursors selected for MS/MS fragmentation were corrected for errors in monoisotopic peak assignment, and precursor ion mass measurements were refined (Huttlin et al., 2010). All MS/MS spectra were then searched using version 28 of the Sequest algorithm against a database containing all human proteins (IPI version 3.6) in both forward and reversed orientations. Since lysine residues may contain two variable modifications (isotopic label and diGly remnant), each MS experiment underwent two searches, the first of which designated the lysine mass as its natural abundance value, while the second increased its mass by 8.014199 to account for heavy labeling. Variable modifications included diGly addition to lysine (114.042927), heavy labeling of arginine (10.008269) and oxidation of methionine (15.994946). The precursor mass tolerance was set to 25 ppm, and three missed cleavages were allowed. The target-decoy method was used to estimate false discovery rates (Elias and Gygi, 2007), and linear discriminant analysis (LDA) was employed to filter ubiquitinated peptides to an initial 1% peptide-level FDR, indicated by the number of decoy sequences in the filtered data set. Subsequently, peptides were assembled into proteins and further filtered to a protein-level FDR of 1%, as described (Huttlin et al., 2010). After applying these filters, final peptide-level FDRs were 0.11% for the three replicates in Figure 2 and 0.15% for the washout experiment in Figure S2D. Due to the inability of trypsin to cleave immediately after ubiquitinated lysine, we did not allow di-Gly modifications to occur on C-terminal lysine residues. The diGly remnant was found on over 70% of immunopurified peptides in each replicate but only 0.06%–0.08% of peptides prior to peptide IP, indicating an approximately 1000-fold enrichment. Peptide quantification was performed using an algorithm developed in the Gygi laboratory (Bakalarski et al., 2008). To ensure reliable quantification, we required that the signal-to-noise (S/N) values for heavy and light species each be greater than 5.0. If this criterion was not met, the peptide was still considered if one of the two S/N values was above 10.0. Peptides without quantification values in Table S3 failed to meet one of these criteria. Heavy-to-light ratios underwent log2 transformation, and these log2 values were averaged across the three replicates for each unique peptide. Peptides with an average decrease in abundance of more than two-fold were considered QUAINT substrates in Figure 2. The QUAINT overlap with the MLN4924-GPS screen (Figure 2D) was determined by manually inspecting the graphs of all 295 proteins that have a 2-fold changing peptide and are encoded by the ORFeome collection. In Table S7, where we identify proteins that scored in overlapping genetic and/or proteomic screens, we used a cutoff of 1.5fold. Antibodies Used in This Study Phospho Histone H3- Serine 10 (Abcam ab47297), Vinculin (Sigma-Aldrich V9131), Cyclin B (Santa Cruz SC-752), HDAC3 (Millipore 04-230), NUSAP1 (Bethyl A302-596A), NFE2l2/NRF2 (Epitomics 2178-1), CDT1 (Abcam ab70829), Ran (BD Biosciences 610340), FLAG (M2) (Sigma-Aldrich F3165), GFP (JL-8) (Clontech 632381), Cyclin F (Epitomics S1256), TUBULIN (DM1A) (Fisher Sciences 05829MI), Cyclin H (CCNH) (Epitomics 2800-1), CSN7A (Epitomic5213-1), MOV10 (Bethyl A301-571A), BTBD1 (Epitomics T2278), THOC1 (Bethyl A302-839A), PKM2 (Cell Signaling Technologies 4053S), SEPHS2 (Epitomics S1503), MCM5 (Bethyl A300195A), APBB1IP (Epitomics 455-1), PTM Scan Ubiquitin Remnant Antibody (Cell Signaling Technologies, 5562). siRNA Oligonucleotides Used in This Study All siRNA oligonucleotides were purchased from Dharmacon. The following sequences were used for depletion. Cyclin F siRNA Sequences. 1, ucacaaagcauccauauug; 2, gaagucauguuuacagugu; 3, uagccuaccucuacaauga; and 4, gcacccgg uuuaucaguaa. Nusap1 siRNA Sequences. 1, gauaauagagcauaagcguu; 2, ccacuuuagucacgagauc; and 3, cagccaacgacgcucgcaa. Firefly Luciferase siRNA Sequence. 1, cguacgcggaauacuucgauu. SUPPLEMENTAL REFERENCES Bakalarski, C.E., Elias, J.E., Ville´n, J., Haas, W., Gerber, S.A., Everley, P.A., and Gygi, S.P. (2008). The impact of peptide abundance and dynamic range on stableisotope-based quantitative proteomic analyses. J. Proteome Res. 7, 4756–4765. 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. Finn, R.D., Mistry, J., Tate, J., Coggill, P., Heger, A., Pollington, J.E., Gavin, O.L., Gunasekaran, P., Ceric, G., Forslund, K., et al. (2010). The Pfam protein families database. Nucleic Acids Res. 38 (Database issue), D211–D222. Huang, W., Sherman, B.T., and Lempicki, R.A. (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57.

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Huttlin, E.L., Jedrychowski, M.P., Elias, J.E., Goswami, T., Rad, R., Beausoleil, S.A., Ville´n, J., Haas, W., Sowa, M.E., and Gygi, S.P. (2010). A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174–1189. Luo, J., Emanuele, M.J., Li, D., Creighton, C.J., Schlabach, M.R., Westbrook, T.F., Wong, K.K., and Elledge, S.J. (2009). A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene. Cell 137, 835–848. Smoot, M.E., Ono, K., Ruscheinski, J., Wang, P.L., and Ideker, T. (2011). Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27, 431–432. Stark, C., Breitkreutz, B.J., Chatr-Aryamontri, A., Boucher, L., Oughtred, R., Livstone, M.S., Nixon, J., Van Auken, K., Wang, X., Shi, X., et al. (2011). The BioGRID Interaction Database: 2011 update. Nucleic Acids Res. 39 (Database issue), D698–D704. Yen, H.C., and Elledge, S.J. (2008). Identification of SCF ubiquitin ligase substrates by global protein stability profiling. Science 322, 923–929. Yen, H.C., Xu, Q., Chou, D.M., Zhao, Z., and Elledge, S.J. (2008). Global protein stability profiling in mammalian cells. Science 322, 918–923.

S4 Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc.

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Figure S1. Development of a Second-Generation GPS Screening Platform, Related to Figure 1 (A) Graphical depiction of the first generation MSCV and second generation lentiviral GPS vectors. The pGPS-LP vector contains a PGK-Puro selection marker and T7 promoter. (B) Random ORFs were chosen to compare the viral packaging efficiency between the first and second generation (MSCV and Lenti) GPS constructs. (C) Microarray hybridization of the original pMSCV-GPS v.1 (top) and the pGPS-LP (bottom) viral ORFeome libraries. The Lenti-GPS vector shows better preservation of longer ORFs and better signal intensity across the entire library. (D) The hybridization intensity for GPS v.1 (MSCV based) and GPS v.2 (lentivirus based) is shown relative to background. (E) The number of probes per ORF is depicted for the first generation ORFeome microarray and the second generation microarray which contains additional probes per gene.

Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc. S5

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S6 Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc.

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THOC1 Figure S3. Validation Data for the GPS MLN4924 Screen, Related to Figures 1 and 2 (A) 293T cells expressing the indicated ORFs in the GPS vector were analyzed by flow cytometry after 4 hr treatment with MLN4924. The microarray probe distributions are shown at the right for three randomly selected probes for each of the indicated ORFs. A complete list of the validation status of tested proteins can be found in Table S2. (B) HeLa cells were treated with the indicated doses of MLN4924. The twelve proteins examined with endogenous antibodies include Cyclin H (CCNH), APBB1IP, MOV10, HDAC3, SEPHS2, THOC1, BTBD1, CSN7A, MCM5 and NUSAP1 (See Figure 6A).

Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc. S7

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Figure S4. Validation Data for the GPS CRL4 Screen, Related to Figure 3 (A) 293T cells expressing GPS-CDKN1A were treated with DMSO or 4NQO (1 mg/ml for 1 h) and analyzed by flow cytometry. CDKN1A is degraded following treatment with 4NQO. (B) The PSI for each probe from the CRL4 GPS screen, comparing DN-Cul4 treated and control cells. (C) A subset of proteins that validated in 293T cells when treated with DN-Cul4 were re-examined in HeLa and U2OS cells. HeLa and U2OS cells stably expressing the designated GPS-ORF were treated with empty vector (red) or DN-Cul4 (blue) and analyzed by flow cytometry. (D) 293T cells were treated with DN-Cul4 for 24 hr and were then treated with cycloheximide and collected for immunoblot at various time points. Immunoblots for HDAC3 show that it is degraded in empty vector, but not DN-Cul4-treated cells following addition of cycloheximide. Graphed is a semiquantitative analysis of HDAC3 protein levels, relative to loading controls, from the immunoblot shown in the figure. (E) MCM complex components scored in the MLN4924 GPS screen, CRL4 GPS screen and in the QUAINT MLN4924 screen. The microarray probe distributions from the MLN4924 and CRL4 screens are shown (top). Validation of GPS-MCM components in 293T and HeLa cells is shown (bottom).

S8 Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc.

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Figure S5. NUSAP1 Stability is Cell Cycle and DNA Damage Regulated, Related to Figures 6 and 7 (A) U2OS cells were transfected with four independent siRNA targeting Cyclin F. Sixty hours later, lysates were prepared and immunoblotted for NUSAP1, Cyclin F and Ran. (B) HeLa cells were synchronized at the G1/S boundary using a double thymidine block and were collected for immunoblotting after release. Lysates were immunoblotted for NUSAP1, phospho-Histone H3 at Serine 10 (to delineate mitosis) and Vinculin. Cell cycle analysis was performed on cells from the same experiment using propidium iodide staining and flow cytometry (bottom). (C) HeLa cells treated with 50 J/m2 UV and examined by immunoblotting 1, 2 and 4 hr after treatment. (D) 293T, HeLa and U2OS cells were treated with 10 Gy ionizing radiation (IR) and analyzed by immunoblotting of 293T, HeLa and U2OS cells 1, 2 and 4 hr after IR. (E and F) Cells treated for 72 hr with three independent siRNAs targeting NUSAP1 or non-targeting siRNA to firefly luciferase (FF) and were examined by immunoblot (E) and propidium iodide cell cycle analysis (F). (G) HCT116 cells were treated with siRNA targeting NUSAP1, or FF, for 24 hr and were then incubated in either nocodazole or taxol for an additional 72 hr with the indicated drug concentrations. Survival of NUSAP1 siRNA treated cells, relative to siFF treated cells, was examined using cell titer glo. Each drug concentration and siRNA treatment was tested in triplicate and the graph reports the relative survival (siNusap1/siFF) as the mean +/ the standard deviation. (H) U2OS cells were treated with siRNA targeting the 30 UTR of NUSAP1 (siRNA #2 in previous experiments E-G), or FF, for 24 hr and were then incubated in taxol for an additional 72 hr with the indicated drug concentrations. Survival of cells treated with siFF, siNUSAP1 and siNUSAP1 + NUSAP1 cDNA was examined using cell titer glo with each siRNA and drug combination performed in triplicate. Survival is reported as the mean +/ the standard deviation.

Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc. S9

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# of Interactions Figure S6. Protein Interaction Networks Depicting Betweenness for CRL-Validated Proteins, Related to Figures 1, 2, 3, and 5 (A–C) The interaction networks for validated proteins from the combined MLN4924 and CRL-GPS screens (A), SCF-GPS screens (B) and CRL4 GPS screen (C) are shown. Proteins in cyan were analyzed for betweenness and were used to generate the networks. The size of a proteins circle is indicative of its degree of connectivity. (D) Graphs show the degree of connectivity for proteins in the yeast essential gene collection (top), proteins in our list of 472 high priority candidates CRL substrates which scored in more than one overlapping screen (middle; Table S3) and proteins which scored in our QUAINT-MLN4924 screen with an average 2-fold change (bottom; Table S7). The x axis corresponds to the number of interactions for an individual protein and the y axis corresponds to the frequency of proteins in the database that have at least that many interactions.

S10 Cell 147, 459–474, October 14, 2011 ª2011 Elsevier Inc.