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 () (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 custom designed 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 ().
GPS Profiling and Chemical Genetic Inhibition of the Nedd8 Pathway Identifies CRL Substrates
To improve GPS, we designed a lentiviral reporter vector (pGPS-LP) (Figure S1A
) that can infect non-dividing 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 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 (Figure S1D and S1E
, Table S1
). These microarrays contain 46,000 probes with an average of ~3.3 probes per gene, and 80% of genes have ≥ 2 probes. We also developed a scoring system that considers changes in Protein Stability Index (ΔPSI), 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 (). 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 ( and Figure S2C
). We ultimately screened our GPS library with 1 µM MLN4924 for 4 hours, conditions that do not affect the cell cycle (Figure S2A
We GPS screened on a 293T lentiviral GPS-library treated with either DMSO or MLN4924 (). We hybridized PCR amplified ORFs (sorted versus unsorted) onto second generation 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 8 bins, comparing treated and untreated conditions (see ). The 2nd 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 DMSI treated samples showed a linear relationship, with an R2
value of 0.92 (). When comparing treated and untreated conditions within a single bin, each individual bin showed an R2
value ≥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 8 bins are almost indistinguishable, suggesting strong agreement and low cross-reactivity.
Probes were ranked according to their ΔPSI and 1000s 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, JUN and PDCD4 (). A recent study examining cullin interactors using IP-MS/MS (Bennett et al., 2010
) identified specificity factors co-precipitating with specific cullins. Since many specificity factors are ubiquitylated by their cognate ligase we examined this dataset. 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, 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 (). 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 employed an immunoaffinity reagent specific for tryptic ubiquitin remnants, the PTMScan ubiquitin remnant motif antibody. This antibody specifically recognizes a di-glycine 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 QUAINT (Quantitative Ubiquitylation Interrogation). Cells were treated with MG132 alone or with MG132 and MLN4924 (). MG132 was included to capture ubiquitylated substrates that would otherwise be degraded by the proteasome. The 4 h 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 false discovery rate of 0.11% (, Table S3
). Internal validation of peptide identification was provided by the fact that 5,114 (>50%) peptides overlapped between at least two experiments (). Since 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 two-fold in at least one replicate ().
Mass Spectrometry Identification of CRL Substrates using QUAINT
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 two-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 contributing to the mean H/L ratios were very similar across replicates, as 77% of the 364 peptides were reduced ≥2-fold and 94% trended (reduced ≥1.5-fold) in a second replicate. This yielded many known substrates, including SETD8, NFkB, Cyclin D, CDT1, HIF1A, POLR2A, YBX1, CDC25A, ORC1, β-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 411 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- value < 10−50). 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 (pre-MLN4924) 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 . Known substrates, substrate adaptors, proteins with known interactions with CRLs, and proteins that scored in other GPS screens for CRL substrates (see below) are colored (see Figure Legend). 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 co-expressed dominant negative Cul4A and Cul4B (DN-Cul4) 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 (). 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 ().
GPS Screen to Identify CRL4 Substrates
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 h and both conditions were treated with 4NQO for 2 h prior to FACS since 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
), suggested the screen data is of high quality. After ΔPSI 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 EGFP tagged proteins after DN-Cul4 and all validated (Table S4
). Importantly, reanalysis of the MLN4924-GPS screen graphs for each of the 279 candidates revealed that substrates shifting in both CRL4 and MLN4924 GPS screens validated at a rate of 72% (,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 . High confidence hits scoring in both the MLN4924 and CRL4 GPS screens and validated when re-examined by flow cytometry are depicted in .
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 disease. FTH1 (ferritin heavy chain) scored in both the MLN4924 and CRL4 GPS screens and was validated in 293T and HeLa cells ( and Figure 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 ( and Figure 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 and SLC39A13. SLC38A2 is a sodium dependent amino acid transporter and SLC29A2 is a nucleotide transporter. In addition, LDHB (lactate dehydrogenase b), which catalyzes the inter-conversion 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 ( and Figure 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 the CDK-activating Kinase (CAK) and TFIIH and a key regulator of RNA Pol II, scored in both the CRL4 and MLN4924 GPS screens and validated by endogenous immunoblotting after MLN4924 treatment. FAM53A, was strongly stabilized in both the CRL4 and MLN4924 GPS screens, and validated with DN-Cul4, is a nuclear protein involved in dorsal neural tube development ( and Figure S4C
) (Jun et al., 2002
). ETS2, which scored in the CRL4 and MLN4924 GPS screens and validated with DN-Cul4, is a winged helix-turn-turn transcription factor involved in telomere maintenance through hTERT transcriptional regulation, and in maintenance of trophoblast and colonic stem cells () (Munera et al., 2011
; Wen et al., 2007
; Xu et al., 2008
). HDAC3 interacts with SMRT and N-CoR in a nuclear co-repressor complex and scored and validated in both DN-Cul4 and MLN4924 screens () (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 ( and Table S4
). GPS-MCM7 also validated in HeLa cells following DN-Cul4 treatment (Figure S4E
). While 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
GPS Screen to Identify CRL3 Substrates
CRL3 GPS Screen
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 (). GPS-NRF2 was stabilized following DN-Cul3 treatment to the same extent as strong oxidative stress induced by TBHQ treatment (tert-Butylhydroquinone; ).
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 cross-referenced 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; and Table S5
). This list is enriched for proteins containing the BTB-Kelch fold found in CRL3 specificity factors (). 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 approximately 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 GPS-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 β-TRCP by yeast-two hybrid (Rual et al., 2005
) and contains the β-TRCP degron sequence (DSGxxS), strongly suggesting that it is controlled by SCFβ-TRCP
Validated proteins that overlap between the MLN4924 and SCF GPS screens are shown in . Proteins scoring in both SCF and MLN4924 screens validated at a rate of ~81% (Table S6
). Since 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
GPS Screen to Identify SCF Substrates
Identifying Specific E3 Ligases: NUSAP1 is a substrate of SCFCyclin F
Since phosphorylation can drive proteolysis, we cross-referenced our overlap lists with phospho-proteomic 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., 2003
; Ribbeck et al., 2006
; Ribbeck et al., 2007
). To identify the specific ligase controlling NUSAP1, we treated cells with MLN4924 or DN-Cul. MLN4924 confirmed the CRL-dependency () and only DN-Cul1 produced a significant increase in NUSAP1 levels (). 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 h post transfection, cells were harvested and immunoblotted for NUSAP1. We found that depletion of Cyclin F, the founding member of the F-box family, increased the levels of NUSAP1 (). 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 post-translational, Cyclin F was depleted from 293T cells expressing GPS-NUSAP1 or GPS-MDH1 (negative control). Cyclin F depletion caused an increase in the EGFP/DsRed ratio of GPS-NUSAP1 cells (), reflecting an increase in EGFP-NUSAP1 stability, relative to the siRNA.
NUSAP1 is an SCFCyclin F Substrate
Since 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 (). This was expected since 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 ( 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 h of siRNA depletion ( and Figure S5B
). Importantly, Cyclin F depletion does not affect cell cycle timing following release ( and Figure 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 (). We conclude that SCFCyclin F
targets NUSAP1 for degradation during S and G2 phases of the cell cycle.
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., 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 UV and 4NQO, but not IR, caused NUSAP1 degradation in U2OS, HeLa and 293T cells (, S5C, S5D
). Degradation was observed at 1 h 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 (). 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 (). However, depletion of Cyclin F had no effect on the UV-induced NUSAP1 degradation, indicating it is likely to be a substrate of two distinct SCF ligases ().
NUSAP1 is Degraded Following UV and its Loss Sensitizes Cells to Anti-Tubulin Chemotherapeutics
NUSAP1 Maintains Resistance to Anti-tubulin Therapeutics
resides on chromosome 15q15.1, a region frequently deleted in a wide variety of cancers. The role of NUSAP1 in microtubules prompted us to examine the potential sensitivity of NUSAP1 depleted cells to anti-tubulin chemotherapeutics. We found that depletion of NUSAP1 with 3 independent siRNA made both U2OS and HCT116 cells highly sensitive to treatment with the anti-tubulin cancer therapeutic taxol relative to siFF treated controls ( and S5G
) without perturbing the cell cycle distribution (Figure S5E, F
). This phenotype was rescued by re-introduction of a NUSAP1 ORF lacking the 3’UTR following depletion with the siRNA targeting the 3'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 ( and S5G
). This suggests that the presence of NUSAP1 makes cells more resistant to the toxic effects of anti-tubulin chemotherapeutics and could explain taxol sensitivity in tumors deleted for NUSAP1.
CRLs have non-overlapping functions
Functional categorization and domain analysis of proteins identified by QUAINT and GPS are shown in Tables S1
and 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 (). As well as domain enrichment for WD40 repeat proteins, which serve as substrate adaptors for CRL4, and protein kinases, zinc fingers and others (). 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 analysis for putative CRL3 and SCF substrates are shown in and , respectively. Domain analysis 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 proteins centrality within an interaction network. A higher degree of betweenness indicates a greater degree of inter-connectivity 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 value of 3.96 ×10−15
; 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 () also showed a high degree of betweenness (p-value of 1.1×10−22
, 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-values of 3.7×10−3
, respectively: datasets in Table S7
). Network diagrams showing the betweenness centrality of putative substrates from these screens are depicted in Figure S6
. A sub-network for SCF is also shown in . 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.