Rozenblatt-Rosen et al., 2012
SUPPLEMENTARY INFORMATION doi:10.1038/nature11288
Contents
Supplementary Figures and Legends
1 Summary of viral ORFs screened 2 Overlap of Y2H and TAP-MS data sets 3 Enrichment of GO terms for targeted host proteins 4 Network of HPV E7 protein complex associations 5 Chromatin accessibility of IRF1 binding sites 6 Regulatory loops 7 Heatmap of transcriptome perturbations 8 Growth rate and senescence of selected IMR-90 cell lines expressing viral ORFs 9 Viral proteins, transcription factors and clusters 10 MAML1 is targeted by E6 proteins from HPV5 and HPV8 11 The E6 protein from HPV5 and HPV8 regulates expression of Natch pathway responsive genes HES1 and DLL4 12 DNA tumour viruses target cancer genes 13 Reproducibility of viral-host protein associations observed in replicate TAP-MS experiments as a function of the number of unique peptides detected 14 Somatic mutations in genome-wide tumour sequencing projects 15 Network of VirHostSM to host targets and cancers 16 The IMR-90 cell culture pipeline 17 Silver stain analyses of viral-host protein complexes 18 Cluster propensity of microarrays before and after ComBat 19 Cluster coherence 20 Expression bias of TAP-MS associations 21 Overlap of viral-host protein pairs identified through TAP-MS with a literature-curated positive reference set 22 Assessment of Y2H data set
Supplementary Methods
A. Viral ORFeome cloning B. Yeast two-hybrid (Y2H) assay C. Cell culture pipeline D. HPV E6 oncoproteins and Notch signalling E. Tandem Affinity Purifications (TAP) followed by Mass Spectrometry (MS) F. Subtracting likely non-specific protein associations (“Tandome”) G. TAP reproducibility H. Virus-human Positive Reference Set (PRS)
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I. Pathway enrichment analysis J. Microarray preprocessing and differential expression K. Microarray gene and sample clustering L. Predicting cell-specific transcription factor binding sites M. Transcription factor binding site (TFBS) enrichment analysis N. Randomization of gene clusters for enrichment analyses O. Evaluating predictive power of regulatory cascades P. Building a probabilistic map of IMR-90 specific RBPJ binding sites Q. Notch pathway enrichment analysis R. Identification of loci implicated in familial and somatic cancer S. Testing enrichment of gene sets for cancer genes T. Viral target overlap with candidate cancer genes identified by transposon screens U. Comparison of viral interactome and prioritization of cancer genes
Supplementary Tables (available on Nature website)
1 List of viral ORFs Summary of all viral ORFs tested, including all information for each viral ORF and the data generated for each ORF. 2 Y2H data set Summary of virus-host binary interactions identified by Y2H screens. 3 Host proteins targeted by List of host proteins in the Y2H network that are viral proteins in Y2H significantly targeted or untargeted by viral proteins. 4 TAP data set List of virus-host protein associations identified in two independent TAP-MS experiments. 5 GO cluster summary List of the GO terms that are enriched in each cluster of differentially regulated genes, and the adjusted P values and odds ratios associated with them. 6 KEGG cluster summary List of all the KEGG terms that are enriched in each cluster of differentially regulated genes, and the adjusted P values and odds ratios associated with them. 7 TF list by Y2H,TAP,Array Virally targeted TFs with an enriched number of predicted binding sites for genes within each cluster. Listed TFs come from either associations through TAP-MS, interactions through Y2H, or are differentially expressed in response to viral ORF expression. 8 COSMIC Classic annotation COSMIC Classic genes are annotated based on literature review as tumour suppressor, oncogene or both. If no call could be made, the designation is left as unclear. 9 VirHost data set 947 VirHost proteins identified through TAP-MS association (3 or more unique peptides), Y2H interaction, or as differentially expressed TFs from motif enrichment analysis. 10 Transposon Candidates Overlap between Sleeping Beauty and murine leukemia virus transposon-based screens and VirHost data set.
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11 Polyphen score Cumulative POLYPHEN2 score calculated for each gene with one or more somatic mutations from genome-wide tumour sequencing data. 12 GWAS–VirHost, SCNA-DEL– Genes at intersection of VirHost data set with genes VirHost, SCNA-AMP–VirHost identified through cancer GWAS or through cancer SCNA intersections analysis (amp=amplification, del=deletion). 13 PCR Primers and viral ORF PCR primers used to generate viral ORFs from Ad5, sequences HPVs and PyVs and sequence of each viral ORF. 14 Autoactivators List of viral ORFs that act as auto-activators in Y2H or wNAPPA protein interaction assays. 15 Tandome List of host proteins identified in more than 1% of 108 control TAP experiments (Tandome). 16 Y2H PRS, TAP-MS PRS List of Y2H and TAP-MS Positive Reference Sets (PRS). 17 Pathway enrichment analysis Gene Ontology term enrichment for viral targets identified by TAP-MS for Ad5, EBV, HPV and PyV viruses; Odds ratio and resampling-adjusted P-values are provided. 18 Microarray batches Lists of the individual microarrays in the gene expression data set, and the batch in which each one was processed. 19 Differentially regulated genes Annotation of the most frequently perturbed host genes, with the cluster membership, fold changes, and adjusted P values in each cell line expressing a viral ORF relative to control cell line. 20 GWAS loci Mapping of loci previously identified through cancer GWAS to nearby genes. 21 SCNA loci Loci previously identfied through SCNA analysis of cancers, with corresponding statistical evidence (q-value and residual q-value) and genes within each interval. 22 wNAPPA results wNAPPA scores for tested viral ORF interactions.
Supplementary Notes
1. Significantly targeted and untargeted proteins 2. HI-2 and analysis of overlaps between Y2H, TAP-MS and their respective PRS 3. Measurement of the precision of the Y2H data set by wNAPPA 4. Network motif identification 5. Cellular growth phenotypes 6. Cancer pathways perturbed by viral ORFs
Supplementary References
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Viral ORFs screened Viral ORF data sets
Transduced into 0 IMR90: 87 1 TAP-MS: 54
55 68 19 26 24 29
Y2H: 53 Tested by Y2H: 123 Microarray: 63 3 7
Supplementary Figure 1. Summary of viral ORFs screened. Overlap of the number of viral ORFs tested in the Y2H and cell line branches of the experimental pipeline (left Venn diagram), and the overlaps of viral ORFs that yielded data in each channel of the experimental pipeline (right Venn diagram).
Rozenblatt-Rosen et al., 2012
250 TAP-MS V-H SV40 TP53 LT 200 Y2H V-H
150 Virus Host Y2H EBV-EBNALP SP100 EBV-EBNA1 KPNA6 100 EBV-EBNA1 SRPK2 P < 0.001 P < 0.001 SV40-LT ZC3H18 50 SV40-LT TP53 SV40-LT CNBP
0 250 TAP-MS V-H Frequency SV40 TP53 200 LT Y2H V-H Y2H+N(HI-2) 150 OR 100 P < 0.001 P < 0.001 TAP-MS PDLIM7 V-H 50 HPV8 Y2H E6 H-H 0 Y2H 0 10 20 30 40 0 10 20 30 40 V-H GEM Number of virus-host protein interactions overlapping between Y2H and TAP-MS
Sampling space Y2H TAP-MS
Supplementary Figure 2. Overlap of Y2H and TAP-MS data sets. Upper panels: number of virus-host interactions observed (green arrow) in Y2H and TAP-MS versus those seen by chance through random sampling of the Y2H (red) or TAP-MS (blue) search spaces, with six shared inter- actions observed listed. Lower panels: corresponding overlaps with expanded (Y2H+N(HI-2)) network, which includes human proteins “one hop” away in the HI-2 human-human interactome network.
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7 SUPPLEMENTARY INFORMATION RESEARCH
Rozenblatt-Rosen et al., 2012 Adenovirus HPV EBV Polyomavirus
PDLIM7TP53 STRN3 PDLIM7 PDLIM7BAX ATPase activity
STRN STRN4 BIK BAK1 GPI-anchor transamidase complex
POLYO ADENO5 Mitotic cell cycle
Proteasome complex PP2A binding BH domain binding
Apoptosis PDLIM7P4HA2 P4HA3 PDLIM7
P4HA1 P4HB Procollagen-proline 4-dioxygenase activity
Protein phosphatase 2A binding EBV
BH domain binding Procollagen-proline 4-dioxygenase activity mRNA 5’UTR binding
1 5 Odds Ratio
Supplementary Figure 3. Enrichment of GO terms for targeted host proteins. Enrichment of GO terms for host proteins physically interacting with viral proteins (Supplementary Table 17). Three examples are shown. All Odds Ratios higher than 5 were set to 5 for visualization purposes.
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120 P = 0.424 600
60 300 P < 0.001 Frequency Frequency
0 0 0 15 30 10.5 21.5 Low-risk Shared host targets High-risk Ratio = E7 of same class Cutaneous of HPV-E7 proteins E7 of different class
Supplementary Figure 4. Network of HPV E7 protein complex associations. Network of protein complex associations of E7 viral proteins from six HPV types (hexagons, coloured according to disease class) with host proteins (circles). Host proteins that associate with two or more E7 proteins are coloured according to the disease class(es) of the corresponding HPV types. Circle size is proportional to the number of associations between host and viral proteins in the E7 networks. Viral-host protein complex associations (links) are weighted by the number of unique peptides detected for the host protein (thin links: 1-2; thick links: ≥ 3). Distribution plots of 1,000 randomised networks and experimentally observed data (green arrow) for the number of host proteins targeted at random by two or more proteins in the corresponding sub- networks (left grams), or the ratio of the probability of a host protein being targeted by viral pro- teins from the same class to the probability it is targeted by viral proteins from different classes (right histograms). Representative random networks selected from these distributions are shown as insets in the histograms.
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200
IRF1
150
IFI6 IFI35 IFITM1 IFIT3 IFIT2 IFIT1 100
Response to Type I interferon
Padj < 0.001 50 OR = 40
IMR90 DNAse Accesibility (counts per 10 million reads) 0 ● ● IFIT3 IFIT1L IFIT1 91,100,000 91,120,000 91,140,000 91,160,000 Position (bp) AAACTGGAAAGTGAAACT AAAGGGGAAAGTGAAACT Site 1 Site 2 2.0
1.5
1.0
0.5 Information content
0
2 4 6 8 10 12 14 16 18 Position IRF1 binding motif
Supplementary Figure 5. Chromatin accessibility of IRF1 binding sites. DNase accessible canonical IRF1 binding sites in the promoters of interferon inducible genes highly expressed in response to Group III viral protein expression (left). Predicted targets of IRF1 within cluster C24 are significantly annotated for the GO term “Type I interferon response” (right).
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150
100
E2F1 50 P < 0.001 Frequency
0 0 4 8 12 Autoregulatory loops
400
SMAD3
200
Frequency P < 0.001
TFAP2A
0 0 50 100 150 Feedback loops
RBPJ
150
100 TFAP2A
50 P < 0.001 Frequency
HES1 0 0 2,000 4,000 6,000 Feedforward loops
Supplementary Figure 6. Regulatory loops. Null distributions of autoregulatory (top), feedback (middle) and feedforward (bottom) motifs compared to the number of observed network motifs (green arrow) for enriched TFs.
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I II III
TP53 RB/E2F Enriched GO (KEGG) Enriched TF C1 [snoRNA]# C2 C3 (Phagosome) ERG GABPB2 FOS* NFE2L2 C4 C5 C6 Lysosome (Lysosome) RBPJ CREB3* EGR2 EHF STAT* C7 Lysosome (Lysosome) EGR2 FOSL2 C8 Regulation of fibroblast proliferation FOSL2 JUN TFAP4 STAT1 EGR2 C9 Lipid biosynthesis NFYA E2F* ZFP161 MYC FOSL* C10 C11 Response to radiation (Wnt signalling) E2F* FOSL2 JUN TFAP2A C12 TRAIL binding (p53 signalling pathway) STAT* TP53 REL FOSL2 E2F* C13 Cell-substrate adhesion FOSL2 C14 (Calcium signalling pathway) C15 Glutathione metabolism E2F* NRF1 EGR1 C16 TGF-beta signalling pathway E2F* STAT* TFAP4 EGR2 EP300 C17 Growth factor binding FOSL2 ARNTL C18 Proteinaceous extracellular matrix RELA FOSL2 JUNB TFAP4 EGR2 C19 Cell morphogenesis in differentiation STAT1 EGR2 C20 PDGFR signalling (Pathways in Cancer) C21 C22 Response to hypoxia REL E2F* MAFF PATZ1 NRF1 C23 Acute inflammatory response REL* NFKB* C24 Type I interferon response IRF1 STAT1 CREB* PATZ1 MAZ C25 Regulation of cell proliferation STAT NFKB* REL* CTCF E2F* C26 Nucleosome assembly POU2F2 CREB3* SP* E2F* CRX C27 DNA damage response BPTF REL TFAP2* NRF1 ETV4 C28 C29 Potassium ion import GABPB2 C30 (Circadian rhythm) E2F* C31 DNA damage response (DNA replication) E2F* SP* REL* CTCF MAZ LF2
5-E7 8-E7 EBV 8−E6 5−E6 Color Key BILF1 11-E6 6b-E6 16-E5 6b-E7 11-E7 16-E7 18-E5 16-E6 18−E6 18−E7 BFLF2 BXLF1 BALF1 BVLF1 BLRF2 BRLF1 BDLF4 BTRF1 BFRF2 BGLF3 BSRF1 BNRF1 BRRF2 BHRF1 EBNA1 LMP2A 18-E6X 11-E5B 6b-E5B 6b-E5A HPV E1B19K BNLF2B BNLF2A WU-LST E4ORF1 E4ORF3 EBNALP BDLF3.5 SV40-LT BFRF0.5 E4ORFB 16-E6XX BGLF3.5 TSV-LST SV40−ST E3GP19K JCCY-LST MCPyV-LT
16−E6NOX 18−E6NOX Adenovirus MCPyV−ST HPyV7-LST HPyV6-LST MCPyV-LST
JCMad1-LST −1 01 MCPyV-LTTR Polyomavirus Log2 (fold change) 16-E6NOXI128T
Supplementary Figure 7. Heatmap of transcriptome perturbations. Enlarged version of Fig. 2a. Enriched GO terms and KEGG pathways are listed adjacent to the numbered expression clus- ters. Transcription factors (TFs) with enriched binding sites and gene targets enriched for the listed GO and/or KEGG pathways that are physically associated with or differentially expressed in response to viral proteins are shown, with * denoting multiple members of a TF family. TFs are ranked by odds ratio (OR) for pathway enrichment, and up to 5 TFs are shown for any cluster. In cluster C1 eight of the nine transcripts are snoRNAs (# sign). Upper dendrogram is shaded by viORF grouping. Blocks show which viral proteins associate with the indicated host proteins, as detected in our data set (grey) or manually curated (green).
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R = 0.8 SV40-LT SV40-LT Padj = 0.008 EBV-BGLF3 0.3 HPV16-E6 N-GFP HPV18-E6NOX
C-GFP HPV6b-E6 SV40-ST HPV18-E6X 0.2 N-GFP
Growth rate (1/days) 0.1
MCPyV-ST MCPyV-LTTRUNC
0.0 HVP5-E6 HPV8-E6
-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4
Average cluster C31 log2(fold change)
100 HVP5-E6
HPV8-E6 R = -0.92
Padj = 0.016 80 MCPyV-LTTRUNC
60
40
Percentage of senescent cells 20 HPV18-E6X SV40-ST C-GFP N-GFP N-GFP EBV-BGLF3 MCPyV-ST HPV6b-E6 SV40-LT SV40-LT HPV16-E6
0 HPV18-E6NOX -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4
Average cluster C31 log2(fold change)
Supplementary Figure 8. Growth rate and senescence of selected IMR-90 cell lines expressing viral ORFs. Growth rate and senescence plotted against average expression of genes in cluster 31. Error bars in growth rate represent standard deviation across three samples in all cases except for MCPyV-LTTRUNC, which is across two samples, and HPV5-E6 and HPV8-E6, which represent one sample each. Error bars in senescence represent standard devia- tion across two samples.
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E2F2 NFYB Predicted Experimental GABPB1 BGLF3.5 ETS1 BNLF2B ELK3 C TF V NFE2L2 HPV16-E6NOX FOSL1 HPV16-E6 C28 PATZ1 ELF4 BSRF1 SP1 HPV16-E6NOXI128T SP3 E4ORFB C24 MYB STAT5A E4ORF3 MAZ SOX11 HPV18-E6NOX C CTCF E1B19K Differential expression? C25 ETV4 BRRF2 MYBL2 V RUNX1 LMP2A IRF1 HPyV6-LST C C30 JUN NFIB MCPyV-LTTRUNC ZFP161 C31 TFAP4 BILF1 SMAD3 MCPyV-LT FLI1 Viral HPV5 HPV18 SV40 HPyV6 HPV5 HPV8 HPV6b HPV11 TSV-LST BILF1 E4ORF3 LST E6 E6 E7 E7 C14 NFIA BFRF0.5 proteins E6 E6 LT KLF7 HPV8-E6 TEAD2 C9 EBNA1 TFAP2C HPV18-E6 FOS E2F3 HPV11-E5B POU2F2 TFs ELF4 ETV4 BPTF JUN TEAD2 TFAP4 FOSL2 STAT3 HPV16-E6XX MYC HPV5-E6 C22 MYBL1 C11 EGR1 MCPyV-ST HES1 BLRF2 C19 TP53 BDLF3.5 C23 LEF1 C20 BGLF3 Clusters C27 C8 C16 CREB5 BFLF2 SP4 C8 HBP1 DNA damage C15 HPV18-E7 Regulation of fibroblast ATF3 response C13 STAT2 proliferation CRX SV40-LT BPTF C3 MAX BNRF1 NFKB1 MAFF HPyV7-LST Viral HPV16 HPV18 HPV11 HPV6b C27 EP300 BVLF1 BRLF1 MCPyV-LST proteins E6 E7 E7 E7 EGR2 BALF1 C18 NFYA JCCY-LST E2F4 JCMad1-LST C26 EHF CEBPB HPV11-E6 TFs RELB BXLF1 EGR1 FOSL2 TFDP1 EBNALP NFKB2 BHRF1 C10 JUNB HPV6b-E6 NR5A2 RB1 WU-LST C7 E4ORF1 C17 ZNF281 Clusters C21 NFE2L3 BDLF4 C13 RORA HPV5-E7 PRDM1 TEAD4 BTRF1 Cell-substrate adhesion STAT4 BNLF2A CREB3L1 BRLF1 C2 ZNF219 LF2 GATA6 HPV6b-E5B REL C12 GABPB2 HPV6b-E7 EBV C5 CREBL2 HPV18-E5 C4 ARNTL FOSL2 HPV8-E7 CREB3 HPV C6 BACH1 HPV16-E7 C29 FOXK1 Adenovirus ERG E2F1 HPV11-E7 TFDP2 Polyomavirus E2F5 SV40-ST ELF1 STAT1 Causes expression change of TF TP73 TAP-MS Y2H Binding site enrichment
Supplementary Figure 9. Viral proteins, transcription factors and clusters. Network repre- sentation of all predicted viral protein-TF-cluster cascades (left). Schematic (right) shows how viral protein-TF-target gene network was constructed, with representative networks underneath.
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IMR-90 WCL IP: HA
L
C 1-E6 1-E6 W
P F HPV18-E6 HPV16-E6 HPV 1 HPV6b-E6 HPV8-E6 HPV5-E6 GF P G kDa HPV18-E6 HPV16-E6 HPV 1 HPV6b-E6 HPV8-E6 HPV5-E6 GF P WB kDa WB EP300 225 - 225 - EP300
150 - MAML1 150 - MAML1 102 - 102 -
102 - UBE3A 102 - UBE3A 76 - 76 - 38 - 38 - 31 - HA 31 - HA 24 - 24 - *
Actin 38 -
U-2 OS WCL IP: HA WCL
1-E6 1-E6
kDa HPV18-E6 HPV16-E6 HPV 1 HPV6b-E6 HPV8-E6 HPV5-E6 pNMCV siMAM L1 siControl WB kDa HPV18-E6 HPV16-E6 HPV 1 HPV6b-E6 HPV8-E6 HPV5-E6 pNMCV siMAM L1 siControl WB 225 - 225 - EP300 EP300 150 -
150 - 150 - MAML1 MAML1 120 - 102 -
102 - UBE3A 102 - UBE3A 76 - 76 -
24 - HA 24 - HA
52 - 38 - Actin
Supplementary Figure 10. MAML1 is targeted by E6 proteins from HPV5 and HPV8. Western blots of whole cell lysates (WCL) and co-immunoprecipitations of HPV E6 proteins in IMR-90 cells (upper panels) or U-2 OS cells (lower panels).
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Primers shRNA 2.0 HES1 1.2 1.8 Control 1.6 DLL4 1.0 MAML1 1.4 1.2 0.8 1.0 0.6 0.8 0.6 0.4 0.4 0.2 0.2 Relative expression Relative expression 0 0 MAML1 HES1 DLL4 GFP
HPV5-E6HPV8-E6 HPV6b-E6HPV11-E6HPV16-E6HPV18-E6 IMR-90
kDa Control MAML1 150 - MAML1 52 - Actin 38 -
U-2 OS Cells HES1 reporter 3
2 RLU 1
0 WB: FLAG ICD 5E6 8E6 11E6 11E6 6bE6 16E6 18E6 N-CMV No ICD
25, 50, 100 ng of plasmid
Supplementary Figure 11. The E6 protein from HPV5 and HPV8 regulates expression of Notch pathway responsive genes HES1 and DLL4. Upper panels: qPCR of Notch pathway responsive genes upon expression of E6 proteins from different HPV types (left panel) or upon knockdown of MAML1 (right panel). Error bars represent standard deviation across two experi- ments and three experiments, respectively. Western blot of MAML1 in IMR-90 cells upon shRNA knockdown. Lower panel: HES1 luciferase reporter assays of co-transfected Notch intracellular domain (Notch-ICD) and E6 proteins at increasing concentrations from different HPV types in U-2 OS cells. Error bars represent standard deviation across three experiments. Western blot (inset) depicts E6 protein expression from the indicated HPV type in a representative experiment.
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16 cancer proteins within 2.4 Y2H+TF+TAP-MS: P = 0.007 13 15 TAP-MS 2.2 Unique Y2H TF 563 peptides 59 ● 1 2.0 ● 2 4 16 ● 6 ● 3 12 4 286 0 0 1.8 5 COSMIC Classic
protein recovery 0
ichment in COSMIC Classic 91 17 13 2 ● 1 1.6 19 0 ● old en r 1 F P=0.05 1.0 1.5 2.0 2.5 3.0 −log(P−value) 19,398 proteins outside diagram
Supplementary Figure 12. DNA tumour viruses target cancer genes. Fold enrichment of COSMIC Classic genes in TAP-MS, Y2H and TF VirHost data sets as a function of the number of unique peptides (circle sizes) detected for host proteins identified through TAP-MS. Numbers of proteins in intersections of VirHost and COSMIC Classic genes are indicated. Venn diagram of overlaps between proteins identified by TAP-MS (≥3 unique peptides), Y2H, TF and COSMIC Classic genes. Rozenblatt-Rosen et al., 2012
98 99 100 97 95 90 90
80 80
70
TAP-A, TAP-B Reproducibility (%) 60 TAP-B, TAP-A 53 Average 50
40 12345678910 Minimum number of unique peptides
Supplementary Figure 13. Reproducibility of viral-host protein associations observed in replicate TAP-MS experiments as a function of the number of unique peptides detected. Data were plotted based on both experimental orientations (TAP-A then TAP-B, blue line and TAP-B then TAP-A, red line), with the average indicated by the green dashed line.
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Polyphen2 Prediction Cancer type (source)
Small Cell Lung Cancer Ovarian(WashU) Ovarian(Broad) Ovarian(Baylor) Non−Small Cell Lung Cancer Multiple Myeloma Medulloblastoma Head&Neck(Stransky) Head&Neck(Agrawal) GBM(WashU)
GBM(Baylor) Chronic Lymphocytic Leukemia
0 2000 4000 6000 Somatic mutation count Unscored Benign Possibly Damaging Probably Damaging
0.5 0.5
0.4 0.4 0.3
0.3 0.2
0.1 0.2 0.0
Polyphen2 mutation 0.1 TP53 PTEN KRAS EGFR NRAS deleteriousness score RHOA CDKN2A C19orf53 HIST1H4A HIST1H3A 0.0 20 40 60 80 100 Protein
Supplementary Figure 14. Somatic mutations in genome-wide tumour sequencing projects. Somatic mutations identified through genome-wide sequencing studies of human cancers and evaluated for impact using Polyphen2 (upper panel). Ranking of proteins with somatic mutations in cancer based on the cumulative inferred impact of all observed mutations (lower panel) (Supplementary Table 11). Inset: top ten ranked proteins.
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6b-E5A BFRF0.5 16-E5 18-E5 BSLF2/BMLF1 BNLF2A BALF1 RABL3 BHRF1 CHRM2 EBNA1 ARHGDIA BBRF2 RRAS2 E1B19K Head and neck F2R E4ORFB UBE2I 11-E5B PRAF2 BSRF1 TMEM126A BDLF4 LAMTOR1 E1A E4ORF3 BAK1 33-E7 BIK BILF1 KHDRBS2 EBNALP RPS24 5-E6 Multiple myeloma BDLF3.5 EGFR MCPyV-LTTRUNC C1orf50 STAU1 8-E6 KRTAP13-1 BNLF2B NFYB 8-E7 6b-E7 Glioblastoma FAM71C ROPN1 SV40-LT POLM 16-E7 DMRT3 BNRF1 NFE2L2 BGLF3 MAGEA1 18-E6X FOXK1 LMP2A ELF1 5-E7 FBXW7 EBNA3B Ovarian POLR1C BK-LTTRUNC MEOX2 JCCY-LST PSMA4 WU-LST CDK4 BTRF1 Medulloblastoma PSMB6 BVLF1 DNAJA3 11-E7 CNP MCPyV-ST ZBTB25 JCMad1-LST WDYHV1 HPyV7-LST GMCL1 18-E6 TP53 HPyV6-LST TAF9 18-E7 PRTFDC1 Small cell lung RB1 MCPyV-LT PSMC5 16-E6NOX 18-E6NOX COSMIC Classic PPP2R1A 16-E6 Regulation of apoptosis BGLF3.5 TF SV40-ST Y2H interaction TAP-MS association Causes expression change of TF
Supplementary Figure 15. Network of VirHostSM to host targets and cancers. Mapping of VirHostSM gene products to both tumours in which they are mutated (left) and to viral interactors (right). Proteins annotated with the GO term “regulation of apoptosis” indicated in purple.
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Cell bank Generation of cell lines Quality Controls
IMR-90 Bank P6 Puromycin selection
P0 Thaw P3 Thaw Western Blot Analysis P7 5 Split 1:5
P1 3 Split 1:3 P4 3 Split 1:3 Sequence Mycoplasma P8 9 Split 1:3 Testing viORF
P2 9 Split 1:3 P5 9 Split 1:3 1 x106 Bioanalyzer 0.5 x106 RNA Protein Freeze P3 27 Split 1:3 P6 27 Split 1:3
5 18 IMR-90 Transduce 26 viral genes & controls(GFP, SV40LT) P9 P12 Bank P7, P8, P10
Supplementary Figure 16. The IMR-90 cell culture pipeline. Generation of the IMR-90 cell bank (left), and generation of IMR-90 cell lines expressing viORFs (middle) and quality control (QC) measures taken (right).
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Viral Protein CTRL-GFP CTRL-GFP CTRL-GFP CTRL-GFP CTRL-GFP CTRL-GFP
Subcellular CN CN CN CN CN CN Fraction Cell Line # 1 33 59 85 99 111
Viral Protein CTRL-IMR90 CTRL-IMR90 CTRL-VECTOR
MW (kDa)
200 160 110 90 70
50 40 30
20 Subcellular CN CN CN 15 Fraction 10 Cell Line # 0 00
Viral Protein HPV-6b E5A HPV-6b E5B HPV-11 E5B HPV-16 E5 HPV-18 E5
Subcellular NM NM NM NM NM Fraction Cell Line # 44 45 47 48 49
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Viral Protein HPV-5 E6 HPV-6b E6 HPV-8 E6 HPV-11 E6 HPV-16 E6 HPV-18 E6 HPV-5 E6 HPV-8 E6
Subcellular CN CN CN CN CN CN CN CN Fraction Cell Line # 21 22 23 24 25 26 38 40
Viral Protein HPV-16 E6no* HPV-16 E6no* HPV-18 E6no* HPV-18 E6* I128T
Subcellular CN CN CN CN Fraction Cell Line # 87 91 92 93
Viral Protein HPV-5 E7 HPV-6b E7 HPV-8 E7 HPV-11 E7 HPV-16 E7 HPV-18 E7 HPV-5 E7 HPV-16 E7
Subcellular CN CN CN CN CN CN CN CN Fraction Cell Line # 15 16 17 18 19 20 96 113
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Viral Protein POLYO-SV40 POLYO-SV40 POLYO-SV40 POLYO-SV40 POLYO-SV40 POLYO-SV40 POLYO-SV40 LT LT LT LT LT LT sT
Subcellular CN CN CN CN CN CN CN Fraction Cell Line # 5 6 37 60 86 112 50
Viral Protein POLYO-MCPyV POLYO-MCPyV POLYO-MCPyV LT sT LsT
Subcellular CN CN CN Fraction Cell Line # 7 51 118
Viral Protein POLYO-JCCY POLYO-JCMad1 POLYO-BK POLYO-WU POLYO-HPy6 POLYO-HPy7 LsT LsT LsT LsT LsT LsT
Subcellular CN CN CN CN CN CN Fraction Cell Line # 114 115 116 117 119 120
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Rozenblatt-Rosen et al., 2012
Viral Protein EBV-EBNALP EBV-LMP2a EBV-BHRF1 EBV-BRLF1 EBV-BNRF1 EBV-EBNA1 EBV-BTRF1 EBV-BGLF3
Subcellular CN CN CN CN CN CN CN CN Fraction Cell Line # 14 53 54 56 57 70 77 78
Viral Protein EBV-BVLF1 EBV-BDLF4 EBV-BDLF3.5 EBV-BXLF1 EBV-BALF1 EBV-BNLF2a EBV-BNLF2b EBV-BFRF0.5
Subcellular CN CN CN CN CN CN CN CN Fraction Cell Line # 79 81 82 109 110 129 130 131
Viral Protein EBV-BSRF1 EBV-BGLF3.5 BFLF2 ADENO5 ADENO5 ADENO5 E1B19K E4ORF1 E3GP19K
Subcellular CN CN CN CN CN CN Fraction Cell Line # 132 135 136 102 122 125
Supplementary Figure 17. Silver stain analyses of viral-host protein complexes. A 15% aliquot of the final eluate of each TAP was resolved on polyacrylamide gels then visualized by silver-stain (shown here for the first TAP replicate).
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Rozenblatt-Rosen et al., 2012 a X6E X5A X5E X4E X5B X6B X3A X3B X2A X2B X4A X4B X1E X1B X2E X3E X6A X1A X7B X7A X7E X6C X6D X5D X2D X4D X3C X1C X1D X4C X2C X3D X5C X7D X7C X40c X14E X37B X37A X37E X33B X33E X17A X20A X20B X20E X22B X22A X40A X40B X85E X85B X14A X14B X88E X99E X77E X82E X79A X79B X75B X18A X16E X16B X15B X35E X35A X35B X53B X53E X53A X50A X50B X54B X16A X18B X19B X15A X17B X85A X93A X70E X70A X70B X74A X74B X77B X78E X86A X86B X87B X92B X92A X92E X87A X91A X26A X25A X26B X38E X38A X55A X55B X86E X50E X79E X59E X59B X57A X60A X60B X60E X78B X74E X81B X77A X81A X54E X17E X56A X56B X56E X58E X58B X58A X78A X59A X25B X25E X26E X55E X87E X19A X22E X36B X47A X47E X33A X48A X48E X44A X44B X99A X96A X96E X93E X88B X93B X96B X99B X88A X75E X75A X38B X21A X21E X23A X23E X34B X34A X24B X24A X98A X98B X98E X44E X48B X49A X49B X49E X91B X91E X57E X36A X36E X19E X15E X18E X47B X45A X45B X45E X51E X51A X51B X21B X23B X24E X54A X81E X57B X82A X37D X37C X20C X22D X22C X36D X48D X99C X99D X75C X34C X34D X24D X98C X98D X85D X85C X44D X44C X48C X49D X49C X14C X79C X81C X15C X35D X35C X53D X53C X54D X19C X70D X70C X74D X77D X81D X25D X26C X38C X38D X55D X86D X86C X40D X96D X88D X93D X79D X75D X59D X57C X60C X60D X59C X78C X74C X77C X15D X16D X17D X50C X54C X17C X16C X18C X56C X56D X93C X88C X96C X58C X58D X82C X20D X25C X26D X55C X87C X87D X14D X92C X92D X91C X91D X57D X36C X19D X18D X47C X47D X33C X33D X45C X45D X51C X51D X21D X21C X23C X23D X24C X78D X82D X50D X6C.3 X6C.1 X6C.2 X131B X110A X110B X131A X135A X125E X125B X122E X119A X119B X119E X120A X114B X114E X114A X130B X130E X136B X136E X118B X118E X102E X117E X120E X109E X109A X109B X112A X112E X112B X129A X129E X129B X122A X102A X107A X107B X107E X110E X111B X111E X135B X135E X123E X123A X123B X124B X124E X122B X117A X117B X115B X115E X115A X113A X113E X113B X131E X132B X132A X125A X111A X124A X121A X121B X136A X130A X127B X127A X118A X102B X120B X121E X127E X132E X119C X119D X114C X114D X115C X113C X113D X111D X130C X127C X129C X125C X131C X136C X136D X109C X109D X112D X112C X111C X122D X107D X107C X110D X110C X129D X135C X135D X123C X123D X124D X122C X115D X132C X124C X118C X118D X130D X102D X102C X117C X117D X120C X120D X121C X121D X127D X125D X131D X132D X20E.3 X20E.2 X20E.1 X20E.4 X6C_0407 X6C_0416 X20E_0416 X20E_0407 X6C_033010 X20E_033010
b X5E X2E X3A X7B X3E X6E X5A X6A X7A X7E X1B X1E X1A X3B X2B X2A X4A X4B X4E X5B X6B X4C X2C X7D X3C X2D X4D X6D X5D X5C X7C X1C X3D X1D X6C X40c X38A X26E X54A X21B X21E X59B X20B X36E X78B X70E X70A X24A X53E X53A X25B X87E X19A X59A X17E X78A X50A X50B X50E X88A X16A X18B X18E X37A X37E X75A X93E X99B X96B X79A X79B X74B X58E X58B X58A X38E X26B X54E X56A X56B X56E X23B X34A X17A X79E X15A X34B X20E X35E X35A X35B X70B X23A X23E X38B X40A X40B X21A X22B X77B X77E X53B X92A X92E X87A X91A X25A X26A X86A X86B X47A X47E X48A X48E X85E X85B X75E X78E X45E X45A X45B X14B X14A X14E X99E X33A X88E X82E X59E X17B X16B X15E X15B X85A X93A X18A X16E X48B X20A X81E X81B X77A X81A X96A X96E X36B X49A X98A X98B X98E X24E X60A X19B X91B X91E X86E X25E X36A X55A X55B X55E X19E X47B X74E X60E X75B X33B X33E X57A X57B X57E X37B X24B X44A X82A X88B X93B X74A X87B X92B X44B X44E X54B X99A X22A X22E X49B X49E X60B X51E X51A X51B X33C X56C X56D X23C X34C X85D X79D X16C X20D X35D X70C X21C X85C X78D X45C X15C X48C X19C X91D X88C X47D X40D X60D X45D X75C X59C X44C X57C X57D X93C X96C X70D X24D X53C X33D X82C X50C X37C X54D X99C X38D X87D X77C X54C X88D X93D X92C X92D X22D X81D X82D X59D X49D X49C X98C X24C X60C X48D X18C X91C X86D X86C X26D X25C X36C X51D X16D X78C X25D X50D X99D X79C X21D X58C X58D X38C X87C X26C X81C X74C X14C X14D X23D X34D X96D X15D X20C X35C X22C X77D X53D X75D X17C X36D X98D X18D X55D X55C X51C X19D X47C X44D X74D X17D X37D X6C.1 X6C.2 X6C.3 X117E X123A X109E X109A X127B X124B X124E X136B X118A X118B X122B X111A X112A X112E X135A X122E X125E X111B X130A X119A X119B X132B X131B X129A X129E X122A X117A X113A X113E X107B X110A X110B X110E X130E X125A X102E X129B X123E X120A X114B X114E X114A X131A X132A X123B X109B X127A X136E X118E X127E X107E X121B X124A X132E X112B X111E X115E X115A X135B X135E X119E X125B X117B X107A X120B X102B X136A X130B X131E X113B X121A X121E X115B X102A X120E X117D X136D X109C X109D X127C X122C X111D X112D X127D X107C X125C X131C X129C X130C X130D X119C X119D X122D X117C X118C X118D X107D X120C X120D X102D X102C X110D X110C X136C X135D X125D X131D X113C X113D X132C X111C X123D X123C X114C X114D X121C X121D X124D X124C X132D X129D X112C X115C X115D X135C X20E.2 X20E.3 X20E.4 X20E.1 X6C_0407 X6C_0416 X20E_0407 X20E_0416 X6C_033010 X20E_033010
Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 Group 7 Group 8
Rozenblatt-Rosen et al., 2012 Supplementary Figure 18. Cluster propensity of microarrays before and after ComBat. Hierarchical clustering of all microarrays (a) before and (b) after applying ComBat, an algorithm used for removing batch effects in microarray data.
40 30
20 15 P < 0.01 P < 0.01 Frequency Frequency
0 0 20 40 60 0.0 0.4 0.8 Enriched GO pathways Enriched KEGG pathways
Supplementary Figure 19. Cluster coherence. Frequency of KEGG and GO pathway enrich- ment as compared to randomly generated clusters.
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14
12
10 75th percentile
8 50th percentile Mean mRNA intensity in control samples 6 25th percentile
10 20 30 40 50 60 Average number of unique peptides detected
Supplementary Figure 20. Expression bias of TAP-MS associations. Viral targets identified through TAP-MS are biased towards highly expressed genes. Relationship between mRNA abun- dance in control samples of IMR-90 (y-axis) versus the number of unique peptides observed for viral associations for that protein (x-axis). Number of unique peptides is the average for two biological replicates. If more than one virus association is seen for a host protein, the association with the highest number of unique peptides is shown. Horizontal lines correspond to the indicated percentiles for mRNA abundance of all transcripts on the microarray.
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17 ● ●
16
15 ●
14
13
12 associations recovered
Number of host−vi rus PRS 11
10
30 35 40 45 50 55 60 Fold enrichment over random sets ● 1 ● 2 ● 3 4 5 Number of identified unique peptides
Supplementary Figure 21. Overlap of viral-host protein pairs identified through TAP-MS with a literature-curated positive reference set. All points are significant at P < 0.001. Dot size reflects minimum number of unique peptide detections required for protein identification.
Rozenblatt-Rosen et al., 2012
30 14
12 25 10 8
6 20 4
Recovery rate (%) 2
15 0 PRS RRS Y2H Data set 10 PRS (50) Recovery rate (%) rate Recovery RRS (94) Y2H Viral-Host 5 data set (447)
0 1 2 3 4 5 Signal threshold
Supplementary Figure 22. Assessment of Y2H data set. Percentage of Y2H interacting protein pairs positive in wNAPPA assay at increasing assay signal for PRS, RRS and Y2H viral-host data set. Inset: fraction of PRS or Y2H viral-host data set positive in wNAPPA at a threshold of 1% RRS positive.
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Supplementary Methods
A. Viral ORFeome cloning
Open-reading frame (ORF) clones encoding selected proteins from adenovirus 5 (Ad5), seven human papillomaviruses, and nine polyomaviruses (Supplementary Table 1) were obtained by
PCR-based Gateway recombinational cloning, following a protocol previously described for cloning Epstein-Barr virus ORFs4. To generate viral entry clones ORFs were PCR-amplified with
KOD HotStart Polymerase (Novagen). The PCR primers used contained attB1.1 and attB2.1 recombination sites fused to ~20 nucleotides of ORF-specific forward and reverse primers, respectively. Primers used to clone viral ORFs are listed (Supplementary Table 13). PCR products were then transferred into pDONR223 by a Gateway BP reaction, followed by transformation into chemically competent E. coli DH5α cells, selecting for spectinomycin resistance30.
Sequence-verified entry clone viral ORFs were transferred by Gateway LR recombinational cloning (Invitrogen) into appropriate expression vectors. Recombination products were directly transformed into E. coli (DH5α-T1R strain) via selection for ampicillin resistance in liquid LB media. Plasmid DNA was extracted from E. coli grown overnight in a 96-well format using a
Qiagen BioRobot 8000. The prepared plasmid DNA was used for transformations into yeast cells or for generation of retrovirus for subsequent transductions into IMR-90 human cells.
For assay of protein interactions by yeast two-hybrid (Y2H), viral ORFs (viORFs) were introduced into both pDEST-DB and pDEST-AD-CYH2 destination vectors9, generating Gal4
DNA binding domain (DB)-viORF hybrid proteins and Gal4 activation domain (AD)-viORF hybrid proteins, respectively.
For expression in mammalian cells, sequence validated viral ORFs in pDONR223 were recombined into either the Gateway destination vector MSCV-N-Flag-HA-IRES-PURO (NTAP) or MSCV-C-Flag-HA-IRES-PURO (CTAP) (gifts of M. Sowa and J. W. Harper)31 using LR
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Clonase (Invitrogen). The following primer sets were used for resequencing of the resulting
plasmids:
MSCV-N and MSCV-C Fwd: 5’-CCCTTGAACCTCCTCGTTCGACC-3’
MSCV-N Rev: 5’-GCCAAAAGACGGCAATATGGTGG-3’
MSCV-C Rev: 5’-GTCGGGCACGTCGTAGGG-3’
Adenovirus: Nine full length ORFs (Supplementary Table 1) were PCR amplified from Ad5
genomic DNA (human adenovirus 5 strain Adenoid 75 from the ATCC (ATCC VR-5) to generate
Gateway entry clones.
Epstein-Barr Virus (EBV): Eighty-one EBV ORFs were investigated (Supplementary Table 1).
EBV Gateway entry clones and Y2H DB-X and AD-Y clones were previously described4.
Selected EBV ORFs (Supplementary Table 1) were transferred to the NTAP vector for
subsequent transduction of IMR-90 cells.
Human Papillomaviruses (HPV): Seven HPV types32-38 (virus classifications from
http://pave.niaid.nih.gov/#prototypes?type=human) were chosen for this study: HPV6b, 11, 16,
18 and 33 of the alpha genus associated with mucosal lesions (gifts of Y. Jacob), and HPV5
and HPV8 (gifts of Y. Jacob and H. Pfister) of the beta genus which infect cutaneous epithelia.
ORFs clones encoding the early region proteins (E4, E5 proteins where relevant, E6, and E7) of
all seven HPVs (Supplementary Table 1) were amplified by PCR and cloned by recombination
into Gateway vectors.
HPV16 and HPV18 E6 variants: Due to the occurrence of internal splicing in HPV16-E6 and
HPV18-E6 and the desire to assure that full-length E6 proteins were expressed in IMR-90 cells,
various splice-defective derivatives of HPV16-E6 and HPV18-E6 were also generated. HPV11,
6b, 5 and 8 do not encode internally spliced versions of E6. HPV16-E6X, HPV16-E6XX and
HPV18-E6X correspond to the previously described major splice variants of these proteins39,40.
“E6X” designates the E6*, whereas E6XX designates the E6** splice variants. Primers used to
clone and mutagenize HPV16-E6 and HPV18-E6 variants are listed (Supplementary Table 13).
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HPV16-E6X, HPV16-E6XX and HPV18-E6X were cloned by PCR amplification using as template genomic DNA from IMR-90 cell populations that had been transduced with NTAP-
HPV16-E6 or NTAP-HPV18-E6 expression vectors. These cell lines had been shown to contain integrated copies of these splice variants during the quality control process of the IMR-90 cell culture pipeline. PCR products with the 5’ CACC overhang were directionally cloned into the pENTR vector, sequenced, cloned into the NTAP vector and re-sequenced.
To generate versions of HPV16 and HPV18-E6 proteins that do not undergo internal splicing events (designated E6NOX), the splice donor site(s) encoded within the respective E6 ORFs were eliminated by site-directed mutagenesis (QuikChange®; Stratagene)40. As a consequence the HPV16-E6NOX and HPV18-E6NOX proteins carry a V to L mutation at amino acid residues
42 and 44, respectively40.
The previously characterized HPV16-E6 mutants defective in p53 binding (Y54D) and
UBE3A/E6AP binding (I128T)41 were generated by site directed mutagenesis (QuikChange®;
Stratagene) of the NTAP-HPV16-E6NOX vector.
PDZ protein binding defective HPV16-E6 and HPV18- E6 mutants were generated by cloning the HPV16-E6NOX and HPV18-E6NOX ORFs into the CTAP vector. This construct fuses HA and FLAG epitope tags to the C-terminal PDZ binding domains and thereby blocks association with cellular PDZ proteins.
Polyomaviruses: ORF clones were obtained from nine polyomaviruses: BK, HPyV6, HPyV7,
JCCY, JCMad1, MCPyV, SV40, TSV and WU. The entire early region of BK was PCR amplified from BKPyV Dunlop genomic DNA cloned into pBR322 (gift from Michael Imperiale and Peter
Howley). Upon quality control we found that the vector encoded a truncated form of Large T protein. The entire early region of HPyV6 was PCR amplified from pHPyV6-607a (Addgene plasmid 24727)42. The entire early region of HPyV7 was PCR amplified from pHPyV7-713a
(Addgene plasmid 24728)42. The entire early region of JCCY was amplified from JCCY genomic
DNA (gift from Igor Koralnik). The entire early region of JCMad1 was amplified from JCMad1
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genomic DNA (gift from Igor Koralnik). The entire early region of TSV was amplified from
pUC19-TSV43. The entire WU early region was amplified from pcDNA WUER (gift from David
Wang). SV40 ORFs encoding Large T antigen (LT)44, Small T antigen (ST)45, Agnoprotein,
17KT, VP1, VP2, and VP3 were PCR amplified from SV40 genomic DNA.
MCPyV-LST was PCR amplified from Addgene plasmid 24729 pMCPyV-R17a containing
the MCPyV R17a genome42. MCPyV ORF clones MCPyV-LT, MCPyV-LTtrunc (which contains
a premature stop codon at position 961-963), and MCPyV-ST were subcloned from a MCPyV-
LST ORF that was first PCR amplified from tumour samples. MCPyV-ST was PCR amplified
directly, whereas MCPyV-LT and MCPyV-LTtrunc were obtained by overlap extension PCR46 to
generate a cDNA for LT (nt 429 to 861 of MCV). MCPyV-SPLT was created similarly but with
MCPyV-LT as template and with a different set of overlapping primers (spanning nucleotides
1622 to 2778 of MCV). LT, ST, and SPLT use the same Gateway attB1-ORF primer and LT and
SPLT use the same Gateway attB2-ORF primer. After two separate PCRs with the relevant
Gateway-ORF primer and internal spanning primer pairs, overlap extension PCR produced the
final PCR product for subsequent BP cloning to generate MCPyV-LT, MCPyV -LTtrunc, and
MCPyV -SPLT entry clones.
B. Yeast two-hybrid (Y2H) assay
Y2H assays used viral ORFs or ORF fragments against the human ORFeome v5.1 collection
consisting of ~15,000 full-length human ORFs10,30 (http://horfdb.dfci.harvard.edu/). Yeast strains
Y8800 and Y893047, of mating type MATa and MATα respectively, harboring the genotype leu2-
3,112 trp1-901 his3Δ200 ura3-52 gal4Δ gal80Δ GAL2::ADE2 GAL1::HIS3@LYS2
GAL7::lacZ@MET2 cyh2R, were transformed together with AD-viORF and DB-viORF
constructs, respectively. For Y2H screening MATa and MATα haploid yeast strains carrying the
corresponding AD-viORFs and DB-viORFs were mated against haploid yeast cells of the
appropriate mating type carrying DB-human hybrid proteins (DB-huORF) or AD-human hybrid
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proteins (AD-huORFs), respectively. All AD-viORF and AD-huORF plasmids also carry the counter-selectable marker CYH2, which allows selection on plates containing cycloheximide
(CHX) of yeast cells that do not contain any AD-Y plasmid9,48. Colonies growing on CHX- containing medium lacking histidine are considered latent autoactivators and the presumptive pair removed from the data set9,48. At each stage of the interactome mapping pipeline, reporter gene activity is evaluated in parallel both on regular selective plates and on CHX-containing plates.
For Y2H screens with viral ORFs as DB-hybrid proteins, the 123 Y8930:DB-viORF yeast strains (Y8930 transformed with a unique DB-viORF) were individually mated against mini- libraries containing 188 different Y8800:AD-HuORF yeast strains, each carrying a unique AD- huORF49. Twenty-seven of the viral ORFs consistently behaved as autoactivators when screened as DB-viORF fusions in yeast grown on selective medium (Supplementary Table 14).
Primary reciprocal screens of ~15,000 DB-huORFs against AD-viORFs used individual
Y8930:DB-huORF strains mated against virus-specific mini-libraries of Y8800:AD-viORF pools.
Primary screens in both orientations were completed twice and all initial positive pairs from the primary screens underwent secondary phenotyping prior to determining the interaction pairs by sequencing11,49,50. Each interacting pair was individually retested from fresh stocks of viral and human yeast strains in both orientations, irrespective of the original Y2H orientation in which the viral-human interactions were initially identified. Autoactivating baits were removed at each step of the primary screening and during retesting.
Viral-human interactions found in multiple primary screens and verified by pair-wise retesting are valid biophysical interactions11,49,50. Multiple pair-wise retesting can demonstrate that interactions are reproducibly reliable even though any given pair may not retest positive each and every time, providing an additional level of confidence in the data set. Besides the pair-wise retesting done at the time of the original screens, we also carried out an additional retest of all verified Y2H interaction pairs followed by resequencing of all DB-X and AD-Y ORFs
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to confirm ORF identity in each interaction pair. The final set of viral-human interactions,
consisting of all sequence-verified pairs that successfully retested in a CHX-sensitive manner,
contains 53 viral ORFs engaged in 454 interactions with 307 human proteins (Supplementary
Table 2). These 454 interactions constitute a set of biophysically real interactions that can be
successfully used for further analysis and downstream investigations.
For screens with HPV proteins, all individual viral-human pairs from each HPV strain were
also retested in both orientations using the orthologous ORFs of all seven HPVs against all
human proteins found by at least one HPV (i.e., a particular huORF found to interact with only
HPV16-E6 was tested against all seven HPV E6 proteins multiple times and in both
orientations). The final data set of HPV-human interactions includes those additional interaction
pairs even if they were not initially found in the primary screens, as long as they did score
positive multiple times in pair-wise retests, in either orientation, without exhibiting growth on
CHX-containing medium and after the identity of the specific HPV protein was confirmed by
sequencing. Virus-host interactions detected by Y2H are available at
http://interactome.dfci.harvard.edu/V_hostome/index.php.
C. Cell culture pipeline
Generating the IMR-90 cell bank: We obtained the human diploid fibroblast cell line IMR-90
(CCL-186) from the American Type Culture Collection (ATCC). IMR-90 cells were cultured in
DMEM (Cellgro) supplemented with 15% FBS (Omega Scientific), 1% Pen Strep (GIBCO), 1%
Glutamax (GIBCO) and 1% MEM NEAA (GIBCO). A master cell bank of third passage cells
consisted of 27 aliquots that were frozen and stored in liquid nitrogen. A single vial of frozen
cells was thawed and used to generate each batch of 26 cell lines in the pipeline
(Supplementary Fig. 16).
Generating IMR-90 cell lines expressing viral ORFs: Recombinant retrovirus was produced
in Phoenix cells by co-transfecting (Lipofectamine LTX, Invitrogen) plasmids encoding the viral
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ORF, GAG/POL and ENV. To produce cell lines expressing each viral ORF, a vial of IMR-90 cells from the bank was thawed and expanded (5.0 x 105 IMR-90 cells were seeded onto 10 cm plate per transduction) to allow simultaneous transduction of 26 individual viral ORFs or controls
(Supplementary Fig. 16), and then infected twice with recombinant retrovirus for 5 – 8 hr in the presence of 5 µg/ml hexadimethrine bromide (Sigma) with subsequent selection with puromycin
(2 µg/ml).
To permit comparisons between different batches of 26 cell lines, each batch included biological replicates of the GFP control and SV40 LT. Five pipeline batches with a total of 87 unique viral ORFs led to the establishment of 75 IMR-90 cell lines expressing unique viral ORFs
(Fig. 1b and Supplementary Table 1).
Quality control (QC): IMR-90 cells expressing viral ORFs were uniformly expanded to generate several vials of cells that were frozen and stored. IMR-90 cell lines expressing viral ORFs were banked at passages 7, 8, and 10. All cell lines had to pass several Quality Control (QC) steps
(Supplementary Fig. 16): semi-quantitative western blot analysis for viral ORF expression using anti-HA antibodies (HA-11 Clone 16B12, Covance), mycoplasma testing (MycoAlert Kit, Lonza), and sequencing of the integrated viral ORF from genomic DNA post transduction. Sixty-four of the 75 cell lines initially established passed QC (Fig. 1b and Supplementary Table 1). Following
QC, cell lines were processed for microarray analysis and tandem affinity purification.
For the sequencing QC step, genomic DNA was extracted from all cell lines (DNeasy Blood and Tissue Kit, Qiagen) and the viral ORFs were amplified with LA Taq (Takara), using the following vector-specific primer sets:
MSCV-N and MSCV-C Fwd primer: 5’-CGCATGGACACCCAGACCAGGTC-3’
MSCV-N Rev primer: 5’-TCACGACATTCAACAGACCTTGC-3’
MSCV-C Rev primer: 5’-GTCGGGCACGTCGTAGGG-3’
PCR products were extracted from the gel (QIAquick Gel Extraction Kit, Qiagen) and sequenced with the following primers:
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MSCV-N and MSCV-C Fwd primer: 5’-CCCTTGAACCTCCTCGTTCGACC-3’
MSCV-N Rev primer: 5’-GCCAAAAGACGGCAATATGGTGG-3’
RNA isolation and microarray analysis: IMR-90 cell lines expressing viral ORFs at passage 9
were seeded into five replicates at a density of 1 x 106 cells per 10-cm plates and harvested
after 48 hr in RNAlater (Ambion). After isolation of total RNA (RNeasy, Qiagen) RNA integrity
was determined using a Bioanalyzer (Agilent). Gene expression was assayed using Human
Gene 1.0 ST arrays (Affymetrix) according to standard protocols.
Cell proliferation and senescence assays: We measured the growth rate of a subset of IMR-
90 lines expressing the viral ORFs HPV5-E6, HPV8-E6, MCPyV-LTTRUNC, SV40-ST, MCPyV-
ST, HPV6b-E6, HPV18-E6X, C-GFP, N-GFP (two biological replicates), EBV-BGLF3, HPV18-
E6NOX, HPV16-E6, and SV40-LT (two biological replicates) between passages 9 and 12. Cells
were seeded in triplicate onto six 12-well plates (day 0; 2 x 104 cells per well) and cell density
was measured by crystal violet assay on days 1, 4, 7, 9, 11, and 14. All cell density values were
normalized to the value measured at day 1. For senescence assays, IMR-90 cells expressing
viral ORFs between passages 10 and 12 were seeded in triplicate onto 6-well plates and
subjected after 48 hr to a SA-b-gal Senescence Colorimetric Assay (Sigma). Stained cells were
overlaid with 50% glycerol and photographed at 10X magnification under a Nikon Eclipse E300
microscope with a Spot digital camera (Diagnostic Instruments, Inc.). Three non-overlapping
fields were photographed. The number of SA-b-gal positive cells was determined by
examination of the digital images51.
D. HPV E6 oncoproteins and Notch signalling
The U-2 OS cells used in several of the Notch signalling experiments were cultured in DMEM
(Invitrogen) supplemented with 10% FBS (Gemini Bio-products), and 1% Pen Strep (Invitrogen).
Generating pNCMV expression vectors encoding HPV E6 proteins: PCR amplification
products encoding HPV E6 proteins were amplified using B-Actin HPV E6 expression vectors as
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a template52. PCR amplification products with a 5’ BamHI and a 3’ BglII restriction enzyme cleavage sites were directionally cloned into the BamHI site of the pNCMV vector52,53, resulting in an in-frame fusion of the FLAG and HA epitope tags at the N-terminus of the E6 coding sequence. This approach was used for all HPV E6 proteins except for HPV18 E6 which has an internal BamHI site, and was cloned into the pNCMV vector using PCR amplification products with BglII restriction enzyme cleavage site at both the 5’ and 3’. All constructs were sequence validated.
Transfection, cell lysates, immunoprecipitation, immunoblotting, and antibodies: U-2 OS cells were seeded onto 15 cm plates (2 x 106) and transfected with 12.5 micrograms of a control pNCMV vector or of a pNCMV vector encoding the indicated HPV E6 proteins using
XtremeGENE 9 (Roche). Cells were harvested 48 hours post transfection. Both IMR-90 cell lines expressing viral ORFs and transfected U-2 OS cells were lysed in EBC buffer54 (50 mM
Tris HCl, pH 8.5, 150 mM NaCl, 0.5% NP-40, 0.5 mM EDTA, proteinase and phosphatase inhibitors). Extracts were cleared at 14,000 rpm for 15 min. For immunoprecipitations, equal amounts of cell extracts were incubated for 4 hr at 4°C with 30 µl of anti-HA agarose (Sigma).
The beads were washed four times with EBC buffer and resuspended in sample loading buffer
(Bio-Rad). Proteins were resolved by SDS-PAGE (CriterionTM TGXTM precast gels, Bio-Rad) and subjected to immunoblot analysis with one of the following commercially available antibodies: anti-p300 (A-300-358A, Bethyl Laboratories), anti-MAML1 (4608S, Cell Signaling Technology or
A-300-672A, Bethyl Laboratories), anti-E6AP (H-182, sc-25509, Santa-Cruz Biotechnology), anti-vinculin (V9131, Sigma), and anti-HA (HA-11 Clone 16B12, Covance). After incubation with the appropriate secondary antibodies, antigen/antibody complexes were detected by enhanced chemiluminescence (SuperSignal, Pierce).
Reverse transcription-quantitative PCR (RT-qPCR) analyses: Total RNA isolated from IMR-
90 cells expressing viral ORFs and total RNA isolated (RNeasy, Qiagen) from IMR-90 cells expressing MAML1 shRNA or control was converted to cDNA with a QuantiTect Reverse
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Transcription Kit (Qiagen). This cDNA preparation was diluted five-fold, and 1 µl was used per
reaction. Real-time PCR quantification, was done in triplicate, using the Brilliant III Ultra Fast
SYBR Green QPCR Master Mix (Agilent technologies), and a Stratagene MX3005 instrument.
The qPCR primer pairs for the human genes HES1 (HP208643), DLL4 (HP213393), and
MAML1 (HP211129) were obtained from OriGene. GAPDH (HP205798) was used as the
(- Ct) 55 internal reference standard. We used the 2 ΔΔ method to quantify transcript levels.
RNA interference: For lentivirus-mediated RNA interference, the lentiviral construct targeting
MAML1 (SHCLNG-NM_014757, TRCN0000003353, Sigma) [5’-
CCGGCATGATACAGTTAAGAGGAATCTCGAGATTCCTCTTAACTGTATCATGTTTTT-3’] or
the control empty vector pLKO.1ps (gift of William Hahn) were transfected into HEK293FT cells
according to published protocols56. Puromycin selection (2 µg/ml) began two days after infecting
IMR-90 cells with lentiviruses and was maintained thereafter. IMR-90 cells transfected at
passage 9 with MAML1 shRNA or control pLKO.1ps vectors were seeded (1 x 106) into five
replicates (5 x 10-cm plates) and harvested after 48 hr in RNAlater (Ambion). After isolation of
total RNA (RNeasy, Qiagen) RNA integrity was determined using a Bioanalyzer (Agilent). Gene
expression was assayed using Human Gene 1.0 ST arrays. For siRNA-mediated RNA
interference ON-TARGETplus SMARTpool siRNA (Thermo Scientific) targeting MAML1 (L-
013417-00; sequences below), or control ON-TARGETplus Non-targeting siRNA (D-001810-10-
20; sequences below) were transfected into cells using Lipofectamine RNAiMAX (Invitrogen).
Cells were harvested 72 hours post-transfection.
siRNA J-013417-06, MAML1 GUUAGGCUCUCCACAAGUG siRNA J-013417-07, MAML1 GGCAUAACCCAGAUAGUUG siRNA J-013417-08, MAML1 GCAGCUGUCCAUAUAAGU siRNA J-013417-09, MAML1 UCGAAGACCUGCCUUGCAU siRNA D-001810-01, non-target UGGUUUACAUGUCGACUAA siRNA D-001810-02, non-target UGGUUUACAUGUUGUGUGA siRNA D-001810-03, non-target UGGUUUACAUGUUUUCUGA siRNA D-001810-04, non-target UGGUUUACAUGUUUUCCUA
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SUPPLEMENTARY INFORMATION RESEARCH
Luciferase Reporter Assays: U-2 OS cells were seeded in triplicate onto 6-well plates (2 x 105 cells per well). Each well was transfected with 10 nanograms of pGK Renilla, 0.5 micrograms of pGL2 mHes1-Luc, 0.25 microgram of pcDNA3 2HA-ICN1 (gift of Elliot Kieff) and increasing concentrations (25, 50, and 100 nanograms) of the NCMV vector control or NCMV encoding the indicated HPV E6 proteins using X-tremeGENE 9 (Roche). The total amount of DNA per transfection was brought up to 2 microgram per well by adding salmon sperm DNA. After 48 hours cells were lysed with Passive Lysis Buffer (Promega), and subjected to the Dual-
Luciferase Reporter Assay (Promega). Luciferase activity was measured with a LMax II384 microplate reader (Molecular Devices), and the Renilla luciferase was used as the internal reference standard.
E. Tandem Affinity Purifications (TAP) followed by Mass Spectrometry (MS)
IMR-90 fractionation and protein extract preparation: For each viral ORF tested the corresponding IMR-90 cells were seeded into twelve to eighteen 15 cm dishes to produce ~2 to
4 x 108 cells by harvesting time. Cells were washed in situ with ice-cold PBS, harvested using a cell lifter and collected by centrifugation at 500 x g for 5 min at 4°C. Subcellular fractions were obtained by differential digitonin fractionation57 with the following modifications. To prepare nuclear and cytoplasmic fractions, cell pellets were resuspended in five pellet volumes of ice- cold digitonin extraction buffer (0.015% digitonin, 300 mM sucrose, 100 mM NaCl, 10 mM
PIPES, 3 mM MgCl2, pH 6.8) containing protease inhibitors (Roche) and then incubated end- over-end for 10 min at 4°C. Triton X-100 was added to a final concentration of 0.5% and the cell suspension was vortexed for 10 seconds. Triton X-100 extracted cells were then divided equally between three tubes and centrifuged at 1000 x g for 10 min at 4°C. The supernatants (crude cytoplasmic fraction) were transferred to fresh tubes apart from the nuclear pellets. Both fractions were flash frozen in liquid nitrogen and stored at -80°C. To prepare nuclear and
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membrane fractions, cell pellets were resuspended in five pellet volumes of ice-cold digitonin
extraction buffer and then incubated end-over-end for 10 min at 4°C. The cell suspension was
centrifuged at 500 x g for 10 min at 4°C. The resulting pellet was resuspended in five pellet
volumes of Triton X-100 extraction buffer (0.05% Triton X-100, 300 mM sucrose, 100 mM NaCl,
10 mM PIPES, 3 mM MgCl2, pH 7.4) containing protease inhibitors and then incubated end-
over-end for 30 min at 4°C. The suspension was divided equally into three tubes and
centrifuged at 5000 x g for 10 min at 4°C. The supernatant (crude membrane fraction) was
transferred to fresh tubes and flash frozen in liquid nitrogen, apart from the nuclear pellets,
which were also flash frozen. Before affinity purification the composition of the cytoplasmic and
membrane fractions was adjusted to 150 mM NaCl, 50 mM Tris pH 7.5. The composition of the
cytoplasmic fraction was additionally adjusted by adding NP40 to a final concentration of 0.05%.
TAP of viral protein complexes: Viral proteins were purified from 6 to 9 plate-equivalents of
nuclear and cytoplasmic (or nuclear and membrane) fractions by sequential FLAG and HA
immunoprecipitation12,58. Tandem affinity purifications were typically done in batches of 2 to 6
cell lines expressing viral ORFs and included empty vectors or GFP controls cells. For FLAG
purification 40 ml of FLAG agarose bead slurry (FLAG-M2 agarose, Sigma) was added to
cellular extracts, which were then incubated for 4 hr at 4°C under constant end-over-end mixing.
The FLAG-agarose beads were collected by low speed centrifugation and washed three times
with 500 ml of cold FLAG IP buffer (150 mM NaCl, 50 mM Tris, 1 mM EDTA, 0.5% NP40, 10%
glycerol and protease inhibitors) and once with 500 ml of HA-IP buffer (150 mM NaCl, 50 mM
Tris, 1 mM EDTA, 0.05% NP40, 10% glycerol and protease inhibitors). FLAG-purified
complexes were recovered by two successive 30 min elutions with 20 ml FLAG elution buffer
(400 µg /ml of FLAG peptide in HA IP buffer) at 4°C under constant vortexing. To prevent FLAG
agarose bead carry-over, pooled eluates were filtered through a pre-wetted 0.45 mm filter
before the subsequent HA purification step. For HA purification 20 ml of HA agarose bead slurry
(HA-F7 agarose conjugate, Santa Cruz) was added to cellular fractions, which were then
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incubated overnight at 4°C under constant vortexing. HA agarose beads were collected by low speed centrifugation and washed three times with 200 ml of cold HA-IP buffer. HA-purified complexes were recovered by two successive 30 min elutions in 20 ml HA elution buffer (400 µg
/ml of 6xHis-HA peptide in HA IP buffer) at 4°C under constant vortexing. To prevent HA agarose bead carry-over, pooled eluates were filtered through a pre-wetted 0.45 mm filter before analysis. A 15% aliquot of each HA eluate was resolved on 4-12% acrylamide gels run in
MOPS buffer (NuPAGE, Invitrogen), and viral and associated host proteins were visualised by silver-stain (Supplementary Fig. 17).
Biological replicates: We repeated the tandem affinity purification process for 144 (95%) of the initial 152 sub-cellular fractions used for the first iteration (TAP1). This biological repeat (TAP2) was initiated several months after the completion of TAP1.
Sample digestion of viral-protein multi-component complexes: Purified viral proteins and associated host proteins were denatured and reduced by incubation at 56°C for 30 min in 10 mM DTT and 0.1% RapiGest (Waters). Reduced cysteines were alkylated by adding iodoacetamide to a final concentration of 20 mM and then incubated at room temperature for 20 min in the dark. Protein digestion was carried out overnight at 37°C after adding 2 mg of trypsin and adjusting the pH to 8.0 with a 1 M Tris solution. For tryptic peptide clean-up RapiGest was inactivated following the protocol of the manufacturer. Tryptic peptides were purified by batch- mode reverse-phase C18 chromatography (Poros 10R2, Applied Biosystems) using 40 ml of a
50% bead slurry in RP buffer A (0.1% trifluoroacetic acid), washed with 100 ml of the same buffer and eluted with 50 ml of RP buffer B (40% acetonitrile in 0.1% trifluoroacetic acid). After vacuum concentration, peptides were further purified by strong cation exchange SCX chromatography (Poros 20HS, Applied Biosystems) using 20 ml of a 50% bead slurry in SCX buffer A (25% ACN in 0.1% formic acid), washed with 20 ml of the same buffer and eluted with
20 ml of SCX buffer B (25% ACN, 300 mM KCl in 0.1% formic acid).
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Primary LC-MS/MS data acquisition: After vacuum concentration, half of each sample was
loaded onto a pre-column (100 µm I.D. x 4 cm; packed with POROS 10R2, Applied Biosystems)
at a flow rate of 4 µl/min for 15 min, using a NanoAcquity Sample Manager (20 µl sample loop)
and UPLC pump59. After loading, the peptides were gradient eluted (1-30% B in 45 min; buffer
A: 0.2 M acetic acid, buffer B: 0.2 M acetic acid in acetonitrile) at a flow rate of ~50 nl/min to an
analytical column (30 µm I.D. x 12 cm; packed with Monitor 5 µm C18 from Column
Engineering, Ontario, CA), and introduced into an LTQ-Orbitrap XL mass spectrometer
(ThermoFisher Scientific) by electrospray ionization (spray voltage = 2200V). Three rapid LC
gradients were included at the end of every analysis to minimize peptide carry-over between
successive analyses and to re-equilibrate the columns. The mass spectrometer was
programmed to operate in data dependent mode, such that the top eight most abundant
precursors in each MS scan (detected in the Orbitrap mass analyzer, resolution = 60,000) were
subjected to MS/MS resolution (CAD, linear ion trap detection, collision energy = 35%,
precursor isolation width = 2.8 Da, threshold = 20,000). Dynamic exclusion was enabled with a
repeat count of 1 and a repeat duration of 30 sec. The second half of each sample was
reanalyzed by LC-MS/MS after the completion of the initial set of analyses (“Technical
Replicates”).
Database searching: Orbitrap raw data files were processed within the multiplierz software
60 environment . MS spectra were recalibrated using the background ion (Si(CH3)2O)6 at m/z
445.12 +/- 0.03 and converted into a Mascot generic format (.mgf). MS spectra were searched
using Mascot version 2.3 against four appended databases of: i) human protein sequences
(downloaded from RefSeq on 07/11/2011); ii) viral proteins analyzed in this study; iii) common
lab contaminants and iv) a decoy database generated by reversing the sequences from the
human database. Search parameters specified a precursor ion mass tolerance of 1 Da, a
product ion mass tolerance of 0.6 Da, trypsin with a maximum of two missed cleavages,
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variable oxidation of methionine (M, +16 Da), and carbamidomethylation of cysteines (C, +57
Da).
Mascot search results from all experimental runs were separately imported for TAP1 and
TAP2 into two multiplierz mzResults files (.mzd)61 for further processing: The list of peptide hits from the Mascot searches was filtered to exclude peptides with precursor mass error greater than 5 ppm. Sequence matches to the decoy database were used to establish a 1% false discovery rate (FDR) filter for the resulting peptide identifications. A rapid peptide matching algorithm62 was then used to map peptide sequences to all possible human Entrez Gene IDs.
Only peptides that were uniquely assignable to a single Entrez Gene ID were considered as detection evidence. For each viral-host protein association, peptide evidences for a given target human protein were combined across different subcellular fractions and LC-MS/MS technical replicates, when applicable. Any Entrez Gene ID present in the “Tandome” (Supplementary
Table 15) was automatically removed from the list of putative interactors.
TAP-LC-MS quality controls: We used a multi-tiered approach to minimize the occurrence of peptide carryover across LC-MS analyses: 1) samples within each batch of LC-MS analyses were injected in order of increasing abundance and complexity, as evaluated by SDS-
PAGE/silver stain visualisation of the corresponding TAPs; 2) three rapid LC gradients were run at the end of every LC-MS analysis; 3) for all LC-MS analyses, we systematically evaluated run- to-run carry-over by calculating extracted ion chromatogram (XIC) intensities of peptides derived from viral proteins. 4) samples within each purification batch, and LC-MS analysis sequence were randomized between biological replicates (TAP1 and TAP2). This multi-step quality control process led us to discard all LC-MS data for EBV-BFRF2 and EBV-BRRF2 from our TAP2 (and as a consequence from the final data set, since we only report proteins identified reproducibly across TAP1 and TAP2), and for two GFP negative controls. Because we identified cross contamination between HPV8 E6 and HPV11 E6 at an early point of our project, we were able to repeat the TAP and LC-MS analyses to generate a “clean” data set for bio-replicates.
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Pathway visualisation and analysis: Protein associations detected between HPV-E6 and
HPV-E7 oncoproteins and host proteins were imported into Pathway Palette for data analysis
and visualisation63. For each of these data sets, we first calculated the probability that the
observed set of human proteins targeted by multiple E6 or E7 across different HPV types
(HPV5, HPV6b, HPV8, HPV11, HPV16 and HPV18) could occur by chance (Fig. 1d and
Supplementary Fig. 4): for each viral protein of a given HPV type, the same number of
associated human proteins as that experimentally observed in our data was randomly chosen
from our HI-2 database of human binary interactions
(http://interactome.dfci.harvard.edu/H_sapiens/index.php). This process was repeated 1,000
times. The number of human proteins associated with two or more viral proteins from different
HPV types was calculated in each simulated network and the frequency distribution of random
graphs with a given number of shared nodes was computed. Lastly, this distribution was used to
calculate the probability that the number of human proteins targeted by multiple HPV types
observed in our experimental graphs (40 for E6 and 25 for E7) could occur by chance. We also
calculated the probability of detecting human proteins targeted by two viral proteins from HPVs
of the same class: the total number of human proteins targeted by viral proteins from pairs of
HPVs of the same class ([HPV5 and HPV8], [HPV6b and HPV11], [HPV16 and HPV18]) was
extracted from our TAP-MS data. This value was divided by the total number of human proteins
that could theoretically be shared across all pairs of HPV from the same class in our observed
networks. The same ratio was calculated for human proteins targeted by two viral proteins from
HPVs of different classes. The ratio of these two ratios (4.9 for E6 and 1.05 for E7, indicated by
green arrows in the frequency distribution on the right) represents the overall enrichment of
human proteins targeted by proteins encoded by HPVs of the same versus different class
observed in our graphs. One thousand randomized networks were created as before and used
to evaluate the probability that the observed ratio could occur by chance (Fig. 1d and
Supplementary Fig. 4). Virus-host interactions detected by TAP-MS are available at
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http://interactome.dfci.harvard.edu/V_hostome/index.php?page=home. Graphs of protein interaction networks for each viral protein, including the silver stain image of a representative tandem affinity purification, are accessible at: http://blaispathways.dfci.harvard.edu/Palette.html.
F. Subtracting likely non-specific protein associations (“Tandome”)
Twenty five control TAP experiments were run at multiple points during the entire project. These controls consisted of FLAG-HA purifications from nuclear, cytoplasmic or membrane extracts prepared from parental IMR-90 cells, from IMR-90 expressing FLAG-HA-tagged GFP or from
IMR-90 cells transduced with the empty NTAP or CTAP vectors. An additional 83 control TAP experiments consisted of FLAG-HA or FLAG-StrepTactin purifications on HeLa subcellular extracts. Any Entrez Gene ID identified in more than 1% of the control TAP experiments
(whether or not the Gene ID was detected by a uniquely assignable peptide sequence) was included in the “Tandome” list. Any of the 679 Gene IDs present in the Tandome was systematically removed from all viral protein interaction TAP data sets (Supplementary Table
15).
G. TAP reproducibility
The reproducibility rate of TAP-MS across the two biological replicates (TAP1 and TAP2) corresponds to the percentage of viral-host protein associations detected in one TAP (the
“reference TAP”) at a given uniquely assignable peptide threshold, which was confirmed in the other (the “repeat TAP”). This percentage was calculated for both permutations in which the
“reference TAP” and the “repeat TAP” were successively (TAP1 and TAP2) and (TAP2 and
TAP1), respectively. The average reproducibility rate was also calculated. As expected, reproducibility increased as a function of the number of unique peptides detected
(Supplementary Fig. 13).
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H. Virus-human Positive Reference Set (PRS)
The VirusMINT database curates from the literature viral-host interactions for 10 disease-
relevant virus groups64. Each curated interaction gets an evidence count, where multiple
evidences could be multiple publications, or multiple methodological verifications, reporting the
same interaction. To generate a Positive Reference Set (PRS) of high confidence interactions,
we only collected interactions supported by two or more evidences in VirusMINT, resulting in a
master list of 134 interactions. Given the different types of associations detected by Y2H and
TAP-MS, a specific PRS was designed for each one of these data sets (Supplementary Table
16). For the Y2H-specific PRS, we first selected host proteins with an available ORF clone in
the human ORFeome 5.1 collection (http://horfdb.dfci.harvard.edu/), resulting in a list of 94 viral-
host protein interactions. This list was filtered to remove interactions involving known
autoactivators in the yeast two-hybrid assay (Supplementary Table 14) and interactions that
were not tested in the yeast two-hybrid screen. The resulting Y2H-specific PRS contains 62
interactions. Starting with the master list of 135 interactions, the TAP-specific PRS was created
by removing interactions that involved viruses that were not tested in the TAP-MS assay and
interactions that involved a human protein present in our “Tandome” (Supplementary Table 15),
resulting in a TAP-specific PRS of 94 interactions (Supplementary Table 16).
I. Pathway enrichment analysis
Pathway enrichment used FuncAssociate 2.0 (http://llama.mshri.on.ca/funcassociate/)65, which
performs a Fisher’s Exact Test and compares the observed P-value for the query set to the
minimum P-value obtained from Monte Carlo sampling of the appropriate gene sample space.
When the query set of genes arose from multiple sample spaces, a Python script of the
FuncAssociate approach was used to independently sample from each space so that the
random query sets matched the original set in size and composition. KEGG pathway
annotations were downloaded from http://www.genome.jp/kegg/download/. Gene Ontology
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annotations were downloaded from the FuncAssociate 2.0 website. For all analyses we considered only specific pathways and functional terms, defined here as those which have been annotated with fewer than 1000 genes. All P-values were adjusted for testing of all pathways in the GOA or KEGG databases (Supplementary Table 17), including the reported P-values for apoptosis pathway members. Additionally, pathway enrichment of TAP-MS data sets were assessed at unique peptide thresholds ranging from 1 to 5, with the resulting P-values corrected for the five hypotheses tested, using the Bonferroni method. Odds ratios were calculated using
2x2 contingency tables.
J. Microarray preprocessing and differential expression
Microarray data analysis used R/Bioconductor (http://www.bioconductor.org). Data was first normalized by robust multiarray averaging (RMA) using a custom Chip Description File (CDF) from the Michigan Microarray Lab (http://brainarray.mbni.med.umich.edu, Version 13). The
Human Gene 1.0 ST array from Affymetrix contains 764,885 probes, with 26 probes located across the full length of each annotated gene from the March 2006 human genome sequence assembly. The Michigan Microarray Lab CDF reanalyzes and matches each probe sequence to the most current gene annotation, producing summarized expression values for 19,628 genes with Entrez Gene IDs. Using this custom CDF rather than the original Affymetrix annotation ensured a more accurate estimate of gene expression levels66.
A total of 435 microarrays were used to profile cell lines expressing 63 viral ORFs, as well as controls. The arrays were processed in eight groups (Supplementary Table 18). Note that each group includes cell lines expressing proteins from a variety of viruses, five of the groups include control cell lines, and the order of the arrays was randomized. These preemptive measures were taken in order to eliminate potential systematic biases that could affect our results and interpretation. The normalized data showed a slight propensity to cluster according to the batch covariate (Supplementary Fig. 18). We used ComBat67 to reduce this batch effect.
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ComBat applies a linear model to the expression levels, with factors corresponding to global
expression, sample-dependent expression, additive/multiplicative batch effects and noise. It
then synthesizes information across genes using an empirical Bayes framework, and thus
estimates and removes batch effects (Supplementary Fig. 18). The R package limma was used
to test for differential expression between each of the 63 conditions and GFP-control arrays.
Multiple testing adjustments were carried out with the Benjamini-Hochberg method.
K. Microarray gene and sample clustering
We used the R package limma to test for differential expression across all pairwise comparisons
between biologically distinct samples, including the control. The top 2,944 genes were selected
by ranking all genes on the array by the number of significant comparisons (Padj < 0.005;
Supplementary Table 19). This transcriptome profiling data is available at
http://interactome.dfci.harvard.edu/V_hostome/index.php?page=home. We applied the R
package mclust to carry out model-based clustering on the top 2,944 genes. We used the hmap
function in the seriation R package to generate the heatmap representation of mean fold-
change of expression of genes within each cluster.
L. Predicting cell-specific transcription factor binding sites
Position weight matrices (PWMs) corresponding to mammalian transcription factors were
obtained from the TRANSFAC 7.0, Jaspar, and UniProbe databases. We supplemented
available PWMs by downloading experimentally determined genome-wide transcription factor
binding sites from the GEO and the UCSC genome databases. We conducted de novo motif
discovery using the MEME 4.3 and Weeder 1.4.2 software tools. We then mapped motifs to
genes using either the keys provided by the databases or based on the antibody used for the
ChIP experiment. A total of 610 genes were identified for which at least one PWM was
available. Of these 361 had at least one “high-quality” PWM, defined as being derived from
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either a ChIP-seq or ChIP-chip experiment or from a universal protein binding microarray
(PBM).
Database Number of PWMs Transfac: http://www.biobase-international.com/ 709 Jaspar: http://jaspar.genereg.net/ 721 Uniprobe (not in Jaspar): http://the_brain.bwh.harvard.edu/uniprobe/ 26 UCSC: http://genome.ucsc.edu/ 67 or GEO: http://www.ncbi.nlm.nih.gov/geo/
Clustering of position weight matrices: Our identification of regulatory variants depends on correctly mapping genes to motifs, although our integrative approach can tolerate some errors.
We took a computational approach to improving the quality of the gene to motif mapping. For genes for which a “high-quality PWM” was available, we eliminated any other PWM that showed no resemblance. To do so systematically, we hierarchically clustered all PWMs. First 100,000 random 30-mers were generated. Then for each of the ~1500 PWMs, the maximum match score (MMS) for overlap between the motif and 30-mer was generated by comparing the observed frequency fbj of base b at each position j in the PWM with the background frequency of that base pb in the genome and summing over the length of the motif:
The matrix of 30-mer x PWM was hierarchically clustered using the hclust function in R, with average linkage and the Pearson correlation coefficient used as the metric for similarity. A coarse clustering cutpoint (height = 0.85, resulting in 232 total PWM clusters) was used to broadly group together PWMs with some evidence of similarity. For genes having at least one high quality PWM, we eliminated all PWMs that failed to cluster with any of these (although we retained PWMs that represented dimers of the high quality motif). This approach rejected 113 gene-PWM pairs.
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Selection of motifs for genome-wide binding site prediction and enrichment analysis: We
selected individual motifs for subsequent analysis with the goal of providing at least one high
quality motif for every gene. For each of the 610 genes we selected all motifs derived from a
ChIP-seq, ChIP-chip or protein binding microarray experiment. If any other motif existed (i.e.
from Transfac or Jaspar), we included it if it appeared to correspond to a dimer or half-site not
represented in the otherwise selected high-quality motifs. All motifs were selected prior to
further analysis for genes without any high-quality PWM. A total of 841 motifs corresponding to
573 TFs satisfied the above criteria and the minimum match score of 12.
M. Transcription factor binding site (TFBS) enrichment analysis
IMR-90-specific TFBS were predicted by identifying binding sites with an MMS≥12 within
accessible chromatin regions. Accessible chromatin regions were identified using IMR-90-
specific genome-wide DNase-seq data downloaded in wig file format from the GEO database
(GSM530665, GSM530666). Wig files were processed using the BEADS packages68 to correct
for systematic biases in GC-content and ability to map reads. Corrected counts were smoothed
and binned into 250 bp groups. Bins with a significant number of counts were identified by
assuming that such counts follow a Poisson distribution. The Benjamini-Hochberg procedure
was used to estimate false discovery rates and contiguous bins with FDR < 0.001 were merged
into peaks. Only peaks observed within both replicate IMR-90 data sets were used for
subsequent analyses.
The enrichment of TFBS within a given expression cluster was computed for each TF by
counting the number of TFBS in DNase accessible regions located within 25 kb of the
transcription start site of any transcript within this cluster and comparing the count to a null
distribution of TFBS from length- and GC-content matched sets of mappable genomic regions.
For each cluster, nearby DNase-seq peaks were assembled into a single file and the peaks
trimmed to common lengths of 250, 500, 750, 1000, 1250, 1500, 1750, 2000, 2250 or 2500 bp.
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Next, 200 sets of length-matched “control” DNase-accessible segments were sampled from the random genome segments. For each motif, the number of TFBS for each set of 200 sequences was computed, and the values fitted to a Poisson distribution. This null distribution was used to estimate the P-value for the actual number of TFBS within IMR-90 cells. The enrichment P- values for all TF motifs within each cluster were combined and a tail-based false discovery rate was estimated using the Benjamini-Hochberg procedure69. TFs with motifs significant at an FDR of 0.5% or less were selected for further analysis. In total, we found 239 TFs to be enriched for target genes in one or more clusters.
N. Randomization of gene clusters for enrichment analyses
To assess the biological coherence of identified gene clusters (Supplementary Tables 5 and 6) we computed the random expectation of enriched KEGG pathways and GO terms. For 100 iterations, we randomly reassigned the 2,944 genes to 31 clusters matched in size to those observed and repeated the enrichment analysis, counting at each iteration the average number of significant KEGG pathways and GO terms per cluster. The computationally more intensive
GO term enrichment was performed by counting the average number of nominally significant pathways (P < 0.05) per cluster. We found that there were more GO and KEGG annotations than expected by chance (P < 0.01), underscoring the functional coherence of these clusters
(Supplementary Fig. 19).
O. Evaluating predictive power of regulatory cascades
We computed scores for the viral ORF-TF-cluster network in the following way: for each viral
ORF, we first identified all genes for which we would expect differential expression. To do so, we identified all TFs that had a physical association with the protein encoded by the viral ORF or was differentially expressed in response to its expression, considering fold changes greater than 1.2 at a Padj < 0.001. For each of these TFs, we then identified all microarray clusters enriched for genes containing the corresponding binding site within their promoters. Within
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these clusters, all genes with a high-probability binding site for that TF in their promoter were
selected. The union of these genes across all TFs targeted by a given viral ORF represents the
set of genes that would be expected to show differential expression upon transduction of this
viral ORF. We chose as the meaningful statistic the fraction of these target genes that were
observed to be differentially expressed on the corresponding array (i.e. where that viral ORF
was transduced) and then computed the average over all viral ORFs. A null distribution was
created by computing the same score for 1,000 random permutations of the 63 array conditions
(Fig. 2b). We used Cytoscape 2.8.1 to visualise the network70. The resulting network
(Supplementary Fig. 9) illustrates how viral proteins may regulate DNA damage response, as
well as core functions of fibroblasts, like proliferation and cell adhesion via multiple TFs.
P. Building a probabilistic map of IMR-90 specific RBPJ binding sites
To build a map of IMR-90 specific TF binding sites, we followed a previously described
probabilistic framework71. The approach combines PWM information with complementary
genomic and experimental data in a mixture model, and outputs a posterior probability of DNA
binding. Features of interest include chromatin accessibility (as obtained through genome-wide
DNase-seq number or histone mark ChIP-seq experiments), multi-species sequence
conservation, and distance from the nearest gene transcription start site.
We downloaded publicly available data from diverse sites. FASTA files corresponding to the
primary sequence of the complete human genome (hg19) were downloaded from the UCSC
FTP site. IMR-90-specific chromatin accessibility data was generated as in “Transcription factor
binding site (TFBS) enrichment analysis”. We collected individual base conservation information
from the phastCon tables in the UCSC genome browser. To identify transcription start sites for
all RefSeq IDs we downloaded the refGene.txt file from the UCSC Genome Browser FTP site.
Using the data obtained for each of the RBPJ PWM, we proceeded as follows:
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1. Match score: the complete genome was scanned to compute a match score at each
base pair. All matches with a score ≥ 12 (an arbitrary threshold for similarity) were
included as potential binding sites.
2. Chromatin accessibility score: we defined a 200 bp “chromatin accessibility window”
around each potential binding site from (1) by assigning the total number of DNase-seq
reads in the 100 bp upstream and downstream of the motif starting position.
3. Nearest transcription start site: we identified the nearest transcription start site to each of
the potential binding sites.
4. Conservation score: we averaged the phastCon conservation score over the length of
the motif and assigned this average score to each potential binding site.
We then used the CENTIPEDE R package71 to infer an IMR-90-specific probability of RBPJ binding at each position in the genome.
To generate a short list of Notch pathway transcriptional targets, we took the union of Notch pathway members (as defined in KEGG) and potential Notch target genes72. From this set, we chose the genes containing an IMR-90-specific high probability RBPJ binding site in their promoters to generate the heatmap in Fig. 3b.
Q. Notch pathway enrichment analysis
Enrichment of viral protein targets for Notch pathway members compared the number of protein complex associations or direct interactions between viral proteins and Notch pathway members
(as defined by KEGG Pathways) to the number of interactions selected at random for each viral protein. The sampling methods have been defined in “HI-2 and analysis of overlaps between
Y2H, TAP-MS and PRS”.
R. Identification of loci implicated in familial and somatic cancer
The COSMIC Classic list of causal genes with somatic mutations observed in tumours was downloaded from http://www.sanger.ac.uk/genetics/CGP/Classic/. To assemble the genome-
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wide association loci associated with cancer we consulted the publicly available Catalog of
Published Genome-Wide Association Studies (http://www.genome.gov/gwastudies/) and
selected all variants associated with cancer. We grouped variants showing evidence of linkage
disequilibrium and, for the resulting 107 loci, selected as the index SNP the most highly
associated variant for any phenotype.
We next mapped SNPs to neighboring genes, a task for which there is no consensus
procedure73. We first defined the boundaries in which SNPs tagged by the index variant may
reside. To do so, we identified all SNPs that were in close linkage disequilibrium (R2 ≥ 0.5) with
the index SNP. We then marched outward from the outermost SNP positions to the nearest
recombination hotspot74 (downloaded from the UCSC genome browser). We assumed that any
SNP within the region bounded by the two recombination hot spots may be a causal variant
responsible for the original association. Since variants within enhancers can act at considerable
distance from their physical position, we also considered all genes within 250 kb from the
hotspot boundaries. When no genes resided within these boundaries, we moved outwards in 50
Kb intervals until a gene was identified (Supplementary Table 20).
To compile SCNA loci implicated in somatic forms of cancer we consulted a recent
compendium27 of loci derived from SNP arrays of over 3,000 tumour samples. Physical
boundaries for the SCNAs were used to map each locus to genes contained fully within the
SCNA interval (Supplementary Table 21).
S. Testing enrichment of gene sets for cancer genes
We followed the simple hypothesis that the viral interactome is enriched in cancer genes and
looked for overlap between viral interaction partners and cancer genes highlighted in the
COSMIC Classic list (Supplementary Table 8). The number of overlapping genes was
compared to that obtained from 10,000 random gene sets matched in size and composition to
the query set.
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T. Viral target overlap with candidate cancer genes identified by transposon screens
We identified four “Sleeping Beauty” transposon-based murine insertional mutagenesis screens focused on finding genetic determinants of tumorigenesis in murine cancer models. These studies correspond to murine models of colon cancer with75,76, or without77 mutations of the Apc tumour suppressor, and pancreatic carcinoma78. The list of candidate cancer genes was extracted from the main text or supplementary data of each manuscript and pooled to form a single set of 1,359 genes (Supplementary Table 10). As expected, the candidate genes identified through insertional mutagenesis exhibited a marked bias towards long genes. We also noted that genes in this candidate set tend to be highly expressed based on our IMR90 microarray data.
We assessed the overlap between the Sleeping Beauty candidate genes and the VirHost set by permutation. The analysis was limited to the universe of human genes with mouse orthologues (downloaded from the Mouse Genome Database). We corrected for the bias observed in gene expression and gene length by binning genes into quartiles based on mRNA expression and based on gene length using the longest distance between the transcription start site and 3’ end of all transcripts corresponding to a single gene. A total of sixteen bins were created, corresponding to all combinations between bins of expression and gene length. 1,249
Sleeping Beauty candidate genes and 900 VirHost genes could be assigned to one of these bins, with 156 genes common to the two sets. To assess the chance expectation for overlap, random sets of 1,249 genes were selected that matched the original query set in gene length and expression composition. Ten thousand iterations were used and the empirical P-value determined to be the fraction of random gene sets with at least 156 overlapping genes.
We also identified an insertional mutagenesis screen completed to find genetic determinants of leukemia using the Maloney Murine Leukemia virus79. The candidate gene set was downloaded from the Supplementary Data, mapped to human orthologues and tested for
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overlap against the VirHost set. A significant overlap (20 out of 213 genes, P = 0.048) was
found.
U. Comparison of viral interactome and prioritization of cancer genes
Catalogues of somatic mutations from genome-wide cancer sequencing studies were obtained
from the supplementary tables of nine recently published studies80-88. The deleteriousness of
each mutation was assessed with the Polyphen2 web server
(http://genetics.bwh.harvard.edu/pph2/), which provides a score (HumVar) between 0 and 1,
with 1 being the most damaging (Supplementary Table 11). A cumulative deleteriousness score
for each protein was obtained by summing all Polyphen2 scores. We observed that large
proteins tended to have higher scores and thus normalized scores for protein length. All proteins
with at least one somatic mutation were ranked by cumulative deleteriousness score, with TP53
having the highest score (Supplementary Fig. 14). For a direct comparison of COSMIC Classic
gene overlap with the viral interactome at various peptide count thresholds, a matching number
of somatically mutated proteins were selected from the top of the ranked list and statistical
significance assessed by Fisher’s Exact Test. Genes at SCNA-AMP, SCNA-DEL and GWAS
loci were also tested against COSMIC Classic genes using Fisher’s Exact Test. Odds ratios
were calculated with 2x2 contingency tables.
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Supplementary Notes
1. Significantly targeted and untargeted proteins
“Significantly targeted or untargeted” host proteins are sets of proteins that interact with viral proteins at a higher or lower frequency, respectively, than would be expected given their degree in the human HI-2 interaction network. The full set of viral-human Y2H interactions
(Supplementary Table 2) includes interactions obtained after recursive testing of the viral proteins from all seven HPV types against a human interactor found by any one HPV in the initial screen. To eliminate any bias introduced by the recursive nature of the verification retest experiments with HPV proteins, and insure that all viruses are analyzed within the context of the same reference search space this analysis only included interactions detected in the primary screens and directly verified by pairwise retesting. In addition, we further restricted this analysis to human proteins present in HI-2 (174 out of 307). We first counted the number of interactions between viral proteins and human targets in the virus-human interaction network and then randomly selected an identical number of proteins from HI-2. Sampling was performed with replacement (i.e. a protein could be chosen more than once) and the likelihood of choosing any protein determined by its number of interacting partners (degree) in HI-2. This process was repeated 10,000 times to generate a random distribution When less than 5% of the simulations generated a number of interactions equal to or greater than the number of experimentally observed interactions for a given host protein, this was considered “significantly targeted”. When more than 95% of the simulations generated a number of interactions equal to or greater than the number of experimentally determined interactions, the host protein was considered
“significantly untargeted”. Otherwise, the human protein was considered “expectedly targeted”.
2. HI-2 and analysis of overlaps between Y2H, TAP-MS and their respective PRS
The human protein-protein interactions previously reported in our three high-throughput data sets11,49,50 were updated to the gene annotations in human ORFeome 5.1 and then combined to
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generate the HI-2 list of unique interactions. Distinctions between ORFs corresponding to
multiple splice variant of a human gene were ignored when reporting protein-protein
interactions.
The significance of overlaps between different data sets was assessed by Fisher’s Exact
Test or permutation. We examined three overlaps, the overlap between TAP-MS and Y2H, as
well as the overlap of these two data sets separately with their respective PRS (Supplementary
Table 16). The search space for calculating the Y2H overlap with the Y2H-specific PRS is the
hORFeome v5.1. The search space for the TAP-MS and Y2H overlap corresponds to the
intersection of the hORFeome 5.1 and TAP-MS search spaces. The TAP-MS search space was
used to calculate the overlap between the TAP-MS data set and the TAP-MS-specific PRS. We
noticed that host proteins identified as viral targets by the TAP-MS technology tend to be
encoded by genes with high mRNA expression in IMR-90 cells (Supplementary Fig. 20). To
circumvent this expression bias when considering the TAP-MS data set, random sampling was
performed from a set of genes matching the query set in terms of expression quartiles, as
judged from genome-wide microarray analysis of IMR-90 cells.
Statistical significance of overlaps observed between the Y2H data set or the TAP-MS data
set and their respective PRS were computed by random sampling from the appropriate search
space, and controlling for expression bias for TAP-MS. Both our TAP-MS protein complex and
Y2H binary data sets significantly overlapped with PRS pairs (17 out of 94 protein pairs [P <
0.001] and 1 out of 62 protein pairs [P = 0.047] respectively). The overlap between our protein
complex viral-host interactions and those in the PRS varied as a function of analytical
parameters in our TAP-MS data: We observed that the fold enrichment of PRS proteins in the
set of host proteins detected in protein complex associations increased with the number of
unique peptides, although with fewer associations recovered at more stringent thresholds
(Supplementary Fig. 21).
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Twenty four viral ORFs were found to have interactors in both TAP-MS and Y2H data sets
(Supplementary Fig. 1), and of these a total of six viral ORF-host protein interactions were common to the two technologies (Supplementary Fig. 2). The observed overlap of six interactions was significantly higher (P < 0.001) than the most common random expectation of
0.81 interactions (Supplementary Fig. 2). To test whether the set of genes identified by TAP-MS as targets of a given viral protein included the immediate neighbours of viral targets identified by
Y2H, we repeated the analysis after expanding the list of Y2H targets to their direct neighbours in HI-2. We observed that targets of viral protein identified through both technologies showed a significant tendency to interact with each other (P < 0.001; Supplementary Fig. 2) implying that host targets of the two technologies tend to ‘fall in the same neighbourhood’ of the network.
3. Measurement of the precision of the Y2H data set by wNAPPA
We measured the fraction of true biophysical interactions in our virus-host binary Y2H data set
(precision) by comparing the recovery rate of the detected interactions to those of the Positive
Reference Set (PRS) and Random Reference Set (RRS) in a wNAPPA orthogonal assay. In the wNAPPA assay, bait and prey fusion proteins were expressed by coupled transcription- translation and GST-tagged bait proteins captured by anti-GST antibody. Interactions were detected using antibody to HA with standard immunochemical protocols. One viral ORF
(adeno5-E1A) was found to bind non-specifically to the GST plate and was therefore removed from subsequent analysis of the wNAPPA data (Supplementary Table 22). The precision was calculated for a recovery rate of 1% for the RRS, corresponding to a z-score threshold of 1.5. At this threshold, we observe recovery rates of 9% ± 1% for the interactions detected by Y2H, 10%
± 4% for the PRS interactions and precisely 1% ± 1% for the RRS interactions. The precision value was calculated according to the following formula50.
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where I+ is the number of true positives in the detected interactions (precision), f+ is the false
positive rate of wNAPPA (obtained as the fraction of RRS pairs reported positive) and (1 – f-) is
the fraction of Y2H supported PRS pairs that report positive in wNAPPA. Error bars of the
precision were calculated according to the variation of those values in the [1.5, 1.6] z-score
window. These measurements led to a precision of 90% +/- 7% for the virus-host binary Y2H
data set. We determined the fraction of true biophysical interactions in our virus-host binary Y2H
data set to be ~90% by comparing the validation rates of the data set to those of the PRS and
random reference set (Supplementary Fig. 22).
4. Network motif identification
We hypothesized that the striking variation in expression of multiple clusters may relate to TF
activity reinforcing feedback or feedforward loops. We thus used the list of predicted TF binding
sites to explore the interconnectedness of TFs. We focused on the TFs that were enriched for
TFBS in expression clusters and physically associated with or were differentially expressed in
response to expression of viral proteins (Supplementary Table 7). We looked for instances of
autoregulatory loops, feedback loops, and feed-forward loops – network motifs that might
amplify or modulate signals in biological networks – and compared the number observed in our
data to that observed after 1,000 random selections of TFs chosen from the set of TFs with one
or more predicted high-probability promoter binding site. There were far more interconnections
than expected by chance (P < 0.001; Supplementary Fig. 6), suggesting that viral proteins
target a dense regulatory TF network.
5. Cellular growth phenotypes
If gene expression clusters regulated by viral proteins were relevant to human cancer, then the
downstream variation in expression of host genes would be reflected in cellular growth
phenotypes. We measured growth and senescence rates for fifteen IMR-90 viral protein
expressing and control cell lines, and computed their Pearson correlation coefficients (R) with
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the average gene expression level in each cluster. The significance of the correlation was assessed for each cluster by randomly sampling 1,000 sets of equal size from the full gene universe represented on the microarray. The resulting P values were adjusted for multiple testing across the 31 clusters using the Benjamini-Hochberg procedure. There was significant correlation between five clusters and growth or senescence phenotypes. Cluster C31 demonstrated the strongest correlation between mRNA expression and both cellular growth (R
= 0.8, Padj = 0.008) and senescence (R = -0.92, Padj = 0.016) (Supplementary Fig. 8). This correlation suggests that the expression variation observed in each cluster across cell lines (Fig.
2a) is functionally relevant to viral proteins altering cellular phenotypes.
6. Cancer pathways perturbed by viral ORFs
Viral proteins perturb diverse core signalling pathways and biological functions of the host cell, including many of the known hallmarks of cancer14. Dysregulation of these pathways perturbed by viral ORFs could lead to tumorigenesis. Together, our transcriptional analysis and the interactome data sets shed light on known and potentially novel connections between viral proteins and cancer mechanisms.
We found several signatures of growth suppressor evasion in the transcriptional data. Viral proteins in group III, which include polyomavirus and high-risk HPV proteins that target RB1, were correlated with increased expression of genes involved in cell proliferation, including components of the cell cycle (CDC25A, CDC25C, CDK1) and DNA replication machinery
(PCNA, RFC and MCM family members) and genes involved in nucleosome assembly (HIST1H histone family). TP53-dependent pathways were typically downregulated in group III proteins, reflecting that many of these viral proteins bind to and inactivate TP53. Pathways include cell- cycle arrest mediated by CDKN1A as well as cell death through BAX and TRAIL pathway components (cluster C12, 2.0-fold enriched for TP53 binding sites, P = 2.3 X 10-9). We also observed that group II viral proteins, which include alternatively spliced E6 proteins from high-
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risk HPV types, HPV E5 proteins, E7 proteins from low-risk HPV6b and HPV11 and high-risk
HPV16, and many EBV proteins including EBNALP, induced a large decrease in TGFβ
signalling genes (cluster C16).
Metabolic dysregulation is often observed in cancer cells. Group III viral proteins induced
high expression of genes involved in cholesterol biosynthesis genes (DHCR24, HMGCS1,
SQLE) and fatty acid metabolism (MGLL, FABP3, ELOVL6, SCD), potentially reflecting the
need of rapidly proliferating cells and tumours for cholesterol89 and unsaturated fatty acids90,91.
Many viral proteins also increased expression of genes involved in response to hypoxia (cluster
C22).
Other characteristics that enable tumour growth include angiogenesis, invasion, and
metastasis. Group III viral proteins induced increased transcription of genes in the VEGF
pathway (cluster C23) and decreased transcription of genes involved in core functions of
fibroblasts, including expression of collagen components, secreted growth factors and
metalloproteases, and cell adhesion molecules such as integrins and protocadherins (clusters
C13, C17, C18, C19; Supplementary Fig. 9). Such changes imply some amount of
“reprogramming” induced by oncogenic viral proteins and again draw parallels with cancer
pathogenesis. High-risk HPV proteins may achieve changes to cell-substrate adhesion and
ECM production through transcriptional regulation of the growth factor EGR1. Other TFs with
highly enriched binding sites for these pathway genes include FOSL2, which interacts with low-
risk HPV E7s, and RUNX1. Still other viral ORFs may act through the transcription factors KLF7
and ARNTL, both of which are involved in insulin signalling92,93.
Many of the transcriptional changes caused by viral proteins relate to the two enabling
characteristics of cancer: genome instability and mutation, and tumour-promoting inflammation.
Group III viral proteins caused decreased expression of genes involved in the response to DNA
damage, including not only cell cycle arrest and apoptosis, but also autophagy via lysosome
assembly (clusters C6 and C7) and acidification (cluster C3; 3.5-fold enriched for binding sites
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of the autophagy implicated TF NRF2/NFE2L294 (P = 0.0007); and oxidative stress control through induction of G6PD and glutathione transferases GSTM4 and GSTM5 (cluster C15). We also observed marked upregulation of DNA nucleotide excision and double-stranded break repair (cluster C26 and C27: BRCA1, BRCA2, POL epsilon/delta, FANCB, XRCC2, BLM,
RAD51) and robust activation of two “inflammasome” pathways known to be triggered by DNA damage: the type I interferon response via IRF activity (cluster C24, 9.5-fold enriched, P = 3 X
10-10; Fig. 2b) and inflammatory responses (e.g. IL6, CCL2, IL1B) via NF-κB activity (cluster
C23, 4.7-fold enriched for NF-κB sites, P = 1.8 X 10-5). Our network suggests that HPV E6s,
EBV proteins, and polyomaviruses activate inflammatory pathways by regulating the expression of TFs like IRF1, NF-kB, RELB, and MAFF. In contrast, HPV E7s perturb REL directly through a binary interaction. Tumour virus proteins also induce changes in the expression of genes involved in other signalling pathways, including PDGFR signaling, calcium signalling, and WNT signalling.
Pathway enrichment analysis of proteins identified through TAP-MS or Y2H was also instructive on established and novel pathways targeted by viruses. To identify host pathways targeted by viral proteins in our combined interactome data set, we explored the enrichment of
Gene Ontology (GO) terms among host target proteins (Supplementary Fig. 3). Enrichment for some of the GO terms was expected (e.g., “mitotic cell cycle” for HPV, EBV and polyomavirus and “protein phosphatase 2A binding” for polyomavirus). Over-representation of “mitotic cell cycle” genes amongst HPV-associated proteins in our data set arises not only from established associations of HPV E6 and E7 with the RB1 and TP53 tumour suppressors, but also from associations observed with 28 additional cell cycle proteins, including PCNA, MCM3 and
CDKN1A (Padj < 0.01, OR = 3.3) (Supplementary Table 17).
Enrichment analysis also revealed previously unrecognised pathways unique to specific viruses. EBV proteins collectively bind members of the procollagen-proline 4-hydroxylase family
(Padj= 0.05, OR = 389; Supplementary Fig. 3) including P4HA2, a hypoxia- and p53-responsive
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protein, which inhibits angiogenesis and tumour growth in mice95. Adenoviral proteins bind
multiple members of the BH-domain family (BAX, BIK, BAK1; Padj = 0.04, OR = 159), which play
a critical role in apoptosis96. The adenovirus E4ORF1 protein binds to a family of mRNA 5’UTR
binding proteins (Padj = 0.03, OR = 319), including the insulin-like growth factor binding protein 2
(IGFBP2), which has recently been implicated in angiogenesis and metastasis in breast
cancer97.
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Supplementary References
30. Rual, J.F. et al., Human ORFeome version 1.1: a platform for reverse proteomics. Genome Res. 14, 2128-2135 (2004). 31. Sowa, M.E., Bennett, E.J., Gygi, S.P., & Harper, J.W., Defining the human deubiquitinating enzyme interaction landscape. Cell 138, 389-403 (2009). 32. Cole, S.T. & Danos, O., Nucleotide sequence and comparative analysis of the human papillomavirus type 18 genome. Phylogeny of papillomaviruses and repeated structure of the E6 and E7 gene products. J. Mol. Biol. 193, 599-608 (1987). 33. Dartmann, K., Schwarz, E., Gissmann, L., & zur Hausen, H., The nucleotide sequence and genome organization of human papilloma virus type 11. Virology 151, 124-130 (1986). 34. Fuchs, P.G., Iftner, T., Weninger, J., & Pfister, H., Epidermodysplasia verruciformis- associated human papillomavirus 8: genomic sequence and comparative analysis. J. Virol. 58, 626-634 (1986). 35. Zachow, K.R., Ostrow, R.S., & Faras, A.J., Nucleotide sequence and genome organization of human papillomavirus type 5. Virology 158, 251-254 (1987). 36. Cole, S.T. & Streeck, R.E., Genome organization and nucleotide sequence of human papillomavirus type 33, which is associated with cervical cancer. J. Virol. 58, 991-995 (1986). 37. Schwarz, E. et al., DNA sequence and genome organization of genital human papillomavirus type 6b. EMBO J. 2, 2341-2348 (1983). 38. Seedorf, K., Krammer, G., Durst, M., Suhai, S., & Rowekamp, W.G., Human papillomavirus type 16 DNA sequence. Virology 145, 181-185 (1985). 39. Schwarz, E. et al., Structure and transcription of human papillomavirus sequences in cervical carcinoma cells. Nature 314, 111-114 (1985). 40. Sedman, S.A. et al., The full-length E6 protein of human papillomavirus type 16 has transforming and trans-activating activities and cooperates with E7 to immortalize keratinocytes in culture. J. Virol. 65, 4860-4866 (1991). 41. Liu, Y. et al., Multiple functions of human papillomavirus type 16 E6 contribute to the immortalization of mammary epithelial cells. J. Virol. 73, 7297-7307 (1999). 42. Schowalter, R.M., Pastrana, D.V., Pumphrey, K.A., Moyer, A.L., & Buck, C.B., Merkel cell polyomavirus and two previously unknown polyomaviruses are chronically shed from human skin. Cell Host Microbe 7, 509-515 (2010). 43. van der Meijden, E. et al., Discovery of a new human polyomavirus associated with trichodysplasia spinulosa in an immunocompromized patient. PLoS Pathog. 6, e1001024 (2010). 44. Zalvide, J. & DeCaprio, J.A., Role of pRb-related proteins in simian virus 40 large-T- antigen-mediated transformation. Mol. Cell. Biol. 15, 5800-5810 (1995). 45. Hahn, W.C. et al., Enumeration of the simian virus 40 early region elements necessary for human cell transformation. Mol. Cell. Biol. 22, 2111-2123 (2002). 46. Zhong, Q. et al., Edgetic perturbation models of human inherited disorders. Mol. Syst. Biol. 5, 321 (2009). 47. James, P., Halladay, J., & Craig, E.A., Genomic libraries and a host strain designed for highly efficient two-hybrid selection in yeast. Genetics 144, 1425-1436 (1996). 48. Walhout, A.J. & Vidal, M., A genetic strategy to eliminate self-activator baits prior to high- throughput yeast two-hybrid screens. Genome Res. 9, 1128-1134 (1999). 49. Rual, J.F. et al., Towards a proteome-scale map of the human protein-protein interaction Rozenblatt-Rosen et al., 2012 network. Nature 437, 1173-1178 (2005). 50. Venkatesan, K. et al., An empirical framework for binary interactome mapping. Nature Methods 6, 83-90 (2009). 51. Litovchick, L., Florens, L.A., Swanson, S.K., Washburn, M.P., & DeCaprio, J.A., DYRK1A protein kinase promotes quiescence and senescence through DREAM complex assembly. Genes Dev. , 801-813 (2011). 25 67 52. Spangle, J.M. & Munger, K., The human papillomavirus type 16 E6 oncoprotein activates mTORC1 signaling and increases protein synthesis. J. Virol. 84, 9398WWW.NATURE.COM/NATURE-9407 (2010). | 61 53. Baker, S.J., Markowitz, S., Fearon, E.R., Willson, J.K., & Vogelstein, B., Suppression of human colorectal carcinoma cell growth by wild-type p53. Science 249, 912-915 (1990). 54. Litovchick, L. et al., Evolutionarily conserved multisubunit RBL2/p130 and E2F4 protein complex represses human cell cycle-dependent genes in quiescence. Mol. Cell 26, 539- 551 (2007). 55. Livak, K.J. & Schmittgen, T.D., Analysis of relative gene expression data using real-time quantitative PCR and the 2(-ΔΔCT) Method. Methods 25, 402-408 (2001). 56. Stewart, S.A. et al., Lentivirus-delivered stable gene silencing by RNAi in primary cells. RNA 9, 493-501 (2003). 57. Ramsby, M.L. & Makowski, G.S., Differential detergent fractionation of eukaryotic cells. Analysis by two-dimensional gel electrophoresis. Methods Mol. Biol. 112, 53-66 (1999). 58. Nakatani, Y. & Ogryzko, V., Immunoaffinity purification of mammalian protein complexes. Methods Enzymol. 370, 430-444 (2003). 59. Ficarro, S.B. et al., Improved electrospray ionization efficiency compensates for diminished chromatographic resolution and enables proteomics analysis of tyrosine signaling in embryonic stem cells. Anal. Chem. 81, 3440-3447 (2009). 60. Parikh, J.R. et al., multiplierz: an extensible API based desktop environment for proteomics data analysis. BMC Bioinformatics 10, 364 (2009). 61. Webber, J.T., Askenazi, M., & Marto, J.A., mzResults: an interactive viewer for interrogation and distribution of proteomics results. Mol. Cell. Proteomics 10, M110 003970 (2011). 62. Askenazi, M., Marto, J.A., & Linial, M., The complete peptide dictionary--a meta- proteomics resource. Proteomics 10, 4306-4310 (2010). 63. Askenazi, M., Li, S., Singh, S., & Marto, J.A., Pathway Palette: a rich internet application for peptide-, protein- and network-oriented analysis of MS data. Proteomics 10, 1880-1885 (2010). 64. Chatr-aryamontri, A. et al., VirusMINT: a viral protein interaction database. Nucleic Acids Res. 37, D669-673 (2009). 65. Berriz, G.F., Beaver, J.E., Cenik, C., Tasan, M., & Roth, F.P., Next generation software for functional trend analysis. Bioinformatics 25, 3043-3044 (2009). 66. Dai, M. et al., Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 33, e175 (2005). 67. Johnson, W.E., Li, C., & Rabinovic, A., Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118-127 (2007). 68. Cheung, M.S., Down, T.A., Latorre, I., & Ahringer, J., Systematic bias in high-throughput sequencing data and its correction by BEADS. Nucleic Acids Res. 39, e103 (2011). 69. Benjamini, Y. & Hochberg, Y., Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Ser. B (Methodological) 57, 289-300 (1995). 70. Smoot, M.E., Ono, K., Ruscheinski, J., Wang, P.L., & Ideker, T., Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27, 431-432 (2011). 71. Pique-Regi, R. et al., Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Res. 21, 447-455 (2011). 72. Palomero, T. et al., NOTCH1 directly regulates c-MYC and activates a feed-forward-loop transcriptional network promoting leukemic cell growth. Proc. Natl Acad. Sci. USA 103, 18261-18266 (2006).
68 Rozenblatt-Rosen et al., 2012
RESEARCH SUPPLEMENTARY INFORMATION 51. Litovchick, L., Florens, L.A., Swanson, S.K., Washburn, M.P., & DeCaprio, J.A., DYRK1A protein kinase promotes quiescence and senescence through DREAM complex assembly. Genes Dev. 25, 801-813 (2011). 52. Spangle, J.M. & Munger, K., The human papillomavirus type 16 E6 oncoprotein activates mTORC1 signaling and increases protein synthesis. J. Virol. 84, 9398-9407 (2010). 53. Baker, S.J., Markowitz, S., Fearon, E.R., Willson, J.K., & Vogelstein, B., Suppression of human colorectal carcinoma cell growth by wild-type p53. Science 249, 912-915 (1990). 54. Litovchick, L. et al., Evolutionarily conserved multisubunit RBL2/p130 and E2F4 protein complex represses human cell cycle-dependent genes in quiescence. Mol. Cell 26, 539- 551 (2007). 55. Livak, K.J. & Schmittgen, T.D., Analysis of relative gene expression data using real-time quantitative PCR and the 2(-ΔΔCT) Method. Methods 25, 402-408 (2001). 56. Stewart, S.A. et al., Lentivirus-delivered stable gene silencing by RNAi in primary cells. RNA 9, 493-501 (2003). 57. Ramsby, M.L. & Makowski, G.S., Differential detergent fractionation of eukaryotic cells. Analysis by two-dimensional gel electrophoresis. Methods Mol. Biol. 112, 53-66 (1999). 58. Nakatani, Y. & Ogryzko, V., Immunoaffinity purification of mammalian protein complexes. Methods Enzymol. 370, 430-444 (2003). 59. Ficarro, S.B. et al., Improved electrospray ionization efficiency compensates for diminished chromatographic resolution and enables proteomics analysis of tyrosine signaling in embryonic stem cells. Anal. Chem. 81, 3440-3447 (2009). 60. Parikh, J.R. et al., multiplierz: an extensible API based desktop environment for proteomics data analysis. BMC Bioinformatics 10, 364 (2009). 61. Webber, J.T., Askenazi, M., & Marto, J.A., mzResults: an interactive viewer for interrogation and distribution of proteomics results. Mol. Cell. Proteomics 10, M110 003970 (2011). 62. Askenazi, M., Marto, J.A., & Linial, M., The complete peptide dictionary--a meta- proteomics resource. Proteomics 10, 4306-4310 (2010). 63. Askenazi, M., Li, S., Singh, S., & Marto, J.A., Pathway Palette: a rich internet application for peptide-, protein- and network-oriented analysis of MS data. Proteomics 10, 1880-1885 (2010). 64. Chatr-aryamontri, A. et al., VirusMINT: a viral protein interaction database. Nucleic Acids Res. 37, D669-673 (2009). 65. Berriz, G.F., Beaver, J.E., Cenik, C., Tasan, M., & Roth, F.P., Next generation software for functional trend analysis. Bioinformatics 25, 3043-3044 (2009). 66. Dai, M. et al., Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 33, e175 (2005). 67. Johnson, W.E., Li, C., & Rabinovic, A., Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118-127 (2007). 68. Cheung, M.S., Down, T.A., Latorre, I., & Ahringer, J., Systematic bias in high-throughput sequencing data and its correction by BEADS. Nucleic Acids Res. 39, e103 (2011). 69. Benjamini, Y. & Hochberg, Y., Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Ser. B (Methodological) 57, 289-300 (1995). 70. Smoot, M.E., Ono, K., Ruscheinski, J., Wang, P.L., & Ideker, T., Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27, 431-432 (2011). 71. Pique-Regi, R. et al., Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Res. 21, 447-455Rozenblatt-Rosen (2011). et al., 2012 72. Palomero, T. et al., NOTCH1 directly regul ates c-MYC and activates a feed-forward-loop transcriptional network promoting leukemic cell growth. Proc. Natl Acad. Sci. USA 103, 18261-18266 (2006). 73. Raychaudhuri, S. et al., Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 5, e1000534 (2009). 74. Myers, S., Bottolo, L., Freeman, C., McVean,68 G., & Donnelly, P., A fine-scale map of recombination rates and hotspots across the human genome. Science 310, 321-324 (2005). 75. Starr, T.K. et al., A Sleeping Beauty transposon-mediated screen identifies murine susceptibility genes for adenomatous polyposis coli (Apc)-dependent intestinal 62 | WWW.NATURE.COM/NATURE tumorigenesis. Proc. Natl Acad. Sci. USA 108, 5765-5770 (2011). 76. March, H.N. et al., Insertional mutagenesis identifies multiple networks of cooperating genes driving intestinal tumorigenesis. Nature Genet. 43, 1202-1209 (2011). 77. Starr, T.K. et al., A transposon-based genetic screen in mice identifies genes altered in colorectal cancer. Science 323, 1747-1750 (2009). 78. Mann, K.M. et al., Sleeping Beauty mutagenesis reveals cooperating mutations and pathways in pancreatic adenocarcinoma. Proc. Natl Acad. Sci. USA 109, 5934-5941 (2012). 79. Bergerson, R.J. et al., An insertional mutagenesis screen identifies genes that cooperate with Mll-AF9 in a murine leukemogenesis model. Blood 119, 4512-4523 (2012). 80. Cancer Genome Atlas Research Network, Integrated genomic analyses of ovarian carcinoma. Nature 474, 609-615 (2011). 81. Agrawal, N. et al., Exome sequencing of head and neck squamous cell carcinoma reveals inactivating mutations in NOTCH1. Science 333, 1154-1157 (2011). 82. Chapman, M.A. et al., Initial genome sequencing and analysis of multiple myeloma. Nature 471, 467-472 (2011). 83. Lee, W. et al., The mutation spectrum revealed by paired genome sequences from a lung cancer patient. Nature 465, 473-477 (2010). 84. Parsons, D.W. et al., An integrated genomic analysis of human glioblastoma multiforme. Science 321, 1807-1812 (2008). 85. Parsons, D.W. et al., The genetic landscape of the childhood cancer medulloblastoma. Science 331, 435-439 (2011). 86. Pleasance, E.D. et al., A small-cell lung cancer genome with complex signatures of tobacco exposure. Nature 463, 184-190 (2010). 87. Puente, X.S. et al., Whole-genome sequencing identifies recurrent mutations in chronic lymphocytic leukaemia. Nature 475, 101-105 (2011). 88. Stransky, N. et al., The mutational landscape of head and neck squamous cell carcinoma. Science 333, 1157-1160 (2011). 89. Kuehnle, K. et al., Prosurvival effect of DHCR24/Seladin-1 in acute and chronic responses to oxidative stress. Mol. Cell. Biol. 28, 539-550 (2008). 90. Scaglia, N., Caviglia, J.M., & Igal, R.A., High stearoyl-CoA desaturase protein and activity levels in simian virus 40 transformed-human lung fibroblasts. Biochim. Biophys. Acta 1687, 141-151 (2005). 91. Scaglia, N. & Igal, R.A., Stearoyl-CoA desaturase is involved in the control of proliferation, anchorage-independent growth, and survival in human transformed cells. J. Biol. Chem. 280, 25339-25349 (2005). 92. Kawamura, Y., Tanaka, Y., Kawamori, R., & Maeda, S., Overexpression of Kruppel-like factor 7 regulates adipocytokine gene expressions in human adipocytes and inhibits glucose-induced insulin secretion in pancreatic beta-cell line. Mol. Endocrinol. 20, 844-856 (2006). 93. Marcheva, B. et al., Disruption of the clock components CLOCK and BMAL1 leads to hypoinsulinaemia and diabetes. Nature 466, 627-631 (2010).
69 Rozenblatt-Rosen et al., 2012
73. Raychaudhuri, S. et al., Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 5, e1000534 (2009). SUPPLEMENTARY INFORMATION RESEARCH 74. Myers, S., Bottolo, L., Freeman, C., McVean, G., & Donnelly, P., A fine-scale map of recombination rates and hotspots across the human genome. Science 310, 321-324 (2005). 75. Starr, T.K. et al., A Sleeping Beauty transposon-mediated screen identifies murine susceptibility genes for adenomatous polyposis coli (Apc)-dependent intestinal tumorigenesis. Proc. Natl Acad. Sci. USA 108, 5765-5770 (2011). 76. March, H.N. et al., Insertional mutagenesis identifies multiple networks of cooperating genes driving intestinal tumorigenesis. Nature Genet. 43, 1202-1209 (2011). 77. Starr, T.K. et al., A transposon-based genetic screen in mice identifies genes altered in colorectal cancer. Science 323, 1747-1750 (2009). 78. Mann, K.M. et al., Sleeping Beauty mutagenesis reveals cooperating mutations and pathways in pancreatic adenocarcinoma. Proc. Natl Acad. Sci. USA 109, 5934-5941 (2012). 79. Bergerson, R.J. et al., An insertional mutagenesis screen identifies genes that cooperate with Mll-AF9 in a murine leukemogenesis model. Blood 119, 4512-4523 (2012). 80. Cancer Genome Atlas Research Network, Integrated genomic analyses of ovarian carcinoma. Nature 474, 609-615 (2011). 81. Agrawal, N. et al., Exome sequencing of head and neck squamous cell carcinoma reveals inactivating mutations in NOTCH1. Science 333, 1154-1157 (2011). 82. Chapman, M.A. et al., Initial genome sequencing and analysis of multiple myeloma. Nature 471, 467-472 (2011). 83. Lee, W. et al., The mutation spectrum revealed by paired genome sequences from a lung cancer patient. Nature 465, 473-477 (2010). 84. Parsons, D.W. et al., An integrated genomic analysis of human glioblastoma multiforme. Science 321, 1807-1812 (2008). 85. Parsons, D.W. et al., The genetic landscape of the childhood cancer medulloblastoma. Science 331, 435-439 (2011). 86. Pleasance, E.D. et al., A small-cell lung cancer genome with complex signatures of tobacco exposure. Nature 463, 184-190 (2010). 87. Puente, X.S. et al., Whole-genome sequencing identifies recurrent mutations in chronic lymphocytic leukaemia. Nature 475, 101-105 (2011). 88. Stransky, N. et al., The mutational landscape of head and neck squamous cell carcinoma. Science 333, 1157-1160 (2011). 89. Kuehnle, K. et al., Prosurvival effect of DHCR24/Seladin-1 in acute and chronic responses to oxidative stress. Mol. Cell. Biol. 28, 539-550 (2008). 90. Scaglia, N., Caviglia, J.M., & Igal, R.A., High stearoyl-CoA desaturase protein and activity levels in simian virus 40 transformed-human lung fibroblasts. Biochim. Biophys. Acta 1687, 141-151 (2005). 91. Scaglia, N. & Igal, R.A., Stearoyl-CoA desaturase is involved in the control of proliferation, anchorage-independent growth, and survival in human transformed cells. J. Biol. Chem. 280, 25339-25349 (2005). 92. Kawamura, Y., Tanaka, Y., Kawamori, R., & Maeda, S., Overexpression of Kruppel-like factor 7 regulates adipocytokine gene expressions in human adipocytes and inhibits glucose-induced insulin secretion in pancreatic beta-cell line. Mol. Endocrinol. 20, 844-856 Rozenblatt-Rosen et al., 2012 (2006). 93. Marcheva, B. et al., Disruption of the clock components CLOCK and BMAL1 leads to hypoinsulinaemia and diabetes. Nature 466, 627-631 (2010). 94. Rao, V.A. et al., The antioxidant transcription factor Nrf2 negatively regulates autophagy and growth arrest induced by the anticancer redox agent mitoquinone. J. Biol. Chem. 285, 34447-34459 (2010). 95. Teodoro, J.G., Parker, A.E., Zhu, X., & Green, M.R., p53-mediated inhibition of 69 angiogenesis through up-regulation of a collagen prolyl hydroxylase. Science 313, 968- 971 (2006). 96. Kelekar, A. & Thompson, C.B., Bcl-2-family proteins: the role of the BH3 domain in apoptosis. Trends Cell Biol. 8, 324-330 (1998). 97. Png, K.J., Halberg, N., Yoshida, M., & Tavazoie, S.F., A microRNA regulon that mediates endothelial recruitment and metastasis by cancer cells. Nature 481, 190-194 (2011).
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