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Interactions and Networks Mike Cherry ! Genomics March 11, 2014 !1 A map of protein- protein interactions in Saccharomyces cerevisiae A-L Barabasi & ZN Oltvai (2004) Nature Reviews 5:104 Representation of protein interactions !3 A great visualization captures data’s complexity with simplicity http://www.youtube.com/watch?v=tnqxrcfUMsw !4 http://www.ebi.ac.uk/intact/ Network Metrics • How many links a node has to other nodes. Degree (connectivity) = k • Probability that a node has exactly k links. Characteristic of different classes of networks. Degree Distribution = P(k) • Overall tendency of nodes to form clusters or groups. Clustering Coefficient = C. C(k) is the average clustering coefficient of all nodes. C = 2N/k(k-1) with k links. !6 Network Types A-L Barabasi & ZN Oltvai (2004) Nature Reviews 5:104 To study the network characteristics A-L Barabasi & ZN Oltvai (2004) Nature Reviews 5:104 MG Grigorov (2005) Drug Discovery Today 10:365 Co-Expression Network J-D Han et al M Vidal (2004) Nature 430:88 Vidal, M. et al. (2011) Cell, Volume 144, 986 - 998 !11 Vidal, M. et al. (2011) Cell, Volume 144, 986 - 998 !12 Vidal, M. et al. (2011) Cell, Volume 144, 986 - 998 !13 2-hybrid screen for interacting proteins !14 http://en.wikipedia.org/wiki/Two-hybrid_screening !15 Ito T et al. PNAS 2001;98:4569-4574 !16 Synthetic genetic array methodology !17 Tong, et al. (2001) Science. 294; 2364-2368 !18 !19 !20 !21 Motif Profiling Network !22 !23 (d) (c) (b) (a) Reguly et al (2006) J. Biology J. (2006) al et Reguly 14,000 10,000 12,000 2,000 4,000 6,000 8,000 1,000 2,000 3,000 4,000 5,000 6,000 Interactions 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 0 0 (8,165) 11,061 0 1977 1 1,740 1 (19,499 nonredundant) 1 2 3 5 10 20 30 50 100 >100 2 1,740 Affinity capture-MS 1978 1,004 2 ofCharacterization dataset interaction LC the 1979 1 LC-GI publications 1,004 LC 33,311 Affinity capture-RNA 1 1980 1 778 1 1,556 Affinity capture-western 1981 8 5 3 662 :11 11 1,324 Biochemical activity 1982 6 21 1983 5 433 Co-crystal structure (11,334) 1,299 1984 33 22,250 399 12 Co-fractionation 18 1,197 1985 9 LC-GI interactions Co-localization LC-PI 1986 30 394 17 1,731 55 Publications Co-purification 1987 21 Interactions perpublication Interactions 501 35 Gene-gene Protein-protein 2,187 HTP-PI 1988 Far western 25 1989 88 309 49 2,278 FRET 1990 89 424 51 3,147 LC-GI 164 Protein-peptide 1991 73 Interactions LC-PI publications 1992 176 120 Reconstituted complex 98 1,663 1993 362 209 HTP-GI 161 2,977 Two-hybr id 1994 554 220 (6,103) 8,111 Dosage growth defect 1995 635 17 257 429 1996 1,087 53 Dosage lethality 359 (17,674 nonredundant) 1,299 1,778 1997 431 Dosage rescue 2,233 HTP 21,105 5 LC-PI interactions 1998 518 180 Phenotypic enhancement 1999 2,573 41 530 1,609 4,616 Phenotypic suppression !24 2000 585 2001 8,382 1 Synthetic growth defect 592 52 2002 12,561 27 595 (11,571) 1,840 Synthetic lethality 4,578 12,994 2003 561 Synthetic rescue 9,436 1 2004 564 133 2005 4,848 22 419 5,651 (a) (b) LC-PI HTP-PI Overlap LC-PI (3,289, 11,334) HTP-PI (4,474, 11,571) 5,000 5,000 4,000 4,000 nodes 3,000 3,000 2,000 2,000 edges n = 3,289 n = 4,474 n = 1,201 1,000 1,000 i = 11,334 i = 11,571 i = 1,624 0 0 0 1,000 2,000 3,000 4,000 5,000 0 1,000 2,000 3,000 4,000 5,000 LC-GI HTP-GI Overlap LC-GI (2,689, 8,165) HTP-GI (1,454, 6,103) 3,000 3,000 2,500 2,500 2,000 2,000 1,500 1,500 1,000 1,000 n = 1,454 n = 216 n = 2,689 500 500 i = 8,165 i = 6,103 i = 305 0 0 0 0 500 500 1,000 1,500 2,000 2,500 3,000 1,000 1,500 2,000 2,500 3,000 (c) (d) !25 Gavin (1019) Ho (456) Reguly et al (2006) J. Biology 5:11 3,000 3,000 1,019 2,500 2,500 0.09 2,000 2,000 0.08 1,500 1,500 0.07 1,000 1,000 500 500 0.06 0 0 0 1,000 2,000 3,000 0 1,000 2,000 3,000 0.05 456 0.04 305 Ito (275) Uetz (202) 0.03 3,000 3,000 275 202 2,500 2,500 0.02 2,000 2,000 Fraction overlap with LC data 0.01 1,500 1,500 0 1,000 1,000 Gavin Ho Ito Uetz HTP-GI 500 500 0 0 0 1,000 2,000 3,000 0 1,000 2,000 3,000 (a) (b) LC-PI HTP-PI Overlap LC-PI (3,289, 11,334) HTP-PI (4,474, 11,571) 5,000 5,000 4,000 4,000 3,000 3,000 2,000 2,000 n = 3,289 n = 4,474 n = 1,201 1,000 1,000 i = 11,334 i = 11,571 i = 1,624 0 0 0 1,000 2,000 3,000 4,000 5,000 0 1,000 2,000 3,000 4,000 5,000 LC-GI HTP-GI Overlap LC-GI (2,689, 8,165) HTP-GI (1,454, 6,103) 3,000 3,000 2,500 2,500 2,000 2,000 1,500 1,500 1,000 1,000 n = 1,454 n = 216 n = 2,689 Overlap of HTP500 with LC 500 i = 8,165 i = 6,103 i = 305 0 0 0 0 500 500 1,000 1,500 2,000 2,500 3,000 1,000 1,500 2,000 2,500 3,000 (c)Gavin (1019) mass spec Ho (456) (d) 3,000 3,000 1,019 2,500 2,500 0.09 2,000 2,000 0.08 1,500 1,500 0.07 1,000 1,000 500 500 0.06 0 0 0 1,000 2,000 3,000 0 1,000 2,000 3,000 0.05 456 0.04 305 Ito (275) 2-hybrid Uetz (202) 0.03 3,000 3,000 275 202 2,500 2,500 0.02 2,000 2,000 Fraction overlap with LC data 0.01 1,500 1,500 0 1,000 1,000 Gavin Ho Ito Uetz HTP-GI 500 500 mass spec 2-hybrid 0 0 0 1,000 2,000 3,000 0 1,000 2,000 3,000 Reguly et al (2006) J. Biology 5:11 !26 Overlap between GI and PI datasets !27 Random Party Hubs Date Hubs All Hubs !28 Nodes random -- green ! Filtered Hubs Yeast all -- brown party -- blue Interactions date -- red -date remove all date hubs -party J-D Han et al M Vidal (2004) Nature 430:88 !30 Batada NN et al. (2006) doi:info:doi/10.1371/journal.pbio.0040317.g006 Layout of FYI network Layout of HCfyi network altocumulus stratus Batada NN et al. (2006) doi/10.1371/journal.pbio.0040317.g006 !32 Batada NN et al. (2006) doi/10.1371/journal.pbio.0040317.g006 Construction of the diseasome bipartite network !33 Goh K et al. PNAS 2007;104:8685-8690 Goh K et al. PNAS 2007;104:8685-8690 !34 Developmental Network !35 Transcription Network TF-Z Gene X X A TF-X/A Gene Y TF-Y Z Y Gene Z Reece-Hoyes, JS et al. (2005) Genome Biology 6:R110 (doi:10.1186/gb-2005-6-13-r110) !36 Worm TF protein-protein interaction network Y51H4A.17 NHR-69 NHR-49 NHR-111 NHR-10 ZF NHR HOMEODOMAIN MIG-5 ZF C2H2 bZIP WH MH1 STAT ZF GATA Unknown AT Hook ZF THAP Y65B4BR.5 FLYWCH ZF PHD HMG MYB IPT/TIG Reece-Hoyes, JS et al. (2005) Genome Biology 6:R110 (doi:10.1186/gb-2005-6-13-r110) !37 Transcription network rewiring Annie E. Tsong, et al. (2006) Nature 443, 415-420 !38 ! Plausible pathway to concurrent rewiring of a large set of genes Tuch et al., (2008) Science 319 (5871): 1797-1799 !39 H-Adrenal gland H-Kidney M-Kidney C-Kidney Kidney H-Liver M-Liver C-Liver F-Gallbladder F-Liver P-Liver H-Pancreas H-Stomach M-Large intestine M-Small intestine M-Stomach C-Gallbladder P-Gallbladder C-Intestine P-Intestine P-Stomach F-Smallintestine Lung & uterus F-Largeintestine F-Stomach C-Oviduct C-Stomach M-Mammary gland H-Lung M-Lung F-Lung H-Uterus Immune tissues M-Uterus M-Ovary H-Placenta P-Fin P-Gill C-Lung H-Thyroid H-Bone marrow M-Bone Marrow H-Thymus Digestive tissues !40 M-Thymus M-Spleen Chan et al. Journal of Biology 2009 8:33 doi:10.1186/jbiol130 C-BursaofFabricus C-Thymus C-Femur Reproductive tissues C-Spleen H-Small Intestine H-Spleen F-Spleen P-Spleen P-Kidney M-Calvaria Liver F-Cartilage F-Femur H-Testis M-Testis C-Testis F-Testis F-Fatbody F-Kidney F-Ovary P-Ovary Neural tissues P-Testis P-Swimbladder F-Oviduct C-Ovary H-Brain H-Brain - cerebral cortex H-Brain - cerebellum M-Cerebellum M-Cortex C-Cerebellum C-Cerebralcortex F-Brain P-Brain H-Retina M-Eye C-Eye F-Eye P-Eye H-Heart M-Heart H-Skeletal Muscle M-Skeletal Muscle C-Muscle F-Muscle P-Redmuscle P-Whitemuscle C-Heart F-Heart P-Heart P-Beak P-Calvaria P-Skin P-Connectivetissue M-Skin C-Skin C-Gizzard Muscle & skin tissues F-Esophagus F-Skin P-Fin P-Gill P-Eye F-Eye C-Eye P-Skin M-Eye F-Skin C-Skin P-Liver F-Liver M-Skin F-Lung P-Beak H-Liver C-Liver P-Brain F-Brain H-Lung C-Lung M-Liver P-Heart M-Lung H-Brain F-Heart P-Testis H-Heart F-Testis C-Heart P-Ovary F-Ovary M-Heart H-Testis C-Testis C-Ovary M-Testis M-Ovary F-Femur C-Femur H-Retina P-Kidney H-Uterus F-Kidney P-Spleen F-Spleen M-Uterus M-Cortex H-Kidney C-Kidney F-Muscle C-Spleen H-Spleen M-Kidney C-Muscle M-Spleen F-Oviduct H-Thyroid C-Oviduct C-Gizzard P-Calvaria F-Fatbody H-Thymus C-Thymus P-Intestine M-Thymus P-Stomach C-Intestine M-Calvaria F-Stomach H-Placenta F-Cartilage C-Stomach H-Stomach M-Stomach H-Pancreas P-Redmuscle F-Esophagus P-Gallbladder F-Gallbladder C-Cerebellum C-Gallbladder M-Cerebellum P-Whitemuscle P-Swimbladder H-Bone marrow M-Bone Marrow F-Smallintestine F-Largeintestine H-Adrenal gland C-Cerebralcortex H-Small Intestine M-Small intestine M-Large intestine H-Skeletal Muscle M-Skeletal Muscle M-Mammary gland C-BursaofFabricus P-Connectivetissue H-Brain - cerebellum H-Brain - cerebral cortex 0 0.1 0.2 0.3 0.4 >0.5 Pearson correlation coefficient Chan et al.
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    Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database.
  • An Amino-Terminal Domain of Mxil Mediates Anti-Myc Oncogenic Activity and Interacts with a Homolog of the Yeast Transcriptional Repressor SIN3

    An Amino-Terminal Domain of Mxil Mediates Anti-Myc Oncogenic Activity and Interacts with a Homolog of the Yeast Transcriptional Repressor SIN3

    CORE Metadata, citation and similar papers at core.ac.uk Provided by Elsevier - Publisher Connector Cell, Vol, 80, 777-786, March 10, 1995, Copyright © 1995 by Cell Press An Amino-Terminal Domain of Mxil Mediates Anti-Myc Oncogenic Activity and Interacts with a Homolog of the Yeast Transcriptional Repressor SIN3 Nicole Schreiber-Agus,*t Lynda Chin,*tt Ken Chen,t et al., 1990), and a carboxy-terminal a-helical domain re- Richard Torres, t Govinda Rao,§ Peter Guida,t quired for dimerization with another basic region-helix- Arthur h Skoultchi,§ and Ronald A. DePinhot Ioop-helix-leucine zipper (bHLH-LZ) protein, Max (Black- "rDepartments of Microbiology and Immunology wood and Eisenman, 1991; Prendergast et al., 1991). and of Medicine Many of the biochemical and biological activities of Myc §Department of Cell Biology appear to be highly dependent upon its association with ~Division of Dermatology Max (Blackwood and Eisenman, 1991 ; Prendergast et al., Albert Einstein College of Medicine 1991; Kretzner et al., 1992; Amati et al., 1993a, 1993b). Bronx, New York 10461 In addition to its key role as an obligate partner in transacti- vation-competent Myc-Max complexes, Max may also re- press Myc-responsive genes through the formation of Summary transactivation-inert complexes that are capable of bind- ing the Myc-Max recognition sequence (Blackwood et al., Documented interactions among members of the Myc 1992; Kato et al., 1992; Kretzner et al., 1992; Makela et superfamily support a yin-yang model for the regula- al., 1992; Mukherjee et al., 1992; Prendergast et al., 1992; tion of Myc-responsive genes in which t ransactivation- Ayer et al., 1993; Zervos et al., 1993).