NetBioSIG2013-Talk Vuk Janjic

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Presentation for Network Biology SIG 2013 by Vuk Janjic, Imperial College London, UK. “A Journey to the Core of Human Disease”

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NetBioSIG2013-Talk Vuk Janjic

  1. 1. Outline Background Methods Data Constructing the networks Graphlets K-core decomposition The Core Diseasome Topological uniqueness Functional annotation Drug targets Computing the Core Diseasome Key cardio-vascular disease genes G-protein coupled receptors Imperial College London Vuk Janjić vj11@imperial.ac.uk
  2. 2. Outline Background Methods Data Constructing the networks Graphlets K-core decomposition The Core Diseasome Topological uniqueness Functional annotation Drug targets Computing the Core Diseasome Key cardio-vascular disease genes G-protein coupled receptors Imperial College London Vuk Janjić vj11@imperial.ac.uk
  3. 3. Background A LOT of system-level biological data due to advances in biotechnology Imperial College London Vuk Janjić vj11@imperial.ac.uk 1/17
  4. 4. Background A LOT of system-level biological data due to advances in biotechnology We’re looking for a “core subnetwork” of the human protein-protein interaction (PPI) network in which genes (their protein products) involved in a multitude of diseases reside Imperial College London Vuk Janjić vj11@imperial.ac.uk 1/17
  5. 5. Background A LOT of system-level biological data due to advances in biotechnology We’re looking for a “core subnetwork” of the human protein-protein interaction (PPI) network in which genes (their protein products) involved in a multitude of diseases reside No a priori knowledge of genes’ involvement in disease and by using k-core decomposition Imperial College London Vuk Janjić vj11@imperial.ac.uk 1/17
  6. 6. Background A LOT of system-level biological data due to advances in biotechnology We’re looking for a “core subnetwork” of the human protein-protein interaction (PPI) network in which genes (their protein products) involved in a multitude of diseases reside No a priori knowledge of genes’ involvement in disease and by using k-core decomposition Other studies have used a similar approach, but with a different goal in mind Imperial College London Vuk Janjić vj11@imperial.ac.uk 1/17
  7. 7. Outline Background Methods Data Constructing the networks Graphlets K-core decomposition The Core Diseasome Topological uniqueness Functional annotation Drug targets Computing the Core Diseasome Key cardio-vascular disease genes G-protein coupled receptors Imperial College London Vuk Janjić vj11@imperial.ac.uk
  8. 8. Data # of nodes # of edges Reference Protein-protein 11,100 56,708 HPRD, BioGRID Genetic 274 281 BioGRID Disease-gene 561 / 4,004 4,029 Disease Ontology (diseases/genes) Table: Interaction data Janjić V. & Pržulj N., Molecular BioSystems, 8, 2614-2625 (2012). Imperial College London Vuk Janjić vj11@imperial.ac.uk 2/17
  9. 9. Constructing the networks Table: Basic network properties for our four networks H-ALL H-SIM REST CORE Number of nodes 11,100 1,706 8,227 88 Number of edges 56,807 8,655 24,730 865 Clustering coefficient 0.125 0.173 0.102 0.462 Diameter 13 9 16 3 Radius 7 5 8 2 Avg. degree 10.23 10.14 4.53 19.65 Avg. path length 3.69 3.48 4.53 1.87 Imperial College London Vuk Janjić vj11@imperial.ac.uk 3/17
  10. 10. Constructing the networks Table: Basic network properties for our four networks H-ALL H-SIM REST CORE Number of nodes 11,100 1,706 8,227 88 Number of edges 56,807 8,655 24,730 865 Clustering coefficient 0.125 0.173 0.102 0.462 Diameter 13 9 16 3 Radius 7 5 8 2 Avg. degree 10.23 10.14 4.53 19.65 Avg. path length 3.69 3.48 4.53 1.87 Imperial College London Vuk Janjić vj11@imperial.ac.uk 3/17
  11. 11. Constructing the networks Table: Basic network properties for our four networks H-ALL H-SIM REST CORE Number of nodes 11,100 1,706 8,227 88 Number of edges 56,807 8,655 24,730 865 Clustering coefficient 0.125 0.173 0.102 0.462 Diameter 13 9 16 3 Radius 7 5 8 2 Avg. degree 10.23 10.14 4.53 19.65 Avg. path length 3.69 3.48 4.53 1.87 Imperial College London Vuk Janjić vj11@imperial.ac.uk 3/17
  12. 12. Constructing the networks 2-node graphlet 4-node graphlets3-node graphlets 5-node graphlets G0 G1 G2 G3 G4 G5 G6 G7 G8 0 1 2 3 4 5 6 7 8 10 11 9 13 12 14 G9 G10 G11 G12 G13 G14 G15 G16 G17 G18 G19 G20 G21 G22 G23 G24 G25 G26 G27 G28 G29 15 16 17 18 20 21 19 22 23 25 26 24 29 30 28 27 32 31 33 34 36 37 38 35 39 42 40 41 43 44 46 48 47 45 50 49 52 53 51 54 55 57 58 56 59 61 60 63 64 62 65 67 66 68 69 70 71 72 Figure: Graphlets with automorphism orbits. Pržulj N., Bioinformatics, 23, e177-e183 (2007). Imperial College London Vuk Janjić vj11@imperial.ac.uk 3/17
  13. 13. Constructing the networks Table: Basic network properties for our four networks H-ALL H-SIM REST CORE Number of nodes 11,100 1,706 8,227 88 Number of edges 56,807 8,655 24,730 865 Clustering coefficient 0.125 0.173 0.102 0.462 Diameter 13 9 16 3 Radius 7 5 8 2 Avg. degree 10.23 10.14 4.53 19.65 Avg. path length 3.69 3.48 4.53 1.87 Imperial College London Vuk Janjić vj11@imperial.ac.uk 3/17
  14. 14. Constructing the networks Table: Basic network properties for our four networks H-ALL H-SIM REST CORE Number of nodes 11,100 1,706 8,227 88 Number of edges 56,807 8,655 24,730 865 Clustering coefficient 0.125 0.173 0.102 0.462 Diameter 13 9 16 3 Radius 7 5 8 2 Avg. degree 10.23 10.14 4.53 19.65 Avg. path length 3.69 3.48 4.53 1.87 Imperial College London Vuk Janjić vj11@imperial.ac.uk 3/17
  15. 15. Constructing the networks Table: Basic network properties for our four networks H-ALL H-SIM REST CORE Number of nodes 11,100 1,706 8,227 88 Number of edges 56,807 8,655 24,730 865 Clustering coefficient 0.125 0.173 0.102 0.462 Diameter 13 9 16 3 Radius 7 5 8 2 Avg. degree 10.23 10.14 4.53 19.65 Avg. path length 3.69 3.48 4.53 1.87 Imperial College London Vuk Janjić vj11@imperial.ac.uk 3/17
  16. 16. Constructing the networks Table: Basic network properties for our four networks H-ALL H-SIM REST CORE Number of nodes 11,100 1,706 8,227 88 Number of edges 56,807 8,655 24,730 865 Clustering coefficient 0.125 0.173 0.102 0.462 Diameter 13 9 16 3 Radius 7 5 8 2 Avg. degree 10.23 10.14 4.53 19.65 Avg. path length 3.69 3.48 4.53 1.87 Imperial College London Vuk Janjić vj11@imperial.ac.uk 3/17
  17. 17. Constructing the networks Table: Basic network properties for our four networks H-ALL H-SIM REST CORE Number of nodes 11,100 1,706 8,227 88 Number of edges 56,807 8,655 24,730 865 Clustering coefficient 0.125 0.173 0.102 0.462 Diameter 13 9 16 3 Radius 7 5 8 2 Avg. degree 10.23 10.14 4.53 19.65 Avg. path length 3.69 3.48 4.53 1.87 Imperial College London Vuk Janjić vj11@imperial.ac.uk 3/17
  18. 18. Constructing the networks 3-core 2-core 1-core Figure: A three-level deep k-core decomposition of a network. Imperial College London Vuk Janjić vj11@imperial.ac.uk 3/17
  19. 19. Constructing the networks Table: Basic network properties for our four networks H-ALL H-SIM REST CORE Number of nodes 11,100 1,706 8,227 88 Number of edges 56,807 8,655 24,730 865 Clustering coefficient 0.125 0.173 0.102 0.462 Diameter 13 9 16 3 Radius 7 5 8 2 Avg. degree 10.23 10.14 4.53 19.65 Avg. path length 3.69 3.48 4.5 1.87 Imperial College London Vuk Janjić vj11@imperial.ac.uk 3/17
  20. 20. Constructing the networks Table: Basic network properties for our four networks H-ALL H-SIM REST CORE Number of nodes 11,100 1,706 8,227 88 Number of edges 56,807 8,655 24,730 865 Clustering coefficient 0.125 0.173 0.102 0.462 Diameter 13 9 16 3 Radius 7 5 8 2 Avg. degree 10.23 10.14 4.53 19.65 Avg. path length 3.69 3.48 4.5 1.87 Imperial College London Vuk Janjić vj11@imperial.ac.uk 3/17
  21. 21. Constructing the networks Table: Basic network properties for our four networks H-ALL H-SIM REST CORE Number of nodes 11,100 1,706 8,227 88 Number of edges 56,807 8,655 24,730 865 Clustering coefficient 0.125 0.173 0.102 0.462 Diameter 13 9 16 3 Radius 7 5 8 2 Avg. degree 10.23 10.14 4.53 19.65 Avg. path length 3.69 3.48 4.5 1.87 Imperial College London Vuk Janjić vj11@imperial.ac.uk 3/17
  22. 22. Outline Background Methods Data Constructing the networks Graphlets K-core decomposition The Core Diseasome Topological uniqueness Functional annotation Drug targets Computing the Core Diseasome Key cardio-vascular disease genes G-protein coupled receptors Imperial College London Vuk Janjić vj11@imperial.ac.uk
  23. 23. Topological uniqueness Maximum EC = 10.52% Algorithm executions 1-4,000 Edgecorrectness(%) 13 12 11 10 9 8 7 6 Imperial College London Vuk Janjić vj11@imperial.ac.uk 4/17
  24. 24. Functional annotation Statistics performed using: hypergeometric test H-ALL as the background model Benjamini-Hochberg False Discovery Rate correction for multiple hypothesis testing Imperial College London Vuk Janjić vj11@imperial.ac.uk 5/17
  25. 25. Functional annotation Statistics performed using: hypergeometric test H-ALL as the background model Benjamini-Hochberg False Discovery Rate correction for multiple hypothesis testing Enriched Molecular Function Gene Ontology (GO) terms enzyme binding, transcription factor binding, transcription regulator activity, DNA binding, promoter binding Imperial College London Vuk Janjić vj11@imperial.ac.uk 5/17
  26. 26. Functional annotation Statistics performed using: hypergeometric test H-ALL as the background model Benjamini-Hochberg False Discovery Rate correction for multiple hypothesis testing Enriched Molecular Function Gene Ontology (GO) terms enzyme binding, transcription factor binding, transcription regulator activity, DNA binding, promoter binding Enriched Biological Process GO terms (mostly regulatory) positive regulation of macromolecule metabolic process, positive regulation of cellular biosynthetic process, response to organic substance, regulation of cell proliferation, positive regulation of gene expression Imperial College London Vuk Janjić vj11@imperial.ac.uk 5/17
  27. 27. Functional annotation Table: Regulation of cell death and apoptosis enrichment. regulation of cell death regulation of apoptosis (GO:10941) (GO:42981) H-ALL 8.9% 8.8% H-SIM 19.9% (p = 8.59 × 10−60 ) 19.8% (p = 1.13 × 10−59 ) REST no enrichment no enrichment CORE 32.1% (p = 6.93 × 10−10 ) 29.8% (p = 1.1 × 10−8 ) Imperial College London Vuk Janjić vj11@imperial.ac.uk 6/17
  28. 28. Functional annotation Top 1% hubs contain only 9 (out of 185) apoptosis annotated proteins Imperial College London Vuk Janjić vj11@imperial.ac.uk 7/17
  29. 29. Functional annotation Top 1% hubs contain only 9 (out of 185) apoptosis annotated proteins These 9 are evenly split between H-SIM and REST (5 are in H-SIM and 4 in REST) Imperial College London Vuk Janjić vj11@imperial.ac.uk 7/17
  30. 30. Functional annotation Top 1% hubs contain only 9 (out of 185) apoptosis annotated proteins These 9 are evenly split between H-SIM and REST (5 are in H-SIM and 4 in REST) Cell death has no annotated proteins in the top 1% of hubs. Imperial College London Vuk Janjić vj11@imperial.ac.uk 7/17
  31. 31. Functional annotation Could the Core Diseasome be capturing genes causal to diseases for which we generally have no effective cure, including cancer, hematologic diseases, neurodegenerative diseases, progression of viral and HIV infection? Imperial College London Vuk Janjić vj11@imperial.ac.uk 8/17
  32. 32. Driver genes Genetic interactions are increasingly starting to show that a very small number of genetic changes may trigger disease onset. These mutations are usually called driver mutations. Ashworth A. et al., Cell, 145, 30–38, (2011). Imperial College London Vuk Janjić vj11@imperial.ac.uk 9/17
  33. 33. Driver genes We verify that CORE genes: are enriched in genetic interactions (GIs) 22 of them participate in 21 GIs within CORE (p = 10−16 ) 32 of them participate in 100 GIs total (including 59 genes outside of core) Imperial College London Vuk Janjić vj11@imperial.ac.uk 10/17
  34. 34. Driver genes We verify that CORE genes: are enriched in genetic interactions (GIs) 22 of them participate in 21 GIs within CORE (p = 10−16 ) 32 of them participate in 100 GIs total (including 59 genes outside of core) capture 15 driver genes (both known and predicted). Imperial College London Vuk Janjić vj11@imperial.ac.uk 10/17
  35. 35. Driver genes SIN3A NCOR1 HDAC5 PMLGATA1 CTBP1 SUMO1 RUNX1 SMARCB1SMARCC1 UBE2I ETS1 DAXX RUNX2 SMARCA4 CEBPA MYOD1 SMAD3 RARA HDAC4 NCOR2 SP1 EP300 HDAC3 RXRA MYC TP73 CEBPB KAT2B JUN CREBBPBRCA1 SMARCA2 SMAD4 POLR2A STUB1 SMAD2 NCOA2 CCND1 CDKN1A HIF1A MDM2 PARP1 CSNK2A1 RELA CAV1 ABL1 HSPA8 HSPA4 UBC HSP90AA1 PAK1 EGFR RAF1 MST1R ERBB2 KHDRBS1 CASP3 CHUK CTNNB1 ESR1 FOXO1RB1 AKT1 AR ESR2 PTPN11LYN PTK2B CRK CRKL KIT CBL PTPN6 PLCG1 LCK JAK2 PIK3R1INSR BCR EPOR IRS1 SHC1 PTPN1 IGF1R PTK2 BCAR1 PXN Imperial College London Vuk Janjić vj11@imperial.ac.uk 11/17
  36. 36. Drug targets Amongst the 22 genes participating in genetic interactions within CORE, there are 11 drug targets linked to 116 distinct drugs (p = 8.64 × 10−5) MDM2MDM2 JUNJUN RB1RB1 ARAR SMAD2SMAD2 NCOA2NCOA2 KAT2BKAT2B CCND1CCND1 ESR1ESR1 CTNNB1CTNNB1 CREBBPCREBBP Imperial College London Vuk Janjić vj11@imperial.ac.uk 12/17
  37. 37. Drug targets Amongst the 22 genes participating in genetic interactions within CORE, there are 11 drug targets linked to 116 distinct drugs (p = 8.64 × 10−5) MDM2MDM2 JUNJUN RB1RB1 ARAR SMAD2SMAD2 NCOA2NCOA2 KAT2BKAT2B CCND1CCND1 ESR1ESR1 CTNNB1CTNNB1 CREBBPCREBBP Out of these 11 drug targets, 3 are targeted by 23 or more drugs: ESR1 is targeted by 61 different drugs, AR by 40, and NCOA2 by 23. (the p-value of any target being hit by more than 22 drugs is 0.0017) Imperial College London Vuk Janjić vj11@imperial.ac.uk 12/17
  38. 38. Drug targets Amongst the 22 genes participating in genetic interactions within CORE, there are 11 drug targets linked to 116 distinct drugs (p = 8.64 × 10−5) MDM2MDM2 JUNJUN RB1RB1 ARAR SMAD2SMAD2 NCOA2NCOA2 KAT2BKAT2B CCND1CCND1 ESR1ESR1 CTNNB1CTNNB1 CREBBPCREBBP Out of these 11 drug targets, 3 are targeted by 23 or more drugs: ESR1 is targeted by 61 different drugs, AR by 40, and NCOA2 by 23. (the p-value of any target being hit by more than 22 drugs is 0.0017) 2 known driver genes in CORE are drug targets: RB1 and CTNNB1 Imperial College London Vuk Janjić vj11@imperial.ac.uk 12/17
  39. 39. Computing the Core Diseasome Breast cancer Prostate cancer Leukemia Yersinia infection Adenovirus infection Rheumatoid arthritis Embryoma Alzheimer's disease Systemic scleroderma Lymphoma Parkinson disease Melanoma Colon cancer Brain tumor Eating disorder Core Diseasome (88 genes) H-ALL Core (17 genes) H-SIM Core (12 genes) Linked via 57 intermediary genes and 175 edges Linked via 57 intermediary genes and 175 edges BCL2 BCL3 CARM1 CASP8 E2F1 GNB2L1 HSF1 IKBKB AHR ARNT CSK GAB2 HIPK2 MEN1 NG1 JAK1 KAT5 KRT18 MAPK14 MDM4 RBBP4 SMARCE1 TSC2 MYB NCK1 NEDD9 PIAS3 SKI SOS1 BCL2 BCL3 CARM1 CASP8 E2F1 GNB2L1 HSF1 IKBKB AHR ARNT CSK GAB2 HIPK2 MEN1 NG1 JAK1 KAT5 KRT18 MAPK14 MDM4 RBBP4 SMARCE1 TSC2 MYB NCK1 NEDD9 PIAS3 SKI SOS1 Imperial College London Vuk Janjić vj11@imperial.ac.uk 13/17
  40. 40. Outline Background Methods Data Constructing the networks Graphlets K-core decomposition The Core Diseasome Topological uniqueness Functional annotation Drug targets Computing the Core Diseasome Key cardio-vascular disease genes G-protein coupled receptors Imperial College London Vuk Janjić vj11@imperial.ac.uk
  41. 41. Key cardio-vascular disease genes Cardio-vascular disease (CVD) Imperial College London Vuk Janjić vj11@imperial.ac.uk 14/17
  42. 42. Key cardio-vascular disease genes Cardio-vascular disease (CVD) Interlogous Interaction Database (I2D), Jurisica Lab @ Toronto around 15,000 nodes and 173,000 interactions (60,000 predicted interactions) Imperial College London Vuk Janjić vj11@imperial.ac.uk 14/17
  43. 43. Key cardio-vascular disease genes Cardio-vascular disease (CVD) Interlogous Interaction Database (I2D), Jurisica Lab @ Toronto around 15,000 nodes and 173,000 interactions (60,000 predicted interactions) Study identifies 10 “Key CVD proteins” via clustering methods Imperial College London Vuk Janjić vj11@imperial.ac.uk 14/17
  44. 44. Key cardio-vascular disease genes Cardio-vascular disease (CVD) Interlogous Interaction Database (I2D), Jurisica Lab @ Toronto around 15,000 nodes and 173,000 interactions (60,000 predicted interactions) Study identifies 10 “Key CVD proteins” via clustering methods All 10 “key” CVD proteins captured by k-core decomp. of: the whole PPI network (p = 10−11 ) induced CVD network (p = 10−10 ) Imperial College London Vuk Janjić vj11@imperial.ac.uk 14/17
  45. 45. Key cardio-vascular disease genes SMARCC1 ETS1 SMARCB1 RUNX1 SMARCA4 RUNX2 NCOR1 SMAD3 CTBP1 HDAC5 KAT2B GATA1 UBE2I SIN3A JUN MYOD1 PML SUMO1 ESR2 EP300 HDAC4 NCOR2 RXRA CREBBP CHUK DAXX ESR1 RB1 CASP3 CSNK2A1 SMAD4 MYC SMAD2 HSP90AA1 HSPA8 ABL1 UBC CAV1 SMARCA2 LCK EGFR KHDRBS1 RAF1 MST1R PTPN6 PAK1 ERBB2 STUB1 JAK2 POLR2A TP73 HSPA4 CEBPA BRCA1 CEBPB AR AKT1 CTNNB1 PARP1 NCOA2 CCND1RELA HIF1A HDAC3 RARA MDM2 CDKN1A FOXO1 SP1 KIT CBL CRKL PLCG1EPOR PXN BCAR1 PTK2 IGF1R PTPN1 IRS1 SHC1INSR LYN PTK2B CRK PTPN11 PIK3R1 BCR 8 Key CVD genes 8 validated CVD gene predictions 2 non-valdated CVD gene predictions 11 drug targets 5 driver gene Sarajlic, A. et al., PLoS One, in press (2013) Imperial College London Vuk Janjić vj11@imperial.ac.uk 15/17
  46. 46. Outline Background Methods Data Constructing the networks Graphlets K-core decomposition The Core Diseasome Topological uniqueness Functional annotation Drug targets Computing the Core Diseasome Key cardio-vascular disease genes G-protein coupled receptors Imperial College London Vuk Janjić vj11@imperial.ac.uk
  47. 47. G-protein coupled receptors New unpublished interaction network of human G-protein coupled receptors (GPCRs) from Štagljar Lab (U-of-T) Imperial College London Vuk Janjić vj11@imperial.ac.uk 16/17
  48. 48. G-protein coupled receptors New unpublished interaction network of human G-protein coupled receptors (GPCRs) from Štagljar Lab (U-of-T) The whole GPCR network is basically a signal transduction “backbone” of the human PPI network — it’s wiring allows it to quickly reach all parts of the interactome Imperial College London Vuk Janjić vj11@imperial.ac.uk 16/17
  49. 49. G-protein coupled receptors New unpublished interaction network of human G-protein coupled receptors (GPCRs) from Štagljar Lab (U-of-T) The whole GPCR network is basically a signal transduction “backbone” of the human PPI network — it’s wiring allows it to quickly reach all parts of the interactome The “core” of this GPCR network has 68 interactions between 25 proteins Imperial College London Vuk Janjić vj11@imperial.ac.uk 16/17
  50. 50. G-protein coupled receptors New unpublished interaction network of human G-protein coupled receptors (GPCRs) from Štagljar Lab (U-of-T) The whole GPCR network is basically a signal transduction “backbone” of the human PPI network — it’s wiring allows it to quickly reach all parts of the interactome The “core” of this GPCR network has 68 interactions between 25 proteins Its “core” proteins primarily expressed in brain, and involved in a range of personality and behavioural disorders: attention deficit hyperactivity disorder, weight gain, bipolar disorder, antipsychotic agent-induced weight gain, attention deficit disorder / conduct disorder / oppositional defiant disorder, schizophrenia, weight loss, obesity, mood disorders, tardive dyskinesia, and personality traits. Imperial College London Vuk Janjić vj11@imperial.ac.uk 16/17
  51. 51. We’ve seen that. . . (i.e., take-home messages) A sub-network of the human PPI network exist, such that it’s topology is unique within that context and it captures disease genes, driver genes and their drug targets Imperial College London Vuk Janjić vj11@imperial.ac.uk 17/17
  52. 52. We’ve seen that. . . (i.e., take-home messages) A sub-network of the human PPI network exist, such that it’s topology is unique within that context and it captures disease genes, driver genes and their drug targets ...and it can be obtained purely computationally Imperial College London Vuk Janjić vj11@imperial.ac.uk 17/17
  53. 53. We’ve seen that. . . (i.e., take-home messages) A sub-network of the human PPI network exist, such that it’s topology is unique within that context and it captures disease genes, driver genes and their drug targets ...and it can be obtained purely computationally Usability of the “core” approach in identifying therapeutically relevant regions of the interactome in two case studies — Cardiovascular disease and G-protein coupled receptors Imperial College London Vuk Janjić vj11@imperial.ac.uk 17/17
  54. 54. Questions...

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