SBGN comprehensive disease maps at LCSB.

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Presentation made during the EISBM workshop, 13-15 June 2012 by Antonio del Sol (LCSB).

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SBGN comprehensive disease maps at LCSB.

  1. 1. Supported byProminent international speakers from h"p://workshop.eisbm.eu1
  2. 2. SBGN comprehensive disease maps at the LCSB Antonio del Sol Lyon June 14th, 2012
  3. 3. Luxembourg Centre for Systems Biomedicine (LCSB)18/06/12 2
  4. 4. LCSB: An Interdisciplinary Centre within the University LanguageFaculties Literature Science Law Humanitites Technology Economics Arts Communication Finance Education (FLSHASE) (FSTC) (FDET)Interdisciplinary Systems Biomedicine Centres (LCSB) Security, Reliability, Trust (SNT) 3
  5. 5. The new campus of the UL in Belval 4
  6. 6. The LCSB on the new campus in Belval 5
  7. 7. Luxembourg!Centre!for!Systems!Biomedicine!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!(LCSB)!!!!!Experimental!! Computa2onal!!!!!!!!!!!Biology! !!!!!!!!!Biology! !!!!!!LCSB!!!!!!!!!!Technical!! !!!!!!!!!!Clinical!!!!!!!!Pla8orms! !!!!!!Research!! !!!!!!!!
  8. 8. The interdisciplinary nature of the LCSBTechnology Theory Transcriptomics! Bioinformatics !!!!Proteomics! !!Metabolomics! Computational Biology Modelling and Simulation !Animal!Models! Public Human!Gene2cs! !!!!!!!!!!!!!!!!!!!!!!!!!! ! Health Parkinson`s Disease Chemical!Biology! Experimental Biomedicine Imaging!!!! Gene-Environment Interactions Experiments 18/06/12 7
  9. 9. Thematic focusComputer! Cells! Mice! Pa2ents! Families! ! ! Network!models!of!diseases:!Inference!and!analysis! 8
  10. 10. Computa(onal+Biology+Group!
  11. 11. Modeling!biological!processes!and!understanding!diseases!at!different!levels:!! • Molecules!( proteins,!nucleic!acids)! ! ! Molecular!analysis!of!structural/dynamical!changes!in!biomolecules!! !!!affec6ng!their!func6on! ! Analysis!of!the!effects!of!disease?causing!molecular!varia6ons!in!cellular!pathways! !• !Molecular!networks!(gene!regulatory,!PPI,!metabolic!networks)!! !! !  Network!inference!and!integra6on!from!experimental!data! !  Network!analysis!and!modeling?!topology!and!dynamics! !  Diseases!as!network!perturba6ons! ! • Cell!popula6ons!(cancer!cells,!neural!stem!cells,!astrocytes,!neurons)! ! !  Modeling!cell!popula6on!dynamics! !  Network!perturba6on!leading!to!cellular!transi6ons! !  Cell?cell!interac6ons! !  Disease?related!perturba6on!of!cellular!popula6ons! ! ! 10
  12. 12. Computational approaches to study function of macromolecules Prediction of Chemokine receptor dimerization Hernandez P, Serrano A, Juan D, del Sol A, Valencia A, Martinez C Nature Immunology (2004) 5: 216-223
  13. 13. Computational approaches to study function of macromolecules Network determinants and key amino acids for allosteric communications Central amino acids identify key residues for the allosteric signaling Central residue del Sol A, Fujihashi H, Amoros D, Nussinov R, Molecular Systems Biology 2006, Tsai C, Antonio del Sol, Ruth Nussinov JMB 2008, 378: 1-11
  14. 14. Molecular!analysis!of!disease?related!perturba6ons ! prion!disease! !Wuthrich K et al. NMR solution structure of the human prion protein. PNAS, 2000, 97: 145-150Nelson R, Sawaya MR, Balbirnie M, et al. Structure of the cross-beta spine of amyloid-like fibrils. Nature.2005;435(7043):773-8.
  15. 15. Network!Dynamics!! !Network Stability Stable!state!Network PerturbationsNetwork Controllability Driver Nodes
  16. 16. Modeling!mouse!neural!stem!cells!differen6a6on!into!astrocytes:! understanding!astrocyte!dedifferen6a6on!under!specific!s6muli! Project in collaboration with Prof Noel J Buckley Department of Neuroscience Institute of Psychiatry King’s College London FBS! NSCs!CTX12! Astrocytes! EGF+FGF?2! BMP4! NSCs!CTX12! Astrocytes! EGF+FGF?2! Goals: -Constructing a gene regulatory network based model to describe differentiation and dedifferentiation of astrocytes. -Identify candidate genes controlling and inducing both differentiation 15 and dedifferentiation.
  17. 17. Role!of!MicroRNAs!in!regula6ng!Epithelial!to!Mesenchymal!Transi6on! Project in collaboration with Evelyne Friederich, Life Sciences Research Unit, University of Luxembourg Goals: -  Introduce a dynamic model for a gene regulatory network describing EMT -  Elucidation of the role of novel microRNAs in EMT 16
  18. 18. Goals:-  Network model describing molecular changes underlying normal and Alzheimer’s disease aging 17-  Understand similarities and differences between normal and Alzheimer’s disease aging
  19. 19. Priori6sa6on!of!gene6c!varia6ons!involved!in!mechanisms!of!epilepsy!Epilepsy!is!a!channelopathy!of!oUen!complex!gene6c!background! Goals: -  Identify and validate the genetic risk factors for Idiopatic Epilepsy 18 -  Construction of a pipeline for identifying and characterizing disease-related SNPs
  20. 20. Parkinson´s Disease J. Parkinson 181719
  21. 21. Parkinson´s disease!MotorLsymptoms!in!Parkinson´s!Disesase!!•  R ! es2ng!tremor!•  R ! igidity!•  B ! radykinesia!•  P ! ostural!instability!!!!NonLMotorLsymptoms!in!Parkinson´s!Disesase!!•  A ! nosmia!•  G ! astrointes2nal!mobility!problems!•  S ! leep!disturbances!•  S ! ympathe2c!denerva2on!•  A ! nxiety!•  ! epression! D! 20
  22. 22. Thematic focus Mol.- & CellularGenetics & Genomics Biology Parkinson´s DiseaseComputational Biology Clinical Translation 21
  23. 23. Initial focus on two hallmarks of PDMitochochondrial dysfunction Neuroinflammation 22
  24. 24. Bioinformatics and Computational Biology Reinhard Schneider Antonio del Sol 23
  25. 25. Parkinson’s Disease map Experimental BiologyBioinformatics Computational Biology Design Results Enrichment Predictions Mining Model 24
  26. 26. Constructing a PD-disease network•  Literature and expertise-based curation•  Map annotation•  Network analysis•  Text mining-based map enrichment•  Integration of sequencing data with map M. Ostaszweski, C. Trefois, P. Antony, R. Balling I. Crespo, A. del Sol E. Glaab, G. Vanketta, R. Schneider Cooperation with the team of 25 H. Kitano (Japan)
  27. 27. Knowledge exploration26
  28. 28. Knowledge exploration o  Visualization o  Annotation o  Text mining o  Network analysis and modeling o  Simulations27
  29. 29. PD map annotationMotivationEnrichment of PD map with annotationsfrom biological databases 28
  30. 30. Databases 1 1 29
  31. 31. PD map after annotation30 30
  32. 32. Data Mining Tools for finding new PD genes and CurationI. !Sta6s6cal!analysis!of!the!full!text!PD!related!ar6cles.!! !Co?occurrence?based!strategy.!II. ! !Lexico?syntac6c!refinement!of!sta6s6cal!results! •  From co-occurrence to typed relationship extractions: –  “PINK1 binds and colocalizes with TRAP1 in the mitochondria and phosphorilates TRAP1 both in vitro and in vivo.” (17579517) •  Data curation: detection of negative & hypothetical context –  “GDNF and PSPN, but not NRTN, induce neurite outgrowth of dopaminergic neurons in vitro” (14699966) –  “One way to resolve this apparent contradiction is to place LRRK2 genetically upstream of deposited proteins such as SNCA or tau, implying that the same initial mutation might then result in different pathological outcomes depending on the course the disease takes” (20696314) Monday, March 12, 2012 31
  33. 33. PD Microarray datasetsStudy Cell type Conditions PlatformC. R. Scherzer et al., PNAS, Whole blood, early PD PD (50), healthy (21), U133A2007 stage other neuro. dis. (33)Y. Zhang et al., Am J Med Genet multiple brain regions, post PD (40), healthy (53) U133AB Neuropsychiatr Genet, 2005 mortemT. G. Lesnick et al., PloS Genet, SN, post mortem PD (16), healthy (9) U133 Plus 2.02007L. B. Moran et al., Neurogenetics, SN + frontal gyrus, post PD (29), healthy (18) U133A,2006 mortem U133B Normalization: if not pre-normalized " GC-RMA (Bolstad et al., 2005) Sample filtering: only use SN, post mortem samples for integrative analysis Monday, March 12, 2012 32
  34. 34. Analysis of differentially expressed genesCross-study analysis of differentially expressed genes1) Empirical Bayes moderated t-statistic (G. K. Smyth, 2004)2) Marot et al. (2009) inverse weighted normal method to combine p-values3) Multiple testing adjustment (Benjamini & Hochberg, 1995), cut-off: 0.05" 1656 differentially expressed genes (DEGs) Healthy PD Differentially expressed genes First observations: •  62% of DEGs are down- regulated •  Among 205 mitochondrial DEGs: 76% down-regulated •  Among 31 proteasomal DEGs: 90% down-regulated Samples Monday, March 12, 2012 33
  35. 35. Visualizing the data on the PD pathwaysVisualization on PD map:•  Project transcriptomes from the meta analysis of the PD microarray studies onto PD map, using a colour gradient coding: Red = down-regulated Green = up-regulated The darker the colour tone, the higher the fold change. PD pathway map Monday, March 12, 2012 34
  36. 36. Network Analysis of the PD map: Overview Content Analysis A prioriknowledge Network inference Structural Analysis G = (V , E ) Computational Biology Group Experimental Analysis Static Model xk = f ( xk +1 ) Dynamic Model & x = f ( x, p ) 35
  37. 37. Content Analysis: Functional Modules 1.  Calcium dependent signalling 18! 2.  Glycolysis 3.  Apoptosis excution 1! 4.  Microglia 24! 19! 5.  Astrocyte 17! 16! 6.  7.  8.  NF-kB signalling ROS-Dependent apoptosis ROS-induced JNK pathway 9.  HTRA2 involvement of apoptosis ! 23 14! 15! 10.  11.  UPS Autophagy and Mitophagy 12.  Fatty acid and keton body 2! 21! 20! 13! 11! 13.  metabolism PINK1 and Parkin Pathway 22! 14.  15.  16.  Apoptosis excution ROS metabolism alpha-synuclein and Lewy Body 12! 10! 17.  18.  Dopamine metabolism Dopamine secretion and recycling 3! 6 8! 7! 9 19.  20.  21.  Apoptosis execution Mitochondrial Outer Membrane Mitochondrial Inner Membrane 22.  Fatty acid & keton body 4! 5! metabolism 23.  ROS formation and removal 24.  Nucleus 36
  38. 38. Structural!analysis!of!PD!network! Goals: •  Identify species/ components that are essential for disease pathology. •  Develop general purpose algorithms that analyses the structure of cellular networks, to understand the genotype-phenotype relationship
  39. 39. Network Structure Formulation•  Adjacency/connectivity matrix is inferred from the Network model x1 x2 x3 x4 x5 x6 x1 0 1 0 0 0 0 x2 0 0 0 0 1 1 x3 0 0 0 0 1 0 x4 0 0 0 0 0 1 x5 0 0 0 0 0 1 x6 0 0 0 0 0 0 Network Model Adjacency Matrix 38
  40. 40. Structure Analysis (Graph theoretic approach) •  Identifying the global properties of the PD network x1 –  Characteristic distance, path length, x2 x3 x4 x5 x6 degree distribution, clustering x1 0 1 0 0 0 0 coefficient, matching Index, Eigen values and spectral properties x2 0 0 0 0 1 1 x3 0 0 0 0 1 0 •  Centrality analysis x4 0 0 0 0 0 1 –  Degree centrality, betweeness centrality, x5 0 0 0 0 0 1 average neighbourhood degree, radiality, integration, katzu index and page rank x6 0 0 0 0 0 0 •  Motif & module identification Adjacency Matrix •  Simple path analysis Junker, BH and Schreiber, F (2008) Analysis of Biological Networks 39
  41. 41. Structure Analysis (Gene prioritization studies) •  Simple path analysis –  Output: To predict the essentiality of components •  Simple paths (SP’s) –  All set of nodes that can perform signal transduction from one node to another –  Given a input-output pair (like x1 – x6) –  SP’s are •  x1 – x2 – x6 •  x1 – x2 – x5 – x6 Wang,RS. (2011) BMC Syst. Biol., 5: 44 40
  42. 42. Structure Analysis (Gene prioritization studies) •  Based on an input-output pair all the other nodes in the sub-network are scored N SP ( G ) − N SP ( GΔv ) E SP ( v ) = N SP ( G ) where E ESM ( v ) − Essentiality of the vertex v N ESM ( G ) − Number of SP s of original graph G N ESM ( GΔv ) − Number of SP s of perturbed graph GΔv Node!Name! Essen2ality!Scores! Node!Name! Essen2ality!Scores! x1 1! x4! 1! x2! 1! x5! 0.5! x3! 0.5! Wang,RS. (2011) BMC Syst. Biol., 5: 44 41
  43. 43. Structure Analysis (Gene prioritization studies) •  Considering all the 1165 nodes (excluding phenotypes) as inputs and 6 hallmarks as outputs we can arrive at 1165 x 6 ranking sets Hallmarks Alpha-synuclein Mitochondrial aggregation dysfunction For Ex1 (Given caspase-6 – . Alpha synuclein Nodes aggregation pair)Input Nodes caspase-6 Ex1 Ex1109 . BAD:BCL-2 ESP BAD:BCL-2 Ex2 Ex1110 . tBID:BCL-2 ESP tBID:BCL-2 Ex3 Ex1111 . caspase-3 ESP caspase-3 Ex4 Ex1112 . Hallmarks NNT dimer Ex5 Ex1113 . NNT dimer ESP . . . . . . 42
  44. 44. Structure Analysis (Gene prioritization studies) Hallmarks •  Combining all the row rankings of each column will give the Alpha-synuclein Mitochondrial importance of species wrt the aggregation dysfunction given hallmark •  Combining the column rankingsNodes of each row will give the caspase-6 Ex1 Ex1109 Ex2217 Ex3325 . importance of species wrt the BAD:BCL-2 Ex2 Ex1110 Ex2218 Ex3326 . selected target tBID:BCL-2 Ex3 Ex1111 Ex2219 Ex3327 . •  But still the question remains caspase-3 Ex4 Ex1112 Ex2220 Ex3328 . what is the good target and NNT dimer Ex5 Ex1113 Ex2221 Ex3329 . what is the interesting . . . . . . hallmark? 43
  45. 45. Centrality analysis and Simple path analysis•  Simple path analysis: –  Top 10% includes metabolites of citric acid cycle and glycolysis pathway –  Next group forms proteins and complexes involved in apoptosis•  Centrality analysis: –  VDAC1 and apoptosome to be the key components that are highly ranked –  AMPA receptors involved in long-term potentiation mechanism of synaptic plasticity and transcription factors like CREB1.•  Both analysis: –  Highlights many small molecules such as reactive oxygen species (e.g. hydrogen peroxide) 44
  46. 46. Comparison Comparison with text mining and pathway enrichment analysis Text Mining Enrichment Analysis Mitochondrial dysfunction Neuroinflammation All Hallmarks Protein misfolding Synaptic transmission dysfunction Failure of protein quality control 0.6 0.4 0.2 0 Cosine correlation distance 45
  47. 47. Dynamical!model!of!PD!network! Goals: •  to develop a dynamical boolean/fuzzy model of PD pathology, based on the PD map •  to understand the mitochondrial 21! dysfunction mechanism 20! of PD •  to generate hyposthesis for in house experiments.
  48. 48. ROSLac2vated! Regulatory! Network!!Extrac2on!of!subLnetwork:!!ROSLac2vated!Regulatory!Network!!
  49. 49. ROSLac2vated!Regulatory!Network:!Objec2ve!func2ons!(sensing!ROS!and!defense!against!excessive!ROS!genera2on)!! S! 1! ROS!“surgical”! 2! An6oxidants,!mechanism! SOD,!etc.! MPTP!(killing! “preven2ve”!mitochondria)! mechanism!(increasing! ROS!consump2on!Regulatory!Network!
  50. 50. ROSLac2vated!Regulatory!Network:!General!structure!(simply)!! 5.!This!affects!mitochondria!and! 1.!Mitochondria! ROS!genera2on!and! generates!ROS! ROS! consump2on! +!Protein!X!(inac2ve)! Protein!X!(ac2ve)! 4.!Concentra2ons!of! other!proteins!changes!2.!ROS!may!ac2vate/!deac2vate!different!proteins!(ROS!sensing)! Protein!Y!3.!Ac2vated!protein!may! mRNA!Y!regulate!transcrip2on! +! Gene!Y!(inac2ve)! Gene!Y!(ac2ve)!
  51. 51. ROSLac2vated!Regulatory!Network:!towards!a!kine2c!model!! Many!feed!forward! and!feedback!loops!A.!Kolodkin!in!collabora2on!with!N.!Brady!!and!many!!people!involved:! R.!Balling,!A.!del!Sol,!K.! Fujita,!M.!Ostaszewski,!Y.! Matsuoka,!!S.!Ghosh,!E.! Glaab,!C.!Trefois,!I.! Crespo,!T.!M.!Perumal,!W.! Jurkowski,!P.!Antony,!N.! Diederich,!M.!Budni,!A.! Kodama,!V.!P.! Satagopam,!S.!Eifes,!R.! Schneider,!H.!Kitano,!!H.! Westerhoff,!V!Simeonidis! and!many!others!!
  52. 52. ROS!kine2c!model!might!be!useful:!examples!Simula2on!of!PD!development!In!PD:!!•KEAP1!(up?regulated!in! These!perturba2ons!cause!!in!the!model!the!PD!vs.!control)! increase!of!ROS!and!mitochondria!loss:!•PINK1!(down?regulated)!•PARK7!(down?regulated)!•VDAC1!(down?regulated)!•SQSTM1(p62)!(up?regulated)! (data+from+Enrico+Glaab)+Synergy!between!down!regula2on!of!VDAC1!and!PARK7! 60%!increase! 20%!increase! 120%!increase!PARK7!is!down! VDAC1!is!down! PARK7!&!VDAC1!are!down!
  53. 53. LCSB SBIComputational Biology Group Hiroaki Kitano (Group Leader) Antonio del Sol (Group leader) Kazuhiro Fujita Ghosh Samik Wiktor Jurkowski Yukiko Matsuoka Thanneer Malai Perumal Isaac Crespo Bioinformatics core Reinhard Schneider (Group Leader) ISB Fellow Venkata Satagopam Alexey Kolodkin Maria Briyukov Enrico Glaab Serge EifesExperimental Biology Group Rudi Balling (Group leader) Christophe Trefois Paul Antony Manuel Buttini
  54. 54. Thanks for your attention!

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