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Alternative paths in HIV-1 targeted human signal transduction pathways

INCOB, Singapore, September 11, 2009. Alternative paths in HIV-1 targeted human signal transduction pathways. Judith Klein-Seetharaman Associate Professor Department of Structural Biology University of Pittsburgh & Language Technologies Institute Carnegie Mellon University.

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Alternative paths in HIV-1 targeted human signal transduction pathways

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  1. INCOB, Singapore, September 11, 2009 Alternative paths in HIV-1 targeted human signal transduction pathways Judith Klein-Seetharaman Associate Professor Department of Structural Biology University of Pittsburgh & Language Technologies Institute Carnegie Mellon University

  2. Human Immunodeficiency Virus-1 (HIV-1) Global Summary of AIDS epidemic, December 2007 Global Summary of AIDS epidemic, December 2007 Global Summary of AIDS epidemic, December 2007 Global Summary of AIDS epidemic, December 2007 • Causative agent of AIDS • Destroys the immune system • Leads to opportunistic infections & malignancies • Current antiviral therapy • Not accessible to everyone • Cannot eradicate HIV from the body • Drug resistance problems • Side effects • No vaccine Number of people living with HIV in 2007 AIDS related deaths in 2007 Total Children under 15 years Total Children under 15 years 33 million 2 million 2.0 million 270 000 HIV-1 drug discovery needed

  3. HIV-1 Life Cycle Peterlin and Trono Nature Rev. Immu.(2003) 3: 97-107 Communication between HIV-1 and human host is essential

  4. Outline • Aim 1. Define interactome • Predictions of HIV1,human protein interactions (Background) • Aim 2. From interactions to function • This paper

  5. Aim 1: Define Interactome • Identify network of interactions between HIV-1 and human proteins • Rank-order / stratify known interactions • Predict new interactions

  6. Our approach: supervised learning • HIV-1 human protein pair is described with a feature vector and a class label : Given data learn a function that wouldmap feature space into one of the two classes: Each feature summarizes a biological information Tastan, O., Qi, Y., Carbonell, J. and Klein-Seetharaman (2009) Prediction of Interactions Between HIV-1 and Human Proteins by Information Integration, Proc. Pacific Symp. Biocomputing 14, 516-527

  7. The Data Source http://www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions • NIAID database curated from literature Fu W, Sanders-Beer et al. (2009) Nucleic Acids Res. 37, D417-22.

  8. Types of Interactions Reported Group 1: more likely direct Group 2: could be indirect acetylated by, acetylates, binds, cleaved by, cleaves, degraded by, dephosphorylates, interacts with, methylated by, myristoylated by, phosphorylated by, phosphorylates, ubiquitinated by activated by, activates, antagonized by, antagonizes, associates with, causes accumulation of, co-localizes with, competes with, cooperates with ... Keywords: “Nef binds hemopoietic cell kinase isoform p61HCK” 1063 interactions, 721 human proteins, 17 HIV-1 proteins 1454 interactions, 914 human proteins, 16 HIV-1 proteins www.hivppi.pitt.edu HIV-1 protein Human protein

  9. Training and Testing Data Group 1, the more likely direct interactions The ‘interaction’ class: 1063 interactions, 721 human proteins, 17 HIV-1 proteins The ‘non-interaction’ class: Select randomly from the pairs that are not reported in NIAID database 100:1 interacting vs. non-interacting pairs

  10. Human Interactome Features Calmodulin Nef • Making use of human protein protein interaction knowledge: Mimicry of human interaction partners NAP-22/CAP-23 The N-termini resemble and are both myristoylated • Sequence • Post translational modification • Cellular location • Molecular process • Molecular function

  11. Human Interactome Features • Making use of human protein protein interaction knowledge: Human protein’s topological properties in the human protein interaction network Degree Number of neighbors Clustering coefficient The extent the neighbors are connected with each other Betweenness Centrality The fraction of shortest paths pass through the node

  12. Features (35) Differential gene expression in HIV infected vs uninfected cells (4) Human protein expression in HIV-1 susceptible tissues (1) Similarity of the two proteins in terms of (4) Cellular location Molecular process Molecular function Sequence • HIV-1 protein type (17) • Motif-ligand feature (1) • Human PPI interactome features (8)

  13. Feature Importance

  14. Prediction of specific interactions www.cs.cmu.edu/~HIV/hivPPI.html

  15. Aim 2: From Interactions to Functions Long-Term Goal: Drug Discovery • Functionally relevant human proteins are not always direct interactors: • Link interactions to functions • Identify which signal-transduction pathways HIV-1 targets • 304 cellular proteins in Ott Rev Med Bio (2008) 17: 159-75 • 273 genes in Brass et al, Science (2008) 319: 921-6 • 295 genes in Konig et al. Cell (2008) 1: 49-60 • 291genes in Zhou et al. Cell Host Microbe.(2008)4:495-504

  16. Opportunity HIV-1 targets human hub proteins HIV-1 human interactions Randomly paired interactions Number of human partners Degree,d • Epstein–Barr virus targets high degree human proteins Calderwood et al., PNAS (2007) 104: 7606-11 • Pathogens tend to interact with host proteins with high degrees and betweenness centrality • Dyer et. al. PLoS Pathog (2008) 4, e32

  17. New Pathway Analysis Approach • Opportunity: • HIV-1 has to be minimalistic: a lot of work with just 9 genes • Human host signal transduction pathways are robust: many proteins are redundant • Idea: • Identify alternate pathways

  18. Approach • Identify potentially HIV-1 targeted pathways • Define paths: going from a start point (i.e. no edges going in to the node) to an end point (i.e. no edges leaving the node). • Find simple paths, HIV-1 targeted paths, and alternate paths to the end points. • Supplement with functional information • Drug targets from DrugBank: www.drugbank.ca • siRNA genes: Brass et al., Science (2008) 319: 921-6; Konig et al., Cell (2008) 1: 49-60; Zhou et al. Cell Host Microbe.(2008)4:495-504)

  19. Signal Transduction Pathway Data • Find the points at which HIV-1 targets human signal transduction pathways 1Matthews et al. (2009) “Reactome knowledgebase of human biological pathways and processes” Nucleic Acids Research. http://www.reactome.org/ 2Schaefer et al. (2008) “PID: the pathway interaction database” Nucleic acids research. http://pid.nci.nih.gov/

  20. HIV-1 targeted pathways • The larger the pathways, the more proteins targeted • HIV proteins target small & large pathways • Top-ranked degradation pathways • 225 of 453 pathways targeted by >1 interaction • 277 of 453 pathways targeted by at least one Group 1 interaction sorted by known interactions (Group 1)

  21. Alternative paths • Example pathways with alternate paths that contain at least one HIV-1 target, at least one drug target and at least one si-RNA target:

  22. Alternative paths • Example pathways with alternate paths that contain at least one HIV-1 target, at least one drug target and at least one si-RNA target:

  23. Generation of Second Messenger Pathway in NCI PID

  24. An HIV targeted path • CD3E: CD3epsilon -TCR complex • DT: P07766 • HIV target according to G1/pred: P07766 • CD3D: CD3delta -TCR complex • HIV target according to G1/pred: P04234 • CD3G: CD3gamma -TCR complex • HIV target according to G1/pred: P09693 • ZAP70: zeta-chain (TCR) associated protein kinase 70kDa • DT: P43403 • HIV target according to G1/pred: P43403 • ITK: IL2-inducible T-cell kinase • DT: Q08881 • CD4: CD4 molecule • HIV target according to G1/pred: P01730 • siRNA: P01730 • LCK: lymphocyte-specific protein tyrosine kinase • HIV target according to G1/pred: P06239 • CD247 CD247 molecule • HIV target according to G1/pred: P20963 • LCP2: lymphocyte cytosolic protein 2 (SH2 domain containing leukocyte protein of 76kDa) • HIV target according to Oznur: Q13094 • siRNA: Q13094 • PLCG1: phospholipase C, gamma 1 • HIV target according to Oznur: P19174 HIV-1 targets (Group 1) Drug targets siRNA gene HIV-1 and drug target HIV-1 target and siRNA gene

  25. Cholesterol Biosynthesis Pathway HIV target according to our predictions: P37268 / FDFT1 Description: farnesyl-diphosphate farnesyltransferase 1 siRNA: Q01581 / HMGCS1Description: 3-hydroxy-3-methylglutaryl-Coenzyme A synthase 1 (soluble) Drug target: P48449 / LSS Description: lanosterol synthase (2,3-oxidosqualene-lanosterol cyclase) Drug target: Q14534 / SQLE Description: squalene epoxidase

  26. Cholesterol Biosynthesis: A new anti-HIV Drug Discovery Pathway? • AIDS patients are at increased risk for arthrosclerosis • HIV Nef inhibits cholesterol exporter • Cholesterol accumulates in HIV-infected cells Mujawar Z, Rose H, Morrow MP, Pushkarsky T, Dubrovsky L, et al. (2006) Human immunodeficiency virus impairs reverse cholesterol transport from macrophages. PLoS Biol 4: e365.

  27. Summary • Aim 1. Define Interactome • Collected data from multiple biological information sources and encoded as features • Developed a model to predict HIV-1,human protein interaction network. Predictions available at: • Aim 2. From Interactions to Function – Drug Discovery • HIV-1 targets human hubs • HIV-1 targets many interaction partners of functionally relevant (siRNA) genes • Mapped known and predicted interactions to signal transduction pathways: HIV-1 targets many pathways • Combining path identification, drug target, siRNA and HIV-1 target information yields experimentally testable hypotheses on putative anti-HIV intervention routes http://www.cs.cmu.edu/~oznur/hiv/hivPPI.html www.hivppi.pitt.edu

  28. Acknowledgements Oznur Tastan Jaime G. Carbonell Pittsburgh Center for HIV Protein Interactions www.hivppi.pitt.edu Sivaraman Balakrishnan

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