Alternative paths in hiv 1 targeted human signal transduction pathways
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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

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


Human immunodeficiency virus 1 hiv 1

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


Hiv 1 life cycle

HIV-1 Life Cycle

Peterlin and Trono Nature Rev. Immu.(2003) 3: 97-107

Communication between HIV-1 and human host is essential


Outline

Outline

  • Aim 1. Define interactome

    • Predictions of HIV1,human protein interactions (Background)

  • Aim 2. From interactions to function

    • This paper


Aim 1 define interactome

Aim 1: Define Interactome

  • Identify network of interactions between HIV-1 and human proteins

    • Rank-order / stratify known interactions

    • Predict new interactions


Our approach supervised learning

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


The data source

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.


Types of interactions reported

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


Training and testing data

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


Human interactome features

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


Alternative paths in hiv 1 targeted human signal transduction pathways

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


Features 35

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)


Feature importance

Feature Importance


Prediction of specific interactions

Prediction of specific interactions

www.cs.cmu.edu/~HIV/hivPPI.html


Aim 2 from interactions to functions

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


Opportunity

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


New pathway analysis approach

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


  • Approach

    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)


    Signal transduction pathway data

    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/


    Hiv 1 targeted pathways

    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)


    Alternative paths

    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:


    Alternative paths1

    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:


    Generation of second messenger pathway in nci pid

    Generation of Second Messenger Pathway in NCI PID


    An hiv targeted path

    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


    Cholesterol biosynthesis pathway

    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


    Cholesterol biosynthesis a new anti hiv drug discovery pathway

    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.


    Summary

    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


    Acknowledgements

    Acknowledgements

    Oznur Tastan

    Jaime G. Carbonell

    Pittsburgh Center for HIV Protein Interactions

    www.hivppi.pitt.edu

    Sivaraman Balakrishnan


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