Alternative paths in HIV-1 targeted human signal transduction pathways

Judith Klein-SeetharamanAssociate Professor

Department of Structural BiologyUniversity of Pittsburgh

&Language Technologies Institute

Carnegie Mellon University

INCOB, Singapore, September 11, 2009

Human Immunodeficiency Virus-1 (HIV-1)• 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 livingNumber of people livingwith HIV in 2007with HIV in 2007

AIDS related deathsAIDS related deathsin 2007 in 2007

33 million 33 million 2 million2 million

2.0 million 2.0 million 270 000270 000

Global Summary of AIDS epidemic, December 2007Global Summary of AIDS epidemic, December 2007Global Summary of AIDS epidemic, December 2007Global Summary of AIDS epidemic, December 2007Global Summary of AIDS epidemic, December 2007Global Summary of AIDS epidemic, December 2007Global Summary of AIDS epidemic, December 2007Global Summary of AIDS epidemic, December 2007TotalTotalChildren under 15 yearsChildren under 15 years

TotalTotalChildren under 15 yearsChildren under 15 years

HIV-1 drug discovery needed

HIV-1 Life Cycle

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

Communication between HIV-1 and human host is essential

Outline

• Aim 1. Define interactome– Predictions of HIV1,human protein

interactions (Background)

• Aim 2. From interactions to function– This paper

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

• HIV-1 human protein pair is described with a feature vector and a class label :

Given data learn a function that would map feature space into one of the two classes:

Each feature summarizes a biological information

:f X Y

( , ) {'Interact','Not Interact'}ix y y

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

• NIAID database curated from literature

http://www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions

Fu W, Sanders-Beer et al. (2009) Nucleic Acids Res. 37, D417-22.

Types of Interactions Reported

• Group 1: more likely direct

• Group 2: could be indirect

Keywords: “Nef binds hemopoietic cell kinase isoform p61HCK”

www.hivppi.pitt.edu

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 ...

1063 interactions, 721 human proteins, 17 HIV-1 proteins

1454 interactions, 914 human proteins, 16 HIV-1 proteins

HIV-1 protein Human protein

Training and Testing Data

Group 1, the more likely direct interactions

1063 interactions, 721 human proteins, 17 HIV-1 proteins

The ‘interaction’ class:

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

NefCalmodulin

NAP-22/CAP-23

The N-termini resembleand are both myristoylated

• Making use of human protein protein interaction knowledge: Mimicry of human interaction partners

{ , }1 2( , ) max ( , )neigh pairwise

k S k kjf i j f i k

Sequence

Post translational modification

Cellular location

Molecular process

Molecular function

vkNumber of neighborsDegree

Clustering coefficient

Betweenness Centrality

The extent the neighbors areconnected with each other

2

( 1)v

v v

n

k k

,,

( )uw

u w V uwu w v

v

The fraction of shortest paths pass through the node

Human Interactome Features• Making use of human protein protein interaction

knowledge: Human protein’s topological properties in the human protein interaction network

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

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

Aim 2: From Interactions to Functions

• Functionally relevant human proteins are not always direct interactors:

• Link interactions to functions– Identify which signal-transduction pathways HIV-1

targets

Long-Term Goal: Drug Discovery

• 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

Genes Interactors Genes Interactors Genes Interactors 0.51 0.20 3372 246 46 1054 77 422 73 11010.37 0.29 1942 101 14 435 21 182 26 4560.26 0.36 1440 48 5 208 11 99 9 2100.18 0.41 1085 17 2 97 7 54 5 980.13 0.47 622 8 1 49 4 28 0 510.09 0.47 279 4 0 25 2 14 0 23

Recall Brass et al. siRNA screen Zhou et al. screenKonig et al. screenin VirionPrecision Pairs

Opportunity

• Epstein–Barr virus targets high degree human proteinsCalderwood 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

Nu

mb

er

of

hu

man

part

ners

Degree,d

HIV-1 human interactionsRandomly paired interactions

HIV-1 targets human hub proteins

New Pathway Analysis Approach

• Opportunity:– HIV-1 has to be

minimalistic:

a lot of work with just 9 genes

Receptors

Effectors

Ligands

HIV protein Hubs

– Human host signal transduction pathways are robust: many proteins are redundant

• Idea:– Identify alternate pathways

Approach

1. Identify potentially HIV-1 targeted pathways2. 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).

3. Find simple paths, HIV-1 targeted paths, and alternate paths to the end points.

4. 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

Reactome1 NCI PID2

Initial number of pathways 823 132

Pathways discarded:

a) Insufficient detail

b) Subsets of other pathways

150

393

0

9

Final number of pathways 330 123

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/

• Find the points at which HIV-1 targets human signal transduction pathways

HIV-1 targeted pathways

sorted by known interactions (Group 1)

• 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

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 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

An HIV targeted path

HIV-1 targets (Group 1)

Drug targets

siRNA gene

HIV-1 and drug target

HIV-1 target and siRNA gene

• 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

Cholesterol Biosynthesis Pathway

Drug target: Q14534 / SQLE Description: squalene epoxidase

Drug target: P48449 / LSS Description: lanosterol synthase (2,3-oxidosqualene-lanosterol cyclase)

HIV target according to our predictions: P37268 / FDFT1 Description: farnesyl-diphosphate farnesyltransferase 1

siRNA: Q01581 / HMGCS1 Description: 3-hydroxy-3-methylglutaryl-Coenzyme A synthase 1 (soluble)

Cholesterol Biosynthesis: A new anti-HIV Drug Discovery Pathway?

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.

• AIDS patients are at increased risk for arthrosclerosis

• HIV Nef inhibits cholesterol exporter

• Cholesterol accumulates in HIV-infected cells

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

Oznur Tastan

Jaime G. Carbonell

Sivaraman Balakrishnan

Pittsburgh Center for HIV Protein Interactions

www.hivppi.pitt.edu