A protein interaction network associated with asthma

doi:10.1016/j.jtbi.2008.02.011

Journal of Theoretical Biology

Volume 252, Issue 4, 21 June 2008, Pages 722-731

https://doi.org/10.1016/j.jtbi.2008.02.011 Get rights and content

Abstract

Identifying candidate genes related to complex diseases or traits and mapping their relationships require a system-level analysis at a cellular scale. The objective of the present study is to systematically analyze the complex effects of interrelated genes and provide a framework for revealing their relationships in association with a specific disease (asthma in this case). We observed that protein–protein interaction (PPI) networks associated with asthma have a power-law connectivity distribution as many other biological networks have. The hub nodes and skeleton substructure of the result network are consistent with the prior knowledge about asthma pathways, and also suggest unknown candidate target genes associated with asthma, including GNB2L1, BRCA1, CBL, and VAV1. In particular, GNB2L1 appears to play a very important role in the asthma network through frequent interactions with key proteins in cellular signaling. This network-based approach represents an alternative method for analyzing the complex effects of candidate genes associated with complex diseases and suggesting a list of gene drug targets. The full list of genes and the analysis details are available in the following online supplementary materials: http://biosoft.kaist.ac.kr:8080/resources/asthma_ppi.

Introduction

Asthma is one of the most complex diseases characterized by specific patterns of inflammation in the airway mucosa, infiltration of eosinophils, increased numbers of T-helper-2 (Th2) cells relative to Th1 cells, and increased numbers of activated mast cells (Tattersfield et al., 2002). In addition, there are characteristic structural changes to the airways including subepithelial fibrosis, airway smooth muscle hypertrophy and hyperplasia, angiogenesis, and increased mucus secretory cells (Payne et al., 2003). These pathophysiological symptoms of asthma result from interactions between inflammatory cells, mediators, and inflammatory proteins that are controlled by many proinflammatory and cellular signaling genes (Barnes, 2004). Therefore, identifying candidate genes that could play important roles as drug targets for asthma requires systematic analyses of the complicated interrelations between the genes responsible for the progress of asthma.

Various methods have been employed to identify candidate genes to be targeted by drugs in complex diseases (Freudenberg and Propping, 2002; Perez-Iratxeta et al., 2002; Tiffin et al., 2005). Most of these approaches have not extensively considered the molecular biological pathways or the systematic interactions between components, even though previous studies have highlighted the importance of interactions between biological components such as proteins and metabolic substrates (Jeong et al., 2000, Jeong et al., 2001). Hub proteins having numerous interactions with other proteins are strongly related to the lethality of an organism. The complicated interactions and identifying hub nodes or central nodes can be analyzed by representing the components and their relations in a network.

In this paper, we introduce a new approach involving the network analysis of disease-related protein–protein interactions (PPIs) that we have applied to asthma. We adopted PPIs in constructing a biological network since proteins execute nearly all cell functions associated with enzymes, channels, and transporters (Alberts et al., 2002). The systematic approach proposed here is useful for analyzing the complex effects of these genes and building a framework of such relations to genes associated with asthma.

Section snippets

Materials and methods

Our protocol involved three main steps: (1) finding candidate genes from the literature database and analyzing the results of microarray experiments; (2) using these genes and PPI information to construct PPI networks; and (3) analyzing these networks qualitatively by visualization.

PPI network of asthma

We constructed two types of PPI network: a core network and an extended network. The nodes of the core network represent asthma-associated candidate proteins corresponding to genes obtained from a text database and microarray data, and the links between the nodes represent the PPIs. The nodes of the extended network represent not only candidate proteins but also interacting proteins, with the links again representing interactions.

The core network comprises 606 nodes: 269 isolated nodes and 337

Conclusions

We have extracted data related to asthma—text data from the OMIM database, microarray experiments from the GEO database, and PPIs from the HPRD—and subsequently constructed PPI networks that displayed degrees with a power-law distribution, with an exponent of approximately −2. The PPI networks represent the well-known knowledge, such as the proinflammatory and cellular proliferation signaling pathways of asthma. Applying the network analysis to the constructed networks revealed putative target

Acknowledgments

We thank Frank Lev and two anonymous referees for improving the manuscript. This research was supported by the Ministry of Knowledge Economy, Korea, under the ITRC support program supervised by the IITA (IITA-2008-C1090-0801-0001). D. Lee was supported by the Korea Science and Engineering Foundation (KOSEF) through the National Research Lab. Program (No. 2006-01508). S.-W. Son and H. Jeong were supported by KOSEF through the grant No. R17-2007-073-01001-0. S. Hwang, S.C. Kim and Y.J. Kim were

References (70)

T. Adachi et al.
Activation of epidermal growth factor receptor via CCR3 in bronchial epithelial cells
Biochem. Biophys. Res. Commun.
(2004)
Y. Diao et al.
The community structure of human cellular signaling network
J. Theor. Biol.
(2007)
S. Draghici et al.
Global functional profiling of gene expression
Genomics
(2003)
T.J. Griffin et al.
Complementary profiling of gene expression at the transcriptome and proteome levels in Saccharomyces cerevisiae
Mol. Cell Proteomics
(2002)
C.A. Hewson et al.
PMA induces the MUC5AC respiratory mucin in human bronchial epithelial cells, via PKC, EGF/TGF-alpha, Ras/Raf, MEK, ERK and Sp1-dependent mechanisms
J. Mol. Biol.
(2004)
H. Jiang et al.
IL-4/IL-13 signaling beyond JAK/STAT
J. Allergy Clin. Immunol.
(2000)
M. Kabesch et al.
IL-4/IL-13 pathway genetics strongly influence serum IgE levels and childhood asthma
J. Allergy Clin. Immunol.
(2006)
V. Karoor et al.
Insulin stimulates sequestration of beta-adrenergic receptors and enhanced association of beta-adrenergic receptors with Grb2 via tyrosine 350
J. Biol. Chem.
(1998)
S. Kumar et al.
Regulation of DNA-dependent protein kinase by the Lyn tyrosine kinase
J. Biol. Chem.
(1998)
J.W. Lee et al.
Mitogenic signal transduction by integrin- and growth factor receptor-mediated pathways
Mol. Cells
(2004)

R.C. Levitt et al.

IL-9 pathway in asthma: new therapeutic targets for allergic inflammatory disorders

J. Allergy Clin. Immunol.

(1999)

M. MacPartlin et al.

p300 regulates p63 transcriptional activity

J. Biol. Chem.

(2005)

F. Martinon et al.

Activation of a pro-apoptotic amplification loop through inhibition of NF-kappaB-dependent survival signals by caspase-mediated inactivation of RIP

FEBS Lett.

(2000)

K. Okkenhaug et al.

Grb2 forms an inducible protein complex with CD28 through a Src homology 3 domain–proline interaction

J. Biol. Chem.

(1998)

R. Pastor-Satorras et al.

Evolving protein interaction networks through gene duplication

J. Theor. Biol.

(2003)

U. Stelzl et al.

A human protein–protein interaction network: a resource for annotating the proteome

Cell

(2005)

A.E. Tattersfield et al.

Asthma

Lancet

(2002)

A.M. Vignola et al.

Proliferation and activation of bronchial epithelial cells in corticosteroid-dependent asthma

J. Allergy Clin. Immunol.

(2001)

S.J. Yarwood et al.

The RACK1 signaling scaffold protein selectively interacts with the cAMP-specific phosphodiesterase PDE4D5 isoform

J. Biol. Chem.

(1999)

R. Albert et al.

Error and attack tolerance of complex networks

Nature

(2000)

B. Alberts et al.

Molecular Biology of the Cell

(2002)

Y. Amrani et al.

Activation of tumor necrosis factor receptor 1 in airway smooth muscle: a potential pathway that modulates bronchial hyper-responsiveness in asthma?

Respir. Res.

(2000)

A.L. Barabasi et al.

Network biology: understanding the cell's functional organization

Nat. Rev. Genet.

(2004)

P.J. Barnes

New drugs for asthma

Nat. Rev. Drug. Discov.

(2004)

O. Barreiro et al.

Dynamic interaction of VCAM-1 and ICAM-1 with moesin and ezrin in a novel endothelial docking structure for adherent leukocytes

J. Cell Biol.

(2002)

V. Batagelj et al.

Pajek: program for large network analysis

Connections

(1998)

M.E. Bates et al.

Interleukin 5 signals through Shc and Grb2 in human eosinophils

Am. J. Respir. Cell Mol. Biol.

(1998)

U. Brandes

A faster algorithm for betweenness centrality

J. Math. Sociol.

(2001)

A. Bruzzaniti et al.

Dynamin forms a src kinase-sensitive complex with Cbl and regulates podosomes and osteoclast activity

Mol. Biol. Cell

(2005)

A.K. Cardozo et al.

Gene microarray study corroborates proteomic findings in rodent islet cells

J. Proteome Res.

(2003)

J.E. Darnell et al.

Jak-STAT pathways and transcriptional activation in response to IFNs and other extracellular signaling proteins

Science

(1994)

R. Edgar et al.

Gene expression omnibus: NCBI gene expression and hybridization array data repository

Nucleic Acids Res.

(2002)

L.C. Freeman

A set of measures of centrality based on betweenness

Sociometry

(1977)

J. Freudenberg et al.

A similarity-based method for genome-wide prediction of disease-relevant human genes

Bioinformatics

(2002)

S.H. Gavett et al.

Interleukin-4 receptor blockade prevents airway responses induced by antigen challenge in mice

Am. J. Physiol.

(1997)

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View full text

A protein interaction network associated with asthma

Abstract

Introduction

Section snippets

Materials and methods

PPI network of asthma

Conclusions

Acknowledgments

Biochem. Biophys. Res. Commun.

J. Theor. Biol.

Genomics

Mol. Cell Proteomics

J. Mol. Biol.

J. Allergy Clin. Immunol.

J. Allergy Clin. Immunol.

J. Biol. Chem.

J. Biol. Chem.

Mol. Cells

J. Allergy Clin. Immunol.

J. Biol. Chem.

FEBS Lett.

J. Biol. Chem.

J. Theor. Biol.

Cell

Lancet

J. Allergy Clin. Immunol.

J. Biol. Chem.

Error and attack tolerance of complex networks

Nature

Molecular Biology of the Cell

Activation of tumor necrosis factor receptor 1 in airway smooth muscle: a potential pathway that modulates bronchial hyper-responsiveness in asthma?

Respir. Res.

Network biology: understanding the cell's functional organization

Nat. Rev. Genet.

New drugs for asthma

Nat. Rev. Drug. Discov.

Dynamic interaction of VCAM-1 and ICAM-1 with moesin and ezrin in a novel endothelial docking structure for adherent leukocytes

J. Cell Biol.

Pajek: program for large network analysis

Connections

Interleukin 5 signals through Shc and Grb2 in human eosinophils

Am. J. Respir. Cell Mol. Biol.

A faster algorithm for betweenness centrality

J. Math. Sociol.

Dynamin forms a src kinase-sensitive complex with Cbl and regulates podosomes and osteoclast activity

Mol. Biol. Cell

Gene microarray study corroborates proteomic findings in rodent islet cells

J. Proteome Res.

Jak-STAT pathways and transcriptional activation in response to IFNs and other extracellular signaling proteins

Science

Gene expression omnibus: NCBI gene expression and hybridization array data repository

Nucleic Acids Res.

A set of measures of centrality based on betweenness

Sociometry

A similarity-based method for genome-wide prediction of disease-relevant human genes

Bioinformatics

Interleukin-4 receptor blockade prevents airway responses induced by antigen challenge in mice

Am. J. Physiol.