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.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117 Arthritis+Res+Ther
2014 ; 16
(3
): R109
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Network analysis identifies protein clusters of functional importance in juvenile
idiopathic arthritis
#MMPMID24886659
Stevens A
; Meyer S
; Hanson D
; Clayton P
; Donn RP
Arthritis Res Ther
2014[May]; 16
(3
): R109
PMID24886659
show ga
INTRODUCTION: Our objective was to utilise network analysis to identify protein
clusters of greatest potential functional relevance in the pathogenesis of
oligoarticular and rheumatoid factor negative (RF-ve) polyarticular juvenile
idiopathic arthritis (JIA). METHODS: JIA genetic association data were used to
build an interactome network model in BioGRID 3.2.99. The top 10% of this
protein:protein JIA Interactome was used to generate a minimal essential network
(MEN). Reactome FI Cytoscape 2.83 Plugin and the Disease Association
Protein-Protein Link Evaluator (Dapple) algorithm were used to assess the
functionality of the biological pathways within the MEN and to statistically rank
the proteins. JIA gene expression data were integrated with the MEN and clusters
of functionally important proteins derived using MCODE. RESULTS: A JIA
interactome of 2,479 proteins was built from 348 JIA associated genes. The MEN,
representing the most functionally related components of the network, comprised
of seven clusters, with distinct functional characteristics. Four gene expression
datasets from peripheral blood mononuclear cells (PBMC), neutrophils and synovial
fluid monocytes, were mapped onto the MEN and a list of genes enriched for
functional significance identified. This analysis revealed the genes of greatest
potential functional importance to be PTPN2 and STAT1 for oligoarticular JIA and
KSR1 for RF-ve polyarticular JIA. Clusters of 23 and 14 related proteins were
derived for oligoarticular and RF-ve polyarticular JIA respectively. CONCLUSIONS:
This first report of the application of network biology to JIA, integrating
genetic association findings and gene expression data, has prioritised protein
clusters for functional validation and identified new pathways for targeted
pharmacological intervention.