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Gene. 2014 May 25;542(1):38-45. doi: 10.1016/j.gene.2014.03.022. Epub 2014 Mar 12.

Integration of gene expression data with network-based analysis to identify signaling and metabolic pathways regulated during the development of osteoarthritis.

Author information

1
Department of Computer Science, Wake Forest University, Winston-Salem, NC, USA. Electronic address: amy.lynn81@gmail.com.
2
Department of Computer Science, Wake Forest University, Winston-Salem, NC, USA. Electronic address: turketwh@wfu.edu.
3
Department of Computer Science, Wake Forest University, Winston-Salem, NC, USA; Department of Physics, Wake Forest University, Winston-Salem, NC, USA. Electronic address: fetrowjs@wfu.edu.
4
Department of Internal Medicine, Section of Molecular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA. Electronic address: richard_loeser@med.unc.edu.

Abstract

Osteoarthritis (OA) is characterized by remodeling and degradation of joint tissues. Microarray studies have led to a better understanding of the molecular changes that occur in tissues affected by conditions such as OA; however, such analyses are limited to the identification of a list of genes with altered transcript expression, usually at a single time point during disease progression. While these lists have identified many novel genes that are altered during the disease process, they are unable to identify perturbed relationships between genes and gene products. In this work, we have integrated a time course gene expression dataset with network analysis to gain a better systems level understanding of the early events that occur during the development of OA in a mouse model. The subnetworks that were enriched at one or more of the time points examined (2, 4, 8, and 16 weeks after induction of OA) contained genes from several pathways proposed to be important to the OA process, including the extracellular matrix-receptor interaction and the focal adhesion pathways and the Wnt, Hedgehog and TGF-β signaling pathways. The genes within the subnetworks were most active at the 2 and 4 week time points and included genes not previously studied in the OA process. A unique pathway, riboflavin metabolism, was active at the 4 week time point. These results suggest that the incorporation of network-type analyses along with time series microarray data will lead to advancements in our understanding of complex diseases such as OA at a systems level, and may provide novel insights into the pathways and processes involved in disease pathogenesis.

KEYWORDS:

Arthritis; Articular cartilage; Cell signaling; Extracellular matrix; Systems biology

PMID:
24630964
PMCID:
PMC4031746
DOI:
10.1016/j.gene.2014.03.022
[Indexed for MEDLINE]
Free PMC Article
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