Elsevier

Gene

Volume 542, Issue 1, 25 May 2014, Pages 38-45
Gene

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

https://doi.org/10.1016/j.gene.2014.03.022Get rights and content

Highlights

  • Network-based analysis of time course gene expression data identified pathways active during the development of osteoarthritis.

  • Active pathways included extracellular matrix–receptor interaction, focal adhesion, Wnt, Hedgehog and TGF-β signaling.

  • A unique pathway active early in the development of OA was riboflavin metabolism.

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.

Introduction

In order to generate new hypotheses and obtain a better understanding of complex disease processes, investigators have used a systems biology approach to complement the more traditional reductionist approach. A systems approach has been facilitated by the availability of technologies which allow for interrogation of the entire genome, proteome and/or metabolome in an organism or tissue of interest. One of the most widely available and commonly used approaches is analysis of gene expression using microarrays. The analysis and interpretation of gene expression data generated by microarrays most commonly involve grouping or clustering genes based on the measured changes in expression, followed by identification of over-represented annotations in each of the gene clusters using various commercial or publically available bioinformatics tools. Although this methodology can elucidate processes and pathways that are differentially regulated in diseased cells or tissues, it does not incorporate information needed to define the relationships between specific genes or proteins to discover interacting networks and pathways important to a disease process.

In this work, we integrated microarray gene expression data into a network-based analysis to identify the signaling and metabolic pathways most highly regulated during the development of osteoarthritis (OA). OA is a condition of increasing public health interest. As the most common form of arthritis affecting over 27 million people in the US (Lawrence et al., 2008) with similar prevalence worldwide, OA results in significant pain and loss of function making it the most common cause of chronic disability in adults (Anon., 2001). A major limitation in the treatment of OA is the lack of any intervention proven to directly impact the disease process, either in the early or late stage of the disease. A better understanding of the molecular changes that occur during the development of OA will improve our knowledge of how this disease progresses and will help identify new targets needed to develop therapies to improve clinical outcomes.

Microarray studies using RNA extracted from joint tissues affected by OA have been reported in the literature. Many of these studies have focused on a single tissue, like articular cartilage (Appleton et al., 2007, Wei et al., 2010), subchondral bone (Hopwood et al., 2007) or synovium (Kato et al., 2007, Scanzello et al., 2011) most often at single time points. While these approaches have identified many novel genes with altered expression in OA, there is no information on how the gene product interactions may be altered to influence the development of the disease process.

We recently generated a microarray dataset that assessed gene expression changes over time during the early stages of OA in a commonly used mouse model (Loeser et al., 2013). In this model, OA develops after surgical destabilization of the medial meniscus (DMM model). We evaluated changes in gene expression and histological evidence of OA at 2, 4, 8, and 16 weeks after DMM surgery with a sham surgery group serving as control. In order to be able to take a more systems level approach, we examined gene expression changes in the joint as an organ by extracting RNA from a pool of tissues including cartilage, subchondral bone, meniscus and the joint capsule with synovium. Using this dataset, we previously filtered a total of 371 genes into 27 clusters with various temporal gene expression patterns (Loeser et al., 2013). These clusters included many genes previously associated with OA – such as COMP, MMP14, TIMP2, SDC1, DDR2, TGFB2, MMP13, FMOD, BGN, LECT1, and S100B and multiple collagen genes – and elucidated their expression kinetics during the onset of OA. However, the cluster analysis was unable to identify connections between the genes needed to better understand how particular pathways are regulated during the disease process.

Utilizing the data from this time course microarray experiment, in the present study we were able to discover how perturbed pathways change over time by observing which transcripts are active (differentially expressed between DMM and sham controls) or inactive at each time point. Subnetworks relevant to the OA disease process were found, along with novel genes within these networks that have not been previously studied in OA. The results of this network-based analysis demonstrate that an improved comprehensive systems level understanding of OA can be obtained by incorporating pathway-level information into the analyses of gene expression microarray data.

Section snippets

Normalization and processing of microarray data

The gene expression microarray dataset used for the present analysis was obtained from our recently published study (Loeser et al., 2013). In brief, 12 week-old male C57BL/6 mice underwent DMM surgery to induce OA or sham surgery as control. After sacrifice, RNA was isolated from the medial side of the joint (n = 9 mice per surgical group per time point) pre-surgery (time 0) and at 2, 4, 8 and 16 weeks after surgery. RNA from 3 mice were pooled for each array performed using the Affymetrix Mouse

Identification of subnetworks actively regulated during the time course of OA development

Our approach was to identify actively regulated subnetworks from a known background global network. We defined an active subnetwork as a collection of interacting gene products (proteins/nodes in the network) for which the majority of the genes are regulated across the time course. For this method, a threshold for significant gene expression is not required. This process is summarized in Fig. 1. Briefly, we constructed a background global network of 2393 nodes, from the Kyoto Encyclopedia of

Conclusions

Network-based analysis of a time course gene expression dataset revealed the signaling and metabolic pathways that were actively regulated at one or more time points during the development of OA in a commonly used mouse model of the disease. The major pathways identified included the extracellular matrix–receptor interaction and the focal adhesion pathways along with the Wnt, Hedgehog and TGF-β signaling pathways. A newly identified pathway active at the 4 week time point was riboflavin

Conflict of interest

There are no conflicts of interests.

Acknowledgments

This work was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (R37 AR049003-12) and by an Innovative Research Award from the Arthritis Foundation. The authors thank Brian Westwood for the assistance with the network analysis.

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