Published in:
01-10-2005 | Laboratory Investigations
Patterns and Localization of Gene Expression During Intramembranous Bone Regeneration in the Rat Femoral Marrow Ablation Model
Authors:
Shinji Kuroda, Amarjit S. Virdi, Yang Dai, Susan Shott, Dale R. Sumner
Published in:
Calcified Tissue International
|
Issue 4/2005
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Abstract
Tissue formation and repair are dependent upon cascades of biological events, but the signals involved and the possible gene coexpression patterns during intramembranous bone repair are only poorly understood. We sought to place this mode of regeneration in context by profiling quantitative gene expression for a panel of 39 genes between days 1 and 14 following rat femoral marrow ablation. In situ hybridization was employed to localize a subset of genes. Additionally, principal components analysis was conducted to identify underlying factors suggestive of coexpression patterns. During inflammation (days 1–5), several genes, including cyclooxygenase-1 and -2, showed downregulation. Other proinflammatory cytokines, tumor necrosis factor-α and interleukin-1β, exhibited increasing levels around day 5. During repair (days 3–10), growth factors, receptors, and inhibitor genes for transforming growth factor- β; basic fibroblast growth factor; bone morphogenetic proteins 2, 4, and 7; vascular endothelial growth factor; and insulin-like growth factor-I were upregulated. In addition, the gene for core binding factor-α1 and markers of osteoblast function such as alkaline phosphatase, collagen type I, osteonectin, osteopontin, and osteocalcin had peak expression at day 5 or 7. The remodeling phase (days 10–14) was characterized by peaks for cytokines associated with osteoclastic activity including receptor activator of nuclear factor-κB, receptor activator of nuclear factor-κB ligand (RANKL), cathepsin K, tumor necrosis factor-α, interleukin-6, and cyclooxygenase-2. In situ hybridization showed that the most common sites of increased signal were within osteoblastic cells on trabecular and endosteal surfaces. Principal components analysis identified eight underlying factors that together explained over 80% of the variance in the data.