Loading of the rat ulna is an ideal model to examine stress fracture healing. The aim of this study was to undertake a detailed examination of the histology, histomorphometry and gene expression of the healing and remodelling process initiated by fatigue loading of the rat ulna. Ulnae were harvested 1, 2, 4, 6, 8, and 10 weeks following creation of a stress fracture. Stress fracture healing involved direct remodelling that progressed along the fracture line as well as woven bone proliferation at the site of the fracture. Histomorphometry demonstrated rapid progression of basic multicellular units from 1 to 4 weeks with significant slowing down of healing by 10 weeks after loading. Quantitative PCR was performed at 4 hours, 24 hours, 4 days, 7 days, and 14 days after loading. Gene expression was compared to an unloaded control group. At 4 hours after fracture, there was a marked 220-fold increase (P<0.0001) in expression of IL-6. There were also prominent peak increases in mRNA expression for OPG, COX-2, and VEGF (all P<0.0001). At 24 hours, there was a peak increase in mRNA expression for IL-11 (73-fold increase, P<0.0001). At 4 days, there was a significant increase in mRNA expression for Bcl-2, COX-1, IGF-1, OPN, and SDF-1. At 7 days, there was significantly increased mRNA expression of RANKL and OPN. Prominent, upregulation of COX-2, VEGF, OPG, SDF-1, BMP-2, and SOST prior to peak expression of RANKL indicates the importance of these factors in mediating directed remodelling of the fracture line. Dramatic, early upregulation of IL-6 and IL-11 demonstrate their central role in initiating signalling events for remodelling and stress fracture healing.

BackgroundReal-time quantitative RT-PCR (qPCR) is a powerful technique capable of accurately quantitating mRNA expression levels over a large dynamic range. This makes qPCR the most widely used method for studying quantitative gene expression. An important aspect of qPCR is selecting appropriate controls or normalization factors to account for any differences in starting cDNA quantities between samples during expression studies. Here, we report on the selection of a concise set of housekeeper genes for the accurate normalization of quantitative gene expression data in differentiating osteoblasts, osteoclasts and macrophages. We implemented the use of geNorm, an algorithm that determines the suitability of genes to function as housekeepers by assessing expression stabilities. We evaluated the expression stabilities of 18S, ACTB, B2M, GAPDH, HMBS and HPRT1 genes.FindingsOur analyses revealed that 18S and GAPDH were regulated during osteoblast differentiation and are not suitable for use as reference genes. The most stably expressed genes in osteoblasts were ACTB, HMBS and HPRT1 and their geometric average constitutes a suitable normalization factor upon which gene expression data can be normalized. In macrophages, 18S and GAPDH were the most variable genes while HMBS and B2M were the most stably expressed genes. The geometric average of HMBS and B2M expression levels forms a suitable normalization factor to account for potential differences in starting cDNA quantities during gene expression analysis in macrophages. The expression stabilities of the six candidate reference genes in osteoclasts were, on average, more variable than that observed in macrophages but slightly less variable than those seen in osteoblasts. The two most stably expressed genes in osteoclasts were HMBS and B2M and the genes displaying the greatest levels of variability were 18S and GAPDH. Notably, 18S and GAPDH were the two most variably expressed control genes in all three cell types. The geometric average of HMBS, B2M and ACTB creates an appropriate normalization factor for gene expression studies in osteoclasts.ConclusionWe have identified concise sets of genes suitable to use as normalization factors for quantitative real-time RT-PCR gene expression studies in osteoblasts, osteoclasts and macrophages.

Research capacity building in healthcare works to generate and apply new knowledge to improve health outcomes; it creates new career pathways, improves staff satisfaction, retention and organisational performance. While there are examples of investment and research activity in rural Australia, overall, rural research remains under-reported, undervalued and under-represented in the evidence base. This is particularly so in primary care settings. This lack of contextual knowledge generation and translation perpetuates rural–metropolitan health outcome disparities. Through greater attention to and investment in building research capacity and capability in our regional, rural and remote health services, these issues may be partially addressed. It is proposed that it is time for Australia to systematically invest in rurally focussed, sustainable, embedded research capacity building.

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