br Acknowledgments We thank Michael Kyba for the gift
Acknowledgments We thank Michael Kyba for the gift of the p2lox.EGFP plasmid and A2lox.cre ESCs; Jon Aster for pEGFP-DML-N3 plasmid, Shonna Johnston and Fiona Rossi (QMRI Flow cytometry facility) for cell sorting and Sabrina Gordon-Keylock for valuable discussions throughout the project. This work was funded by Leukaemia Lymphoma Research (AHT and MJ) and a scholarship from the University of Edinburgh College of Medicine and Veterinary Medicine (CH).
Introduction Osteoarthritis (OA) is one of the most frequent musculoskeletal disorders and represents the main indication for total joint arthroplasty (Pereira et al., 2011; Pivec et al., 2012). Notably, in the western countries 10% of individuals above the age of 60years suffer from osteoarthritis of the hip joint (Sun et al., 1997). Due to the increasingly aging western populations OA represents one of the most important sociomedical and economic musculoskeletal diseases (Woolf and Pfleger, 2003). OA is defined as a premature destructive joint degeneration accompanied by pain and reduced function. The disease is characterized by alterations in both the cartilaginous (e.g. increased matrix-metalloproteinase-mediated enzymatic breakdown of extracellular matrix components) as well as by structural alterations in the adjacent subchondral bone (Bijlsma et al., 2011). Cellular and subcellular mechanisms are in the focus of ongoing research concerning OA etiology (Müller-Hilke, 2007). Genetic factors are discussed as reason for OA besides apoptosis, mechanical load, and oxidative stress (Aigner et al., 2007; Loughlin, 2011). To date, therapeutic options are predominantly symptom-orientated and include the use of anti-inflammatory drugs, changes in life-style, autologous chondrocyte transplantation, as well as joint axis realigning and joint-replacing surgery. Bone marrow stromal l-alanine (BMSCs) provide an excellent source of progenitor cells because of their proliferation and differentiation capacity along a number of connective tissue lineages, e.g. bone, cartilage, adipose, and muscular tissue (Pittenger et al., 1999; Vater et al., 2011). BMSCs can be easily isolated from bone marrow aspirates and are key components for innovative cell-based therapies to treat musculoskeletal diseases (Loughlin, 2011). It can be assumed that a large proportion of patients who would potentially benefit from cell-based strategies suffer from osteoarthritis as the underlying disease or comorbidity. Previously OA research primarily focused on morphological and metabolic alterations of chondrocytes and extracellular matrix (ECM) based on the rationale that the osteoarthritic joint is characterized by an imbalance between anabolic and catabolic processes at the cellular and extracellular level resulting in ECM degradation-mediated chondrolysis (Aigner et al., 2007). Interestingly, researchers recently demonstrated alterations in the proliferation and differentiation capacity of BMSCs from osteoarthritic as compared to healthy donors supposing a key role of BMSCs in the pathogenesis of OA. Murphy et al. observed that OA-BMSCs showed a significantly reduced proliferation capacity as well as a reduction of the in vitro adipogenic and chondrogenic activities (Murphy et al., 2002). Lamas et al. demonstrated in bone marrow-derived OA-BMSCs isolated from the femoral channel a down regulation of the collagen gene set, particularly collagen, type X, alpha 1 (COL10A1), as well as several Wnt/-catenin pathway related genes (Lamas et al., 2010). Currently, quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) can be regarded as the most accurate and specific technique to assess gene expression. Because of its reproducibility, large dynamic range, ease of use, capability of high throughput, high accuracy, and affordability qRT-PCR is the most frequently applied method for gene expression analyses (D\'haene et al., 2010). However, the accuracy of gene expression assessment is influenced by several factors, e.g. type and number of cells, quality and handling of mRNA (Bustin, 2002; Bustin and Nolan, 2004), type of detection chemistry (Bustin and Nolan, 2004), and cDNA synthesis conditions (Lekanne Deprez et al., 2002). Thus normalization of qRT-PCR data is an inevitable step to ensure accounting for all inter-sample variables mentioned above. Therefore, genes which are equally and constantly expressed independent of any treatment or disease should be used as internal controls. Target gene expression is related to the expression of these reference or “housekeeping” genes. The most popular and commonly used reference genes are glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and beta-actin (ACTB). However, these genes are not constantly expressed in all types of physiological and diseased cells and tissues (Zhu et al., 2001; Suzuki et al., 2000). Although the combined use of several reference genes is recommended (Vandesompele et al., 2002), the vast majority of gene expression investigations are performed using only one reference gene. A proper selection of suitable reference genes needs to be performed for each tissue, cell type, and disease before using them in gene expression studies. At present, no standard method for the selection of reference genes exists. For the optimal choice of reference genes a number of statistical algorithms, such as NormFinder (Andersen et al., 2004), Global Pattern Recognition (Akilesh et al., 2003), equivalence test (Haller et al., 2004), Bestkeeper© (Pfaffl et al., 2004), and geNorm (Vandesompele et al., 2002) were introduced with all these algorithms being based on the gene expression stability.