Unveiling novel genes upregulated by both rhBMP2 and rhBMP7 during early osteoblastic transdifferentiation of C2C12 cells
FindingsWe set out to analyse the gene expression profile of pre-osteoblastic C2C12 cells during osteodifferentiation induced by both rhBMP2 and rhBMP7 using DNA microarrays. Induced and repressed genes were intercepted, resulting in 1,318 induced genes and 704 repressed genes by both rhBMP2 and rhBMP7. We selected and validated, by RT-qPCR, 24 genes which were upregulated by rhBMP2 and rhBMP7; of these, 13 are related to transcription (Runx2, Dlx1, Dlx2, Dlx5, Id1, Id2, Id3, Fkhr1, Osx, Hoxc8, Glis1, Glis3 and Cfdp1), four are associated with cell signalling pathways (Lrp6, Dvl1, Ecsit and PKCδ) and seven are associated with the extracellular matrix (Ltbp2, Grn, Postn, Plod1, BMP1, Htra1 and IGFBP-rP10). The novel identified genes include: Hoxc8, Glis1, Glis3, Ecsit, PKCδ, LrP6, Dvl1, Grn, BMP1, Ltbp2, Plod1, Htra1 and IGFBP-rP10.BackgroundBMPs (bone morphogenetic proteins) are members of the TGFβ (transforming growth factor-β) super-family of proteins, which regulate growth and differentiation of different cell types in various tissues, and play a critical role in the differentiation of mesenchymal cells into osteoblasts. In particular, rhBMP2 and rhBMP7 promote osteoinduction in vitro and in vivo, and both proteins are therapeutically applied in orthopaedics and dentistry.ConclusionUsing DNA microarrays and RT-qPCR, we identified both previously known and novel genes which are upregulated by rhBMP2 and rhBMP7 during the onset of osteoblastic transdifferentiation of pre-myoblastic C2C12 cells. Subsequent studies of these genes in C2C12 and mesenchymal or pre-osteoblastic cells should reveal more details about their role during this type of cellular differentiation induced by BMP2 or BMP7. These studies are relevant to better understanding the molecular mechanisms underlying osteoblastic differentiation and bone repair.
[…] The gene expression data were normalised using the SVR (Support Vector Regression) method , implemented in the GEDI toolbox . More information on normalisation method is available at http://mariwork.iq.usp.br/gedi/. Briefly, SVR is a non-parametric regression, similar to the Loess method; however, it has greater accuracy than the Loess method, therefore, it is a more useful tool to identify differentially expressed genes. Identification of these genes was obtained using the ratio of normalised intensities between rhBMP (2 or 7) and control (untreated) samples, considering a fold change of ≥ 2.5. Induced and repressed genes in C2C12 cells under simultaneous treatment with rhBMP2 and rhBMP7 were classified according to their functions through the FatiGO program (http://bioinfo.cipf.es/babelomicswiki/tool:fatigo). […]
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