In vivo identification of Bmp2-correlation networks during fracture healing by means of a limb-specific conditional inactivation of Bmp2.
In vivo identification of Bmp2-correlation networks during fracture healing by means of a limb-specific conditional inactivation of Bmp2.
复制标题
通过肢体特异性条件性 Bmp2 失活来体内鉴定骨折愈合过程中的 Bmp2 相关网络。
DOI：
10.1016/j.bone.2018.07.016
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发表时间：
2018
期刊：
影响因子：
4.1
通讯作者：
Intini,Giuseppe
中科院分区：
文献类型：
--
作者：
Yu,Yau-Hua;Wilk,Katarzyna;Waldon,PhiAnhL;Intini,Giuseppe
已知Bmp2通过骨膜激活在骨折愈合开始中起重要作用。具体来说，骨膜祖细胞的激活和随后的分化需要Bmp2信号来激活骨-软骨生成途径。在这里，我们探讨了在骨折修复过程中Bmp2和150个已知候选基因之间的相互作用转录基因。我们在体内构建了相互作用的Bmp2信号通路，通过比较Bmp2存在（野生型）和不存在（Bmp2c/c；Prx1::cre肢体特异性条件敲除）时股骨骨折前和24 h后的基因表达水平。26个差异表达基因（骨折前与骨折后）在每个实验条件下都表现出高度相关性，用于构建共表达网络。不同共表达网络的拓扑动态变化将26个差异表达基因描述为非冗余焦点连接枢纽、冗余连接枢纽、外围基因或不存在。鉴定和讨论了排名靠前的上调或下调基因。公共数据库中的蛋白质-蛋白质相互作用支持我们的发现。因此，本研究的共表达网络可以用于未来的实验假设。
Bmp2 is known to play an essential role in the initiation of fracture healing via periosteal activation.Specifically, activation and subsequent differentiation of periosteal progenitor cells requires Bmp2 signaling for activation of the osteo-chondrogenic pathway. Here, we explored the interactive transcriptional gene-gene interplays between Bmp2 and 150 known candidate genes during fracture repair. We constructed the interactive Bmp2 signaling pathways in vivo, by comparing gene expression levels prior and 24 h post femur fracture, in presence (wild type) and in absence of Bmp2 (Bmp2c/c;Prx1::cre limb-specific conditional knockout). Twenty-six differentially expressed genes (pre- vs. post-fracture), which demonstrated high correlations within each experimental condition, were used to construct the co-expression networks. Topological dynamic shifts across different co-expression networks characterized the 26 differentially expressed genes as non-redundant focal linking hubs, redundant connecting hubs, periphery genes, or non-existent. Top-ranked up- or down-regulated genes were identified and discussed. Protein-protein interactions in public databases support our findings. Thus, the co-expression networks from this study can be used for future experimental hypotheses.