Research Papers: Gerotarget (Focus on Aging):
Core level regulatory network of osteoblast as molecular mechanism for osteoporosis and treatment
Metrics: PDF 1164 views | HTML 1265 views | ?
Ruoshi Yuan1,2,*, Shengfei Ma3,*, Xiaomei Zhu5, Jun Li4, Yuhong Liang4, Tao Liu4, Yanxia Zhu4, Bingbing Zhang4, Shuang Tan4, Huajie Guo4, Shuguang Guan3, Ping Ao1,2 and Guangqian Zhou4
1 Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
2 School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
3 Department of Physics, East China Normal University, Shanghai, China
4 The Center for Anti-Ageing and Regenerative Medicine, Shenzhen University, Shenzhen, China
5 GeneMath, N.E., Seattle, WA, USA
* These authors have contributed equally to this work
Ping Ao, email:
Guangqian Zhou, email:
Keywords: hormone, osteoporosis, regulatory network, strontium treatment, systems biology, Gerotarget
Received: August 30, 2015 Accepted: January 04, 2016 Published: January 15, 2016
To develop and evaluate the long-term prophylactic treatment for chronic diseases such as osteoporosis requires a clear view of mechanism at the molecular and systems level. While molecular signaling pathway studies for osteoporosis are extensive, a unifying mechanism is missing. In this work, we provide experimental and systems-biology evidences that a tightly connected top-level regulatory network may exist, which governs the normal and osteoporotic phenotypes of osteoblast. Specifically, we constructed a hub-like interaction network from well-documented cross-talks among estrogens, glucocorticoids, retinoic acids, peroxisome proliferator-activated receptor, vitamin D receptor and calcium-signaling pathways. The network was verified with transmission electron microscopy and gene expression profiling for bone tissues of ovariectomized (OVX) rats before and after strontium gluconate (GluSr) treatment. Based on both the network structure and the experimental data, the dynamical modeling predicts calcium and glucocorticoids signaling pathways as targets for GluSr treatment. Modeling results further reveal that in the context of missing estrogen signaling, the GluSr treated state may be an outcome that is closest to the healthy state.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License.