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This article is part of the supplement: The 2010 International Conference on Bioinformatics and Computational Biology (BIOCOMP 2010): Systems Biology

Open Access Research article

An integrated proteomics analysis of bone tissues in response to mechanical stimulation

Jiliang Li1, Fan Zhang23 and Jake Y Chen234*

Author affiliations

1 Department of Biology, Purdue School of Science, Indiana University Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA

2 Indiana University School of Informatics, Indianapolis, IN 46202, USA

3 Indiana Center for Systems Biology and Personalized Medicine, Indianapolis, IN 46202, USA

4 Department of Computer and Information Science, Purdue University School of Science, Indianapolis, IN 46202, USA

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Citation and License

BMC Systems Biology 2011, 5(Suppl 3):S7  doi:10.1186/1752-0509-5-S3-S7

Published: 23 December 2011

Abstract

Bone cells can sense physical forces and convert mechanical stimulation conditions into biochemical signals that lead to expression of mechanically sensitive genes and proteins. However, it is still poorly understood how genes and proteins in bone cells are orchestrated to respond to mechanical stimulations. In this research, we applied integrated proteomics, statistical, and network biology techniques to study proteome-level changes to bone tissue cells in response to two different conditions, normal loading and fatigue loading. We harvested ulna midshafts and isolated proteins from the control, loaded, and fatigue loaded Rats. Using a label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) experimental proteomics technique, we derived a comprehensive list of 1,058 proteins that are differentially expressed among normal loading, fatigue loading, and controls. By carefully developing protein selection filters and statistical models, we were able to identify 42 proteins representing 21 Rat genes that were significantly associated with bone cells' response to quantitative changes between normal loading and fatigue loading conditions. We further applied network biology techniques by building a fatigue loading activated protein-protein interaction subnetwork involving 9 of the human-homolog counterpart of the 21 rat genes in a large connected network component. Our study shows that the combination of decreased anti-apoptotic factor, Raf1, and increased pro-apoptotic factor, PDCD8, results in significant increase in the number of apoptotic osteocytes following fatigue loading. We believe controlling osteoblast differentiation/proliferation and osteocyte apoptosis could be promising directions for developing future therapeutic solutions for related bone diseases.

Keywords:
Bone Stress; Biomarker Discovery; Pathway Analysis; Tandem Mass Spectrometry