A Systems Biology Approach for Analysis of Skeletal Inflammation, Bone Remodeling, and Bone Metastasis
This study aimed to utilize computational tools to identify and develop new treatment options for skeletal diseases by analyzing large datasets, like genome-wide gene expression data, and modeling biological systems. Rheumatoid arthritis is an inflammatory disease lacking effective treatment options, and parathyroid hormone (PTH) treatment of osteoporosis is applied without a basic understanding of PTH's mechanism of action. Bone metastasis is deadly for cancer patients, and understanding its development is critical for reducing cancer mortality. In this study, principal component analysis (PCA) of gene microarray data identified genes that are potential treatment targets for skeletal diseases, including rheumatoid arthritis, osteoporosis, and breast cancer-borne bone metastasis, while computational modeling evaluated the mechanism of PTH's action. PCA can be used to identify patterns in gene expression by determining a set of principal axes that best represents the changes in gene expression. Through PCA, as well as pathway analysis of differentially expressed genes, Dusp2 (dual specificity phosphatase 2) and c-Fos (Fos proto-oncogene) were identified as mediators of inflammation and bone resorption, respectively. PCA of a microRNA microarray and microRNA target prediction identified a role for miR-222-3p in osteoclast development mediated by Src (proto-oncogene c-Src). A mathematical model of mineral metabolism was used to evaluate the role of PTH and its gradient in regulating calcium levels and osteogenic genes. To identify genes involved in bone metastasis, analysis of RNA microarray and DNA sequencing of four breast cancer-derived cell lines was performed. PCA was applied to the RNA microarray data, and a set of genes that were differentially expressed in metastasized tumor cells were identified. Using RNA silencing, the role of these genes in invasion and metastasis was examined, and S100A4 (S100 calcium binding protein A4) and GRM3 (glutamate metabotropic receptor 3) were identified to be potential contributors. In summary, the study described herein demonstrates how computational methods and experiments can develop and characterize novel treatments for skeletal diseases, such as arthritis, osteoporosis, and bone metastasis. PCA is able to provide quantitative relationships between gene expression and cell phenotypes. Computational modeling is capable of raising hypotheses that consider multiple parameters and dynamical changes in biological responses. It is expected that genes identified using PCA and computational modeling in this study may become novel therapeutic targets of musculoskeletal diseases.
Shi, Purdue University.
Systematic biology|Biomedical engineering|Bioinformatics
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