With the aging of the world population, degenerative diseases such as osteoporosis and rheumatoid arthritis will have an increasing impact on health and quality of life. Restoration of damaged bone and cartilage by stimulating human mesenchymal stem cells (HMSCs) to differentiate into bone- or cartilage-synthesizing cells provides a novel and attractive therapeutic opportunity with profound implications in biomedicine. This requires a thorough understanding of normal lineage commitment of HMSCs as well as an understanding of the key pathogenetic players in disease-induced tissue degradation. Given the multi-potent character of stem cells, and the complexity of the cross-talk between signalling pathways that determine lineage commitment and disease progression, a systems biological approach is essential to understand this process. This project aims to develop a systems biology framework to understand tissue regeneration and to identify key genes affected by tissue degeneration processes in both osteoarthritis and rheumatoid arthritis.
The first aim of the project is to develop a novel systems biology approach, consisting of an overarching framework that connects the biological mechanism of genetic regulation with a mathematical network formulation.
The second aim is to experimentally unravel the gene regulatory network that describes the mechanisms underlying normal in vitro lineage commitment and differentiation of HMSCs. Emphasis will be laid on the role of the glucocorticoid receptor in loss of self-renewal, as well as on the vitamin D receptor, the peroxisome proliferator-activated receptor gamma, and transforming growth factor beta (TGFß) on lineage-specific differentiation.
The third aim is to unravel the regulatory network that describes the inflammation response of synovial fibroblasts in (osteo-) arthritis. We will obtain and analyse time-course microarray data of fibroblasts from synovial membranes, following treatment with pro-fibrotic TGFß or the pro-inflammatory cytokine TNFa.
The fourth aim is to functionally characterize key genes identified from the network analyses by overexpression, knock-out and shRNA/siRNA to validate the networks and to identify potential drug targets.
E.J. van Zoelen
Dept. of Cell & Applied Biology
FNWI Radboud University, Nijmegen, NL
Dr. F. Falciani
School of Biosciences
University of Birmingham, UK
Systems Biology/ Bioinformatics Group
HKI Friedrich Schiller University Jena, DE
Dr. R.W. Kinne
Experimental Rheumatology Unit
University Hospital Jena, DE
© Projektträger JülichOnline 2006
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