Project: Modelling the Gene Regulatory Network underlying Lineage Commitment in Human Mesenchymal Stem Cells: Identification of Drug Targets for the Anabolic Treatment of Degenerative Disorders

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.

Project Results
(after finalisation)
The LINCONET project combines cell biological and bioinformatic expertise to understand the molecular basis of bone and cartilage regeneration, and to identify genes that are involved in degenerative bone diseases (such as osteoporosis) and different forms of arthritis (osteo- and rheumatoid). Bone and cartilage are formed from mesenchymal stem cells (hMSC) present in bone marrow and adipose tissue. Osteoporosis and arthritis result from enhanced tissue degradation due to the local activity of inflammatory cytokines, in combination with a reduced formation of new bone and cartilage cells. To understand the genetic program whereby new bone and cartilage cells are formed, we have unraveled and mechanistically modeled the genetic network underlying normal lineage commitment of hMSC. When treated with the proper stimuli, hMSC can differentiate into bone cells, cartilage cells or fat cells. Using gene expression microarray analysis we have studied the time-dependent transcriptome of hMSC upon differentiation into each of these three lineages. In order to derive genetic networks from these data, new bioinformatic approaches were developed including High Level Dynamic Modelling for analysis of time-dependent biological processes, and NetGenerator V2.0 for studying the effects of the simultaneous input of multiple stimuli. These approaches resulted in new networks for bone and cartilage differentiation of hMSC. In addition we have constructed genetic networks for cells from the synovial membrane of arthritis patients treated with multiple inflammatory cytokines, and verified the obtained gene-to-gene relations by siRNA. The experimental data confirmed the majority of gene-to-gene edges, while others were falsified and new ones proposed, thus leading to refined 2nd generation network inference. Finally drug targets for osteoporosis have been identified based on the network identification of genes that are upregulated during fat cell, but not during bone cell differentation of hMSC. Experiments with known drugs against these upregulated genes reduced fat cell differentiation and may therefore be highly relevant for enhancing bone cell differentiation in osteoporosis patients.
Network ERASysBio+
Call ERASysBio+-2008-01

Project partner

Number Name Role Country
1 Radboud University Nijmegen Coordinator Netherlands
2 University of Birmingham Partner United Kingdom
3 Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute (HKI) Partner Germany
4 University Hospital Jena Partner Germany