Modelling the Gene Regulatory Network underlying Lineage Commitment in Human Mesenchymal Stem Cells (LINCONET)

Lead Research Organisation: University of Birmingham
Department Name: Sch of Biosciences

Abstract

With the aging of the world population, degenerative diseases such as osteoporosis and 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 the normal biological processes underlying cell differentiation and the disease-induced tissue degeneration. 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, an integrated approach that uses advanced computational techniques (a systems biology approach) is essential. This project aims to develop a framework to understand tissue regeneration. To this end, we will unravel and mechanistically model the genetic network underlying normal tissue regeneration processes. An integral part of our strategy is to experimentally validate hypotheses generated by computational models by using tightly integrated cycles of computational modelling and experimental verification at the cellular and organism level.

Technical Summary

With the aging of the world population, degenerative diseases such as osteoporosis and 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 different forms of arthritis. To this end, we will unravel and mechanistically model the genetic network underlying normal lineage commitment of HMSCs and, in parallel, discover genes that are aberrantly expressed in the diseased bone- or cartilage-synthesizing cells. The integration of both approaches is pivotal for raising hypotheses towards disease-modifying gene products important for the restoration of bone or cartilage. An integral part of our strategy is to experimentally validate hypotheses generated by computational models by using tightly integrated cycles of computational modelling and experimental verification at the cellular and organism level.

Planned Impact

This project will result in increased knowledge about the molecular mechanisms and key regulators that control normal lineage-specific commitment and differentiation of HMSC. In addition, this project will give insight into the key genes affected by OA and RA. Functional validations performed in this project concerning the role of druggable key genes will have a major contribution to the development of novel therapies for the treatment of degenerative disorders such as osteoporosis and arthritis. We expect therefore that our research will benefit the public and private sector at several levels. The development of data analysis methodologies for the inference of regulatory networks from observational data is in fact a very new area where organizations interested in the development of bio-markers of clinical relevance are investing a considerable amount of resources. Our methodologies will certainly represent a significant contribution to the field and will indeed directly benefit these organizations. More generally the identification of molecular networks controlling tissue differentiation and regeneration will represent potential important targets for the pharmaceutical industry. As explained in detailed in the impact plan, our project will include several networking activities which aim to the development of an exploitation strategy in direct contact with the stakeholders.
 
Description In the course of this project we have integrated experimental and computational approaches to understanding bone and cartilage differentiation. We have also developed a new computational methodology to identify biological networks integrating multiple levels of information.



Human Mesenchymal Stem Cells (hMSCs) differentiation: hMSCs differentiation is a fundamental process for the maintenance of the musculoskeletal system. Despite extensive research the precise mechanisms beyond this process are still unclear. We addressed this challenge by developing the first comprehensive computational models underlying transcriptional changes during the differentiation of hMSCs towards osteocytes, chondrocytes and adipocytes. Although these models were developed to represent the normal differentiation process, they allowed the identification of potential candidates that could enhance the efficiency of differentiation in ageing and in disease condition. Examples of target genes include DUSP6 and MYC, which we predict may be involved in bone deficiency in multiple myeloma. In the last phase of this project we are now completing the experimental validation of the model predictions.



A new network modularization method: In order to support the identification of networks underlying hMSC differentiation we developed a new methodology that allow the integration of multi-level functional genomics dataset into the framework of network inference. Our methodology is the only method available that allows this level of integration and guarantee that a global optimum is reached. This novel methodology has shown high sensitivity and specificity in identifying true network modules using simulated data and its potential goes well beyond the application to hMSC differentiation.
Exploitation Route If the experimental validation of the differentiation targets will be successful we will be in the position to further develop the concept towards stem cell therapeutics. At that stage we may consider industry partnerships to validate them as clinical targets.

On the other hand, the software developed with the follow up NERC grant will be very useful in an industry settings. In this context we have already made contact with Dr. Philippe Sanseau at GSK (Stevenage, UK) to explore the use of our network modularisation method in his company computational analysis group. The research funded in this grant can be used in several ways. Firstly, the models we have developed have identified several potential drug targets that can be exploited to reduce the severity of bone erosion in ageing and in diseases such as arthritis and multiple myeloma. In order to exploit this avenue we have contacted a CRUK funded group at the University of Birmingham (Prof. Paul Moss) with whom we have initiated a collaboration to test whether modulation of the targets we have identified can reconstitute patient derived hMSC differentiation. In addition, the new modularisation techniques we have developed are potentially extremely useful to other academic users that use integrative approaches. In order to fully exploit this avenue we have applied for a NERC funded discipline hopping fellowship in collaboration with the School of Computer Science at the University of Liverpool (Prof. Leszek Gasieniec). This small grant has been awarded and will allow a PhD in Computer science, currently working in that department, to spend six month in our group to develop a software application that will integrate our new method with other approaches for network analysis. This software will be available to the scientific community shortly after the project.
Sectors Digital/Communication/Information Technologies (including Software),Healthcare,Pharmaceuticals and Medical Biotechnology