Integrative methods for reconstruction of dynamic networks in chondrogenesis

Application of human mesenchymal stem cells represents a promising approach in the field of regenerative medicine. Specific stimulation can give rise to chondrocytes, osteocytes or adipocytes. Investigation of the underlying biological processes which induce the observed cellular differentiation is essential to efficiently generate specific tissues for therapeutic purposes. Upon treatment with diverse stimuli, gene expression levels of cultivated human mesenchymal stem cells were monitored using time series microarray experiments for the three lineages. Application of gene network inference is a common approach to identify the regulatory dependencies among a set of investigated genes. This thesis applies the NetGenerator V2.0 tool, which is capable to deal with multiple time series data, which investigates the effect of multiple external stimuli. The applied model is based on a system of linear ordinary differential equations, whose parameters are optimised to reproduce the given time series datasets. Several procedures in the inference process were adapted in this new version in order to allow for the integration of multiple datasets. Network inference was applied on in silico network examples as well as on multi-experiment microarray data of mesenchymal stem cells. The resulting chondrogenesis model was evaluated on the basis of several features including the model adaptation to the data, total number of connections, proportion of connections associated with prior knowledge and the model stability in a resampling procedure. Altogether, NetGenerator V2.0 has provided an automatic and efficient way to integrate experimental datasets and to enhance the interpretability and reliability of the resulting network. In a second chondrogenesis model, the miRNA and mRNA time series data were integrated for the purpose of network inference. One hypothesis of the model was verified by experiments, which demonstrated the negative effect of miR-524-5p on downstream genes

Defining the transcriptome of the osteocyte network

The skeleton is a multifunctional organ-system, providing structural support to the body and maintaining mineral homeostasis through endocrine interactions with distant organs. Balance between these functions is critical to skeletal health and is regulated by a network of cells distributed throughout bone tissue - the osteocyte network. Little is known of the genes with which the osteocyte network performs this specialised function. As a consequence, understanding of its contribution to skeletal disease is very limited. I hypothesised that defining gene expression in the osteocyte network would reveal genes important to its function and provide insights into skeletal disease. To test this, I developed techniques to perform transcriptome sequencing on the osteocyte network and analysed how gene expression is influenced by skeletal-site, age and sex. I established experimental and analytical strategies to identify a signature of genes enriched for expression in the osteocyte network and reveal molecular processes enabling its specialised function. These genes were examined for their association with skeletal dysplasia and clinically relevant skeletal traits. This work revealed that gene expression in osteocytes is highly conserved between skeletal sites, with the exception of a limited number of developmental transcription factors differentially active between adult bone types. Dynamic changes in the osteocyte transcriptome during skeletal maturation were also identified, including the sexually dimorphic regulation of genes associated with perilacunar-remodelling. An osteocyte transcriptome signature was defined - 830 genes enriched for expression within the osteocyte network. Enriched expression in the osteocyte network was the first evidence of skeletal involvement for the majority of signature genes, including novel genes with skeletally-restricted activity alongside known osteocyte markers. This work identified a range of signalling pathways significantly enriched in the osteocyte network, including neuron-like network formation pathways upregulated early in osteocytic differentiation. This osteocyte signature is enriched for gene-orthologs known to cause human skeletal dysplasias and influence bone mineral density. These discoveries identify the genes and molecular processes that define the osteocyte network and demonstrate that specific expression in the osteocyte network may be a powerful filter to identify genes that cause skeletal disease