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Development and analysis of biological interaction networks

Tsakaneli Stavroula

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Summary

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease that affects approximately 2.8 million persons globally. While there is currently no cure for this neurodegenerative disease, MS has become a highly manageable disease through treatment options like disease-modifying medications, that can help to control the symptoms and slow disease progression. Among them, interferon beta (IFNβ) therapy is a first-line treatment for MS but has shown to be only partially effective. Information from extensive databases for large groups of multiple sclerosis patients indicates that the natural history of MS evolves in two stages: (i) in the focal inflammatory process with flares, and ii) in disability that progresses irrespective of the focal inflammation (lesion or relapse) Thus, it is important to identify biomarkers that aid in early identification of the disease as well as of IFNβ responders. A second aim of our study was to identify biomarkers that aid in early identification of MS stages, i.e. the relapsing-remitting form (RR-MS), the secondary progressive phase (SP-MS) and the primary progressive MS (PP-MS).Gene co-expression patterns for various pheno-types can be reveal with the aid of Microarrays but the variation and heterogeneity of the disease act as limitations for the utility of gene-expression profiles. In addition, the different microarray platforms utilized, as well as the different experimental protocols followed, are facts that make difficult to combine gene-expression datasets form heterogeneous platforms and different studies. Another limitation is the great imbalance between the huge number of transcripts and genes (tens of thousands) and the relatively small number of available sample cases (hundreds). Furthermore, it is essential to combine feature-selection approaches and the ‘biological validity’ of the resulted gene biomarkers. Thus, our purpose in not only to focus on highly differential genes but combine different approaches in order to reach a resulted signature after examining the relationships of genomic signatures and deduce submodules of greater significance in relation to Multiple Sclerosis, the progression of the disease and future therapy.In this study, based on gene expression profiles from untreated, interferon treated patients and healthy subjects from publicly available datasets, we performed differential expression analysis and Pigengene network association (weighted correlation network analysis (WGCNA) and Bayesian networks modeling) so as to construct a high-confidence protein-protein (PPI) interaction network. Subsequently, aiming to find the most significant clustering modules and hub genes, we applied several topological analysis methods (cytoHubba plugin) followed by MCODE clustering algorithm. Our approach resulted in highly connected hub genes generating four highly reliable hub-gene-signatures. Finally, we approached the topic of drug repurposing by examining the drug-gene relationships through different databases.

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