Protein-protein interaction (PPI) networks provide a static map of functional protein interactions, which when combined with algorithms, can prioritize key protein candidates which experimental studies cannot capture....
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Protein-protein interaction (PPI) networks provide a static map of functional protein interactions, which when combined with algorithms, can prioritize key protein candidates which experimental studies cannot capture. This study, aimed to construct knowledge-based nucleus pulposus (NP)-specific PPI networks which could be deployed to investigate complex protein interactions in human NP cells and tissues following IL-4 and IL-10 stimulation. NP-specific PPI networks were developed based on mass spectrometry (MS) and secretome data-sets from human NP cells. These networks were validated using in vitro and ex vivo experimental data sets. Genes Underlying Inheritance Linked Disorders (GUILD) genome-wide network-based prioritization framework was employed for protein candidate prediction under no treatment baseline and IL-4, IL-10 and IL-1 beta single or combined stimulating scenarios. These secretome-based in vitro PPI networks were able to reproduce the no-treatment candidate prioritization baseline. Whereby within NP cells from discs isolated due to traumatic injury biglycan was identified whilst in degenerate samples decorin was highlighted. Furthermore, experimentally observed IL-4 pleiotropic behaviour was predicted by IL-1 receptor-like 1 prioritization. PPI network-based IL-4 and IL-10 conditions offered novel insights of potential candidates, including collagen IV and fibroblast growth factor intracellular binding protein (FIBP) as key candidates within IL-4 activation pathways, whereas urocortin 3 and neural growth factor were identified following IL-10 stimulation. Additionally, MS based PPI network propagation offered a more extensive, module-based structure networks with lower edge degree and biological variability. Overall, multiple proteomic experimental approaches are required to successfully validate in-silico prediction models to understand the complex interactions between the plethora of proteins involved in IVD degeneration.
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