Data-Driven Modeling of Signaling Dysregulation in Rheumatoid Arthritis

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DESCRIPTION (provided by applicant): Human diseases ranging from chronic inflammation to fibrotic disorders and cancer are characterized by dysregulation of cellular signaling pathways, and therapeutics targeting these pathways have shown promise in treating cancer and arthritis. Extensive molecular data is available on individual signaling proteins and on "canonical" pathways, but differences in signaling networks from one cell type to the next are less well understood. Understanding network-level differences associated with diseased-states would substantially advance the development of novel therapeutics. In rheumatoid arthritis (RA), emerging evidence points to the resident fibroblast-like synoviocytes (FLS) as key players in disease progression, but studies of the signaling networks underlying their dysregulation have been limited. This proposal outlines an integrated experimental and computational approach to systematically evaluate primary FLS cells from normal and diseased individuals. I will construct predictive data-driven models of FLS signaling, and link signaling network activity to the resulting cellular response. My specific goals are three-fold: (i) to increase our understanding into how FLS cells have gone awry in disease, (ii) to determine the effects of standard clinical therapeutics for RA on these cells, and (iii) to predict and test new drug targets with the potentil for high therapeutic index. In our preliminary studies we have colected a compendium of ~15,000 data points describing the signaling of FLS from normal or RA primary cells (in culture) in response to diverse environmental stimuli. In Aim 1 I will expand this compendium in multiple dimensions to include investigation of both signaling and cellular responses in eight different primary human FLS cell isolates from normal and RA patient donors. This will provide insights into differences arising from disease-state vs. patient-to-patient variability. I will also directl evaluate the signaling and responses in the presence and absence of clinical therapeutics for RA to identify signaling nodes that persist in the presence of standard treatment modalities. In Aim 2 I will perform multiple data-driven modeling approaches to infer meaningful insights from data collected in our preliminary studies and in Aim 1. Multilinear regression and partial least squares regression analyses will connect activities of specific signaling pathways with cellular responses, and logic-based modeling approaches will be used to generate cell-specific signaling network models for normal and RA FLS, respectively. In Aim 3 I will predict and test novel protein targets for therapeutic intervention in RA. The predictive models generated in Aim 2 will be used for hypothesis testing in silico, and promising hypotheses will be evaluated experimentally. Collectively, this experimental and computational analysis will significantly increase our understanding of rheumatoid arthritis and generate precise molecular models of events likely to underlie disease. Furthermore, it will create an integrated approach that can be used to identify novel sites for therapeutic intervention in a range of other human diseases.