AS Associated Network Modules

Research into AS mostly focuses on specific dysfunctional proteins in diseases. We suggest that AS has much more subtle but widespread consequences on a systems biology level. Using data from SP1, we will apply and develop innovative network analysis approaches to unravel how AS affects the transcriptome and proteome in heart and kidney disease. SP3 will directly benefit from SP2 which studies AS-related changes of protein interaction. Our aim is to identify network modules that reflect disease-associated AS mechanisms, novel biomarkers and putative drug targets that can subsequently be validated in vivo and in vitro by SP4.
We will extract network modules enriched for AS events to gain mechanistic insights into the effects of AS on DCM and HN. Furthermore, we will use de novo endophenotyping to identify AS-associated disease subtypes with clinically exploitable potential. To this end, we will develop a novel approach for graph-restricted biclustering of patients and molecular subnetworks. Results will be inspected in SP4 to gain understanding of mechanistic differences between patient groups and to guide in vivo validation.
We will use time series data from SP 4 to study dynamic patterns of AS in the interactome to infer causality. Moreover, we will generate predictive models for important disease characteristics based on transcriptomic data from tissue and blood samples. A performance comparison of the models will reveal to what extent easily obtainable blood-based measurements can substitute biopsies based on predictive mechanistic markers. Finally, we will study the effect of AS on gene regulation by developing a novel method to identify changes in the abundance of binding sites for miRNA. We will subsequently extract network modules enriched for binding-site gain or loss.
In SP3, we propose a comprehensive view on AS from a networks-perspective where each work package follows a unique and innovative approach to identify disease-associated AS events for follow-up studies.

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