Project P-C: Disease modelling with the PSC Integrative Data Environment (PrIDE)

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While genetic studies earmark PSC as an autoimmune-disease, it shows limited response to immunosuppressants and is therefore believed to be a complex, multifactorial immune-mediated disorder. It is highly likely that current PSC diagnosis, therefore, encompasses several different hepatobiliary diseases. The poor understanding of the pathomechanisms and the disease heterogeneity could explain the lack of an approved medical treatment for PSC.In this project we hypothesize that PSC pathophysiology is dependent on the interplay of the immune system, the microbiome, infections, and the genetic predisposition. We believe that PSC is not a single disease but rather a spectrum of several sub-diseases, which can be stratified based on clinical (age, sex, comorbidity) as well as genetic and molecular information (genome mutations, microbiome, infections, immune status). We propose to model PSC across data types to understand how the immune system, the microbiome, and genetic and clinical factors predict, explain, and stratify PSC.To this end, we will (i) develop and deploy the PSC Integrative Data Environment (PrIDE), a semantic integration platform that will allow for the interactive querying, visualization, and analysis of PSC-related data and results. PrIDE will contain data and results ranging from high-throughput transcriptional profiling of large and small RNAs by next-generation sequencing (NGS), single cell RNA-seq, 16S RNA-seq, WE-seq, FACS, and cytokine measurements to clinical information on patients. The integration platform PrIDE will enhance the cross-project collaborations within this CRU, enabling researchers to query across molecular (omics) and clinical data, and to mix and combine information from public resources to build hypotheses that can be experimentally validated. Ultimately, PrIDE will be made publically available, allowing for the federated querying of available biomaterials, data, and analysis results by the scientific community.We will then (ii) use PrIDE’s deep phenotyping information to answer very specific, hypothesis-driven research questions. More specifically, in the fiuse-case first we want to understand the impact of the microbiome and pathobionts on immunity and PSC. We would like to use this information to stratify PSC into subtypes based on comorbidities such as inflammatory bowel disease (IBD), cholangiocarcinoma (CCA), and/or the microbiome and infections, using mainly existing data of the clinical research unit.In our second data integration use-case we would to determine the translational value of animal models of PSC.