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

虽然遗传学研究表明PSC是一种自身免疫性疾病，但它对免疫抑制剂的反应有限，因此被认为是一种复杂的、多因素免疫介导的疾病。因此，目前的PSC诊断很可能包括几种不同的肝胆疾病。对病理机制和疾病异质性的了解不足可以解释PSC缺乏批准的药物治疗。在这个项目中，我们假设PSC的病理生理依赖于免疫系统、微生物群、感染和遗传易感性的相互作用。我们认为PSC不是一种单一疾病，而是几个亚疾病的谱系，可以根据临床（年龄、性别、合并症）以及遗传和分子信息（基因组突变、微生物组、感染、免疫状态）进行分层。我们建议跨数据类型对PSC进行建模，以了解免疫系统、微生物组、遗传和临床因素如何预测、解释PSC并对其进行分层。为此，我们将(i)开发和部署PSC集成数据环境（PrIDE），这是一个语义集成平台，可以对PSC相关数据和结果进行交互式查询、可视化和分析。PrIDE将包含从下一代测序（NGS）、单细胞RNA-seq、16S RNA-seq、WE-seq、FACS和细胞因子测量到患者临床信息的高通量大rna和小rna转录谱分析的数据和结果。整合平台PrIDE将加强该CRU内的跨项目合作，使研究人员能够查询分子（组学）和临床数据，并混合和组合来自公共资源的信息，以建立可以实验验证的假设。最终，PrIDE将向公众开放，允许科学界对现有生物材料、数据和分析结果进行联合查询。然后，我们将（ii）使用PrIDE的深层表型信息来回答非常具体的，假设驱动的研究问题。更具体地说，在融合病例中，我们首先要了解微生物组和病原体对免疫和PSC的影响。我们希望利用这些信息，根据合并症（如炎症性肠病（IBD）、胆管癌（CCA）和/或微生物组和感染）将PSC分层为亚型，主要使用临床研究单位的现有数据。在我们的第二个数据集成用例中，我们将确定PSC动物模型的转化价值。

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.