Digital Twins: High Resolution Disease Models for Optimized Diagnosis and Treatment

To study immune-mediated diseases, which can affect the expression of thousands of genes among many different cell types and organs, is a daunting challenge. However, for effective diagnosis and therapeutic treatment it is relevant to understand the regulatory functions of disease. In this thesis, we hypothesized that regulatory functions in complex diseases can be effectively prioritized based on so called digital twins, which are based on high-resolution single cell data in combination with network theories. More specifically, we tested if digital twins could be used on a patient-group level to prioritize cell types, genes, and/or organs based on their regulatory function in the disease progression. If this hypothesis is true, potential biomarkers and therapeutic targets can be identified for optimized diagnosis and treatment. The long-term goal is to construct digital twins for personalized medicine, to predict the optimal treatment strategies for the individual patients. Although, this is a very ambitious goal which could not be reached through this thesis, relevant steps towards it have been reached. First, we tested if high-resolution disease models based on single cell RNAsequencing (scRNA-seq) data could be used in combination with network theories, to predict and prevent disease. For this aim, we used a mouse model of antigeninduced arthritis (AIA). Based on the cell type specific genes in AIA joint, we identified a multi-cellular disease model (MCDM), including predicted cell-cell interactions. Analyzing this model, Granulocytes were identified as most central in AIA joint. The results from this centrality analysis correlated with GWAS enrichment among the cell type specific genes, as well as with the centrality analyses based on human RA, supporting our results relevance for human disease. A drug, bezafibrate, was further identified which mainly targeted shared disease modules over the central and GWAS enriched CD4+ T cells in nine of 13 analyzed human diseases. Bezafibrate treatment of our AIA mouse model resulted in a decrease in arthritis severity score as well as a decrease in T cell proliferation into the joint. Since blood is an easily available source of data, it is of interest to know it’s potential usefulness when constructing digital twins. To test if samples taken from blood are representative of the inflamed organ, we performed a meta-analysis of different samples from blood and joint of patients with rheumatoid arthritis, as well as