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Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.

Background Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs. Methods The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs. Results We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated this hypothesis in a large-scale study of patients with 13 different autoimmune, allergic, infectious, malignant, endocrine, metabolic, and cardiovascular diseases, as well as a therapeutic study of the mouse arthritis model. Conclusions Overall, our results support that our strategy has the potential to help prioritize diagnostic and therapeutic targets in human disease.

Background Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery. Methods We started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases. Results Our scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs. Conclusions We present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.

Background Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. Methods Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. Results scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn’s disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn’s disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. Conclusions We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio’s potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

Screening programs for colorectal cancer (CRC) often rely on detection of blood in stools, which is unspecific and leads to a large number of colonoscopies of healthy subjects. Painstaking research has led to the identification of a large number of different types of biomarkers, few of which are in general clinical use. Here, we searched for highly accurate combinations of biomarkers by meta-analyses of genome- and proteome-wide data from CRC tumors. We focused on secreted proteins identified by the Human Protein Atlas and used our recently described algorithms to find optimal combinations of proteins. We identified nine proteins, three of which had been previously identified as potential biomarkers for CRC, namely CEACAM5, LCN2 and TRIM28. The remaining proteins were PLOD1, MAD1L1, P4HA1, GNS, C12orf10 and P3H1. We analyzed these proteins in plasma from 80 patients with newly diagnosed CRC and 80 healthy controls. A combination of four of these proteins, TRIM28, PLOD1, CEACAM5 and P4HA1, separated a training set consisting of 90% patients and 90% of the controls with high accuracy, which was verified in a test set consisting of the remaining 10%. Further studies are warranted to test our algorithms and proteins for early CRC diagnosis.

Background. Unbiased studies using different genome-wide methods have identified a great number of candidate biomarkers for diagnosis and treatment response in pediatric ulcerative colitis (UC). However, clinical translation has been proven difficult. Here, we hypothesized that one reason could be differences between inflammatory responses in an inflamed gut and in peripheral blood cells. Methods. We performed meta-analysis of gene expression microarray data from intestinal biopsies and whole blood cells (WBC) from pediatric patients with UC and healthy controls in order to identify overlapping pathways, predicted upstream regulators, and potential biomarkers. Results. Analyses of profiling datasets from colonic biopsies showed good agreement between different studies regarding pathways and predicted upstream regulators. The most activated predicted upstream regulators included TNF, which is known to have a key pathogenic and therapeutic role in pediatric UC. Despite this, the expression levels of TNF were increased in neither colonic biopsies nor WBC. A potential explanation was increased expression of TNFR2, one of the membrane-bound receptors of TNF in the inflamed colon. Further analyses showed a similar pattern of complex relations between the expression levels of the regulators and their receptors. We also found limited overlap between pathways and predicted upstream regulators in colonic biopsies and WBC. An extended search including all differentially expressed genes that overlapped between colonic biopsies and WBC only resulted in identification of three potential biomarkers involved in the regulation of intestinal inflammation. However, two had been previously proposed in adult inflammatory bowel diseases (IBD), namely, MMP9 and PROK2. Conclusions. Our findings indicate that biomarker identification in pediatric UC is complicated by the involvement of multiple pathways, each of which includes many different types of genes in the blood or inflamed intestine. Therefore, further studies for identification of combinatorial biomarkers are warranted. Our study may provide candidate biomarkers for such studies.

An amendment to this paper has been published and can be accessed via the original article.

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. 1. First, we tested if high-resolution disease models based on single cell RNA-sequencing (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 antigen induced 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. 2. 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 from joint and blood Granulocytes of our AIA mouse model. Based on differentially expressed genes (DEGs) between sick and healthy samples from each dataset, we performed pathway analyses and predicted potential biomarkers and upstream regulators (URs). Comparing the lists of pathways, biomarkers, and URs between the datasets from different subsets of blood samples showed low or no similarities. However, the datasets of human bulk or mouse single cell data collected from synovial fluid or full joint showed high similarities. Furthermore, the top shared enriched pathways, predicted biomarkers, and URs from both human and mouse were to a higher degree connected to known functions of autoimmune diseases or rheumatoid arthritis, compared to the respective results from samples taken from blood. These findings indicate that inflammatory mechanisms in cells in blood and inflamed organs differ greatly, which may have important diagnostic and therapeutic implications. 3. We next analyzed if digital twins could be used to identify the early regulatory mechanisms that are also present at the late time points. For this, we used an in vitro time series model of seasonal allergic rhinitis. Samples were taken before allergen stimulation, as well as at 12 hours, 1 day, 2 days, 3 days, 5 days, and 7 days after allergen stimulation, for scRNA-seq and MCDM construction. Multi-directional interactions including all cell types were found at all time points, even before allergen stimulation, which complicated the identification of one key regulatory cell type or gene. Instead, we found that the regulatory genes could be ranked based on their overall downstream effect over all the time points. Our top-ranked regulatory gene, PDGFB, targeted most of the cell types at all the time points, while a previously known early regulator and drug target in allergy, IL4, targeted only five cell type and time point combinations. Validation studies further showed that neutralization of PDGF-BB on allergen-stimulated PBMC from SAR patients were more effective compared to neutralization of IL-4. 4. Finally, we tested if a digital twin including data from multiple organs could be used to understand the systemic interactional changes due to disease. For this aim, we used a systemic mouse model of arthritis, namely collagen induced arthritis (CIA). We first analyzed ten different organs, based on which we prioritized five organs with the highest number of DEGs between CIA and healthy mice, namely joint, lung, muscle, skin, and spleen. Although only joint showed signs of inflammation, many DEGs were identified in all five organs. Those changes were organized into a multi-organ multi-cellular disease model, which indicated an on/off switch of pro-/anti-inflammatory functions in joint and muscle respectively. Validation studies in human immune-mediated inflammatory diseases supported this on/off switch, where pro-inflammatory functions were mainly found in inflamed organs, while anti-inflammatory functions were found in non-inflamed organs. In conclusion, this thesis supports the potential of using high-resolution disease models for digital twin construction. Such digital twins could then be used to prioritize cell types and genes, for further prediction of diagnostic markers and therapeutic targets. Even though the identification of one key regulatory function was complicated due to multidirectional interactions, the genes could be ranked based on their relative downstream effect. For reproducible results, we found that digital twins should ideally be based on data from locally inflamed organs, while systemic models and models covering different disease stages could be useful to understand the disease progression.

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive , and studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).