COMET common mechanisms in autoimmunity
Dr. Pieber and his team will apply highly sophisticated computerized machine-learning approaches on existing data from people with type 1 diabetes, systemic lupus erythematosus, multiple sclerosis, and rheumatoid arthritis and healthy volunteers, to identify the changes in different immune cells that are shared between the autoimmune diseases or unique to each. (Machine learning are computer programs that improve automatically through experience, which can help researchers find patterns from large-scale datasets.) This will generate a deeper understanding of commonalities and differences of immunological patterns in type 1 diabetes, lupus, multiple sclerosis, and rheumatoid arthritis, and will identify important pathways in immune cells that can then be tested for potential therapies.
Autoimmune diseases have high prevalence and they cause considerable morbidity and mortality. They can manifest either as organ-specific diseases such as Type 1 Diabetes and Multiple Sclerosis, or as systemic diseases such as Systemic Lupus Erythematosus and Rheumatoid Arthritis. Such autoimmune diseases exhibit strong clinical heterogeneity, they are triggered by genetic and environmental factors. Despite the genetic and phenotypical heterogeneity in several autoimmune diseases, common pathways leading to the breakdown of self-tolerance exist. Objective of the proposed project is to identify common or differential patterns across organ-specific and systemic autoimmune diseases. To do so, we will apply machine-learning approaches on existing data from patients with Type 1 Diabetes, Systemic Lupus Erythematosus, Rheumatoid Arthritis and Healthy participants and on within this project collected data from Multiple Sclerosis patients. Machine learning approaches and pathway identification applied on these collected clinical, immunological and metabolic data will help to elucidate the role of different cells that play a central role in the immune response and will generate a deeper understanding of commonalities and differences of immunological patterns in Type 1 Diabetes and Multiple Sclerosis or Systemic Lupus Erythematosus and Rheumatoid Arthritis. The goal of the proposed studies is to successfully modulate the immune system to induce tolerance development in autoimmune diseases. Based on our finding, new biological drugs that are available to stimulate or block particular pathways in immune cells can be tested in early clinical trials. The proposed work as high relevance for Type 1 Diabetes patients. Type 1 Diabetes is increasing both in incidence and prevalence worldwide, with the greatest increases among children younger than 15 years, particularly in those younger than 5 years. The overall lifetime risk varies greatly by country and geographical region but is around one in 200 to 250 people. In the recent three decades several immune interventions targeting the immune system to reduce cytotoxicity or to induce immune tolerance have been tested in either high risk first-degree relatives or in newly diagnosed Type 1 Diabetes patients. Only a few categories of drugs have shown some efficacy in preserving C-peptide secretion in these patient groups, and none is currently approved for clinical use. This highlights the urgent need for a deeper understanding of the interaction between the beta-cells and the immune system and modifiers of these interaction. A systematic deep immunological and metabolomic phenotyping will identify important pathways and will allow the testing of new drug candidates, either identified through effectiveness in other autoimmune diseases or through design.