Our goal is to increase our understanding of the immunopathogenesis of human type 1 diabetes in order to develop novel therapies aimed at increasing insulin secretion and stopping the autoimmune attack. We work in close collaboration with The Network for Pancreatic Donors with Diabetes (nPOD), a biorepository that provides valuable samples from healthy and diabetic organ donors in order to study different aspects of type 1 diabetes. We are particularly interested in the crosstalk between the immune system and the cells that produce insulin, called beta cells. We are working on identifying the immune cells that recognize different beta cell antigens and their exact localization in the pancreas, spleen, lymph nodes and duodenum, in order to better understand the pathogenesis of the disease. We are also trying to elucidate whether viral infections make the islets more accessible for these destructive immune cells or vice versa. Lastly, we are interested in studying if, under conditions of high cellular stress, beta cells make protein processing errors, which could lead to protein modifications. These newly formed proteins could be recognized by infiltrating immune cells and regarded as non-self-antigens, triggering an autoimmune response and causing beta cell destruction.
The insulin secretory granule components and its immune recognition in type 1 diabetes
Type 1 diabetes is a complex and heterogeneous disease that develops when beta cells are destroyed by the immune system. This leads to the lifelong need for exogeneous insulin and a reduction of life expectancy. Beta cells are highly specialized cells and can contain an estimated amount of 10,000 insulin granules, each one containing 200,000 insulin molecules (1). In addition to insulin, these granules contain other important proteins, hormones and ions, such as Islet Amyloid Polypeptide (IAPP), Chromogranins (CHGA and CHGB), Secretogranins (SCG3 and SCG5) and enzymes that process them from precursor proteins. IAPP, which is co-secreted with insulin, is known to inhibit glucagon (1). Low amylin increases basal insulin release and higher concentrations inhibit glucose-induced insulin secretion (1). Understanding how these important proteins interact with each other and with insulin, how they change under conditions of high cellular stress, and which role they might play during the development of type 1 diabetes, is critical to open new opportunities for therapeutic intervention.
In this project, we will obtain human tissue samples from the Network for Pancreatic Organ Donors with diabetes (nPOD) (2). We will study the human pancreas and we will determine the expression, cellular localization and processing of several granule proteins (mainly IAPP, chromogranins and secretogranins) using state-of-the-art microscopy techniques and advanceimage analysis that combines machine-learning algorithms with automated software analysis (3). Then, we will use confocal and super resolution microscopy to visualize potential protein aggregations as well as specific interactions with their processing enzymes. We will use beta cell lines and in vitro experiments as tools to further understand protein dynamics. In the second part of the project, we will focus on investigating if the immune system recognizes these proteins in individuals with and without type 1 diabetes. We will aim to detect antigen-specific CD8 T cells against IAPP, chromogranins and secretogranins in the pancreas and pancreatic lymph nodes, and finally use blood samples to investigate if the detection of these cells could be used as a biomarker or diagnostic tool.
Our approach includes new imaging technologies and analytical tools that will enable us to see inside the beta cell like never before. Ultimately, our work should help to develop novel biomarkers and therapeutic approaches targeting beta cell dysfunction possibly in combination with antigen specific therapies.
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Apaolaza PS, Petropoulou PI, Rodriguez-Calvo T. Whole-Slide Image Analysis of Human Pancreas Samples to Elucidate the Immunopathogenesis of Type 1 Diabetes Using the QuPath Software. Front Mol Biosci. 2021 Jun 11;8:689799. doi: 10.3389/fmolb.2021.689799. PMID: 34179094; PMCID: PMC8226255.
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