Objective
Many advances have been made in understanding the clinical progression of type 1 diabetes, and improving treatment to reduce the impact of dysregulated glucose metabolism. These include a new staging of the disease,
recognising that autoantibodies precede insulin dependence, and improving the diagnostic toolkit to identify these auto antibodies early, and also improvements in insulin therapy, such as continuous glucose monitoring, to fine tune
the insulin dose. These have significantly improved the quality of life and reduced long term consequences of T1D, but there is still no cure, and more can be done.
The genomics era has enabled very large genetic risk studies to be performed, and these have been successful in linking single DNA base pair changes to the disease, but these studies cannot determine cause, without which
prevention strategies are difficult to develop. As advances in molecular biology become available, the true complexity of type 1 diabetes is revealed, and there are now many layers of complexity that need to be understood
to solve the puzzle of what causes T1D and how can we prevent it.
The pieces of the puzzle:
• Autoimmune diseases have an immune tolerance defect - Autoimmune diseases are complex disorders caused by a failure of immune tolerance, and regulatory T cells and conventional T cells play a key role in maintaining
tolerance, so these must be studied.
• Autoimmune diseases have a genetic risk: Genome-wide association studies (GWAS) have identified more than 200 regions of genetic variations linked to increased autoimmune disease risk, and ~50 loci are linked to T1D. Critically, more than 80% of the disease-linked variation are in non-coding regions, not in gene coding regions, so are working indirectly in specific cells.
• Autoimmune disease genetic risk affects T cell specific enhancers: Up to 60% of these candidate autoimmune disease-associated variants are localised to regulatory elements (known as enhancers) that are specifically active in
T cells. These are likely to alter T cell function so these must be connected to genes.
• Enhancers control genes over a long distance by DNA looping: Only by connecting these enhancers to the genes they regulate can the targets of genetic risk be identified. We have now completed this in human Treg by a new
technique called Hi-C (whole genome chromosome conformation capture).
• Only regions of the genome that are active in T cells can cause loss of tolerance: A new high resolution genomic approach for mapping T cell-specific active genes called ATAC-seq has been invented, and it has the power to find
changes in gene regulation in rare cells from the immune system. We have established this method and tested it in a pilot T1D cohort.
• To separate cause and effect of these layers of gene control, we must look in real patient samples over the course of disease progression, which requires a very carefully controlled clinical cohort, which we have established, called
ENDIA. The ENDIA biobank contains enough samples to ask which genes are altered in the immune cells that are critical, and when do they change in progression.
Our objective is to stepwise address these knowledge gaps, so that we can identify only the key genes that drive T1D progression in individuals with different genetic risk, so that these can be tested in future prevention trials.
Background Rationale
T1D occurs when genetic risk, environmental exposures and other risk factors combine to trigger altered immune function, resulting in loss of immune tolerance. Testing new targeted treatments to prevent or
reverse this loss of tolerance will only become possible once the functional link between the risk and its targets has been made in the immune cells. However, these targets must also be linked to progression, to separate cause (identifying new preventions) and effect (identifying new treatments). Here, we will apply a novel three-dimensional genomics approach utilizing samples from the internationally unique Environmental Determinants of Islet
Autoimmunity (ENDIA) longitudinal birth cohort to map the molecular mechanism of T1D.
The current challenge: Genetic risk is mostly found in non-coding regions of the genome, and these regions contain the regulatory elements (enhancers) that control gene expression. Because many enhancer-gene interactions occur
over long distances by DNA looping, without the map of DNA looping, we can’t connect the genetic risk (Single Nucleotide Polymorphisms; SNPs) to the genes they impact. But this is not the last piece of information needed to functionally connect the genetic risk to the genes, because while genetic risk is present in the DNA of every cell, it only has an impact on the genes that are active in a given cell type at a given time. Hence, to functionally map the impact of type 1 diabetes genetic variation, we must connect it to the active genes in the right immune cells: those that control tolerance - regulatory T (Treg) cells and conventional T (Tconv) cells.
The solution: We have developed and validated a novel high resolution genomics pipeline (shown in the Research Plan) to functionally annotate all of the Type 1 diabetes genetic risk regions, and we have used a small T1D case control cohort to test the power of this approach on clinical samples (12 cases vs 12 controls, preliminary data). By applying this pipeline to longitudinal samples over disease progression using the ENDIA T1D biobank, we can start to reveal the link between genotype and altered immune function, and specifically which genes are causing this.
Description of Project
Until Type 1 diabetes (T1D) is preventable, the search for a cure cannot stop. This project is focussed on unravelling the changes in function in rare immune cells that shape immune tolerance, as we believe that these cells are the key to preventing or reversing T1D. This project aims to use cutting edge genomics technologies to carefully connect genetic risk of T1D to the genes that are altered, and to focus on the changes that drive the breakdown in immunological tolerance, so this can be reversed or prevented in the future. To do this we need new knowledge, and we need to test this in samples collected as disease progresses, so that we can separate out cause and effect.
Our approach is to investigate the direct connections between genetic risk for T1D and the exact genes that are disrupted. To date, we have successfully mapped these connections in the immune cells, and we now must add another layer of new knowledge to pick the changes that are drivers of T1D. To do this we must determine which genes are changing and when in the cells we know are critical for immune tolerance. We have developed a tool kit that can do this, and we have tested it in a small T1D cohort to confirm that it works, so we are now seeking to apply it to a larger cohort.
We have reasoned that in order to do this well, we need samples pre and post disease onset, as that is the only way to separate out the changes that might drive progression, rather than be a result of it. The ENDIA cohort is a world
first birth pregnancy cohort following 1400 participants in families with a child at risk of T1D, following them from 0-5 years of life. This cohort is designed to include family members who are not at risk of T1D, so that a very controlled analysis can be made, and we have permission to access this biobank for this project.
By performing our genomics approaches on highly purified immune cells, we will be able to connect genetic risk to the gene(s) it alters. This cannot be predicted from the sequence alone because we now know that genetic risk is scattered throughout the genome, and the majority of this genetic risk is not in genes themselves, but in regions that control gene expression, called enhancers. This required new knowledge because the genes are controlled by these enhancers over long distances, and there are no programs that can predict these connections, as they are cell type specific, and can connect many genes to each enhancer. Now we have these connections mapped, the project is
ready to start examining the patient samples over time.
The two outcomes of this study will be a diagnosis for genetic risk that is linked to specific genes, and a set of new target genes to be tested in future trials to restore immune tolerance or to prevent it from becoming disrupted in
children at risk of T1D.
Anticipated Outcome
We have 3 main outcomes that we will deliver from this innovative pilot project:
1. A world first map of the connections between the known genetic risk regions for type 1 diabetes and the genes that they potentially alter in immune cells.
2. A potential gene diagnosis for each genetic risk variation that is in this map, and the ability to add new genetic risk variants if they are identified in the future.
3. The selection of the target genes that we demonstrate cause changes in immune tolerance in the cells that are specialised to do this, which can then be prioritised for new clinical trials in individuals that have the diagnosis.
The objective is to accelerate the development of T1D therapies that delay, stop or reverse the development and progression of T1D, and to bring personalised medicine to the treatment of T1D, which will be selected based on the molecular defect identified in each person.
Relevance to T1D
This Proposal has very direct relevance to T1D because it is going to use blood samples collected from children enrolled in the ENDIA study. This is a living biobank of the immune cells from children who are at risk of developing T1D, and it has been collected over the first 3-5 years of life. This biobank is the only one of its type in teh world, and was made possible by the generosity of many families affected by type 1 diabetes. We know that over the timeframe some children will develop autoantibodies, and some will progress to insulin dependence. This living biobank has the power to reveal which changes occur prior to autoantibody detection, and which of those might drive changes in immune cell function. Without this biobank, and carefully selected controls, it is not possible to predict these outcomes, so by combining our cutting edge genomics with the ENDIA biobank, real links can be made for the first time. As this is a pilot study, we will confirm the findings in separate samples, and also confirm that the changes we identify can be reversed. If this is successful, we will seek to test those targets in new clinical trials.