Objective
Two of the key immune cells that mediate immunological tolerance are regulatory T cells and effector T cells, and normal function of these immune cells is essential for prevention of islet autoimmunity. It is now well accepted that loss of immune tolerance is a critical part of T1D progression, resulting in destruction of the islet beta cells. However, the exact mechanism of action, the impact of the genetic risk of T1D and the additional environmental stresses that drive progression are still not well understood, despite recent advances in genome wide association studies refining the loci that are linked to disease. What has now become clear is that the loss of stable immune function is also impacted by metabolic stability, as these T cells are sensitive to metabolic stress, and can become skewed to a pro inflammatory phenotype. It is also clear that the beta cells are also sensitive to metabolic stress, and this is amplified by the loss of tight glucose regulation as beta cell function declines. This suggests that when both the immune cells and the beta cells are under stress, each can amplify the loss of function in the other, which may accelerate the changes leading to insulin dependence.
Hence our objective for this grant is to first fully understand these drivers of loss of immune tolerance , and then use high resolution genomics to help us select the best time and targets for intervention to reverse that.
We have established a suite of high resolution genomics approaches that can examine the combined impact of genetic risk and metabolic changes in the immune cells, and by combining it with samples from individuals pre and post seroconversion and islet autoantibody production, we are in a unique position to discover and validate the changes that are drivers of progression, and separate out any changes that are a consequence, as these will not be clinically useful for prevention of T1D, although they may be clinically useful as treatments.
Because we have access to all of the other information gathered by the ENDIA consortium, we will also be able to integrate other changes during seroconversion, such as viral infections, microbiome changes and lifestyle changes, and our objective is to generate a world first atlas of factors that drive T1D progression, by identifying the genes and pathways that are directly changed by all of the factors that conspire to cause progression. We will first functionally confirm them, and then design prevention trials as the long term outcome of this grant. As we appreciate the need for new skills and insights to achieve this, we have grown our research team to include new expertise in genomics and gene regulation, and our team comprises E/MCR and established researchers as part of our plan to train future leaders of T1D research translation.
Background Rationale
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 Type 1 diabetes, 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 Type 1 I diabetes 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 ( know 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 HiC (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 ATACseq 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.
•Metabolic stress can alter T cell function, and accumulation of damage in specialised organelles in the cell , the mitochondria, can predict this.
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.
With this proposal, we will for the first time be able to apply the latest technologies at the highest resolution (on thousands of single cells simultaneously) and from that build a picture of the drivers of T1D progression, which may be targeted in future trials to treat or prevent T1D.
Description of Project
Until Type 1 diabetes is preventable, the search for a cure cannot stop. This project is focused 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.
The goal of this project is to use new cutting edge single cell technologies to unravel the missing link between the genetic risk of type 1 diabetes, altered metabolism and the loss of immune function that leads to islet cell destruction and insulin dependence. We need to achieve this in order to discover the new preventions and cures that will help eliminate T1D, and we have new information suggesting that a strong factor in driving the immune system to attack the pancreas is metabolic instability, as this causes the immune cells to switch from a protective function to an inflammatory function. This metabolic instability affects both the islet cells themselves and the immune cells, causing a dual hit, which may accelerate progression. 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 0-10 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 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 changes in immune cell function from patient samples over time, so that we identify drivers of progression. Theses targets will be validated and tested in future disease prevention trials, and these trials can be tuned to target metabolic pathways to protect both the immune cells and the islets.
Anticipated Outcome
We have 3 main outcomes that we will deliver from this research project:
Our short term outcomes will be:
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 personalised gene diagnosis for genetic risk that is in this map, and the ability to add new genetic risk variants if they are identified in the future.
3) A new understanding of how metabolic stress puts additional pressure on the immune cells, which may exacerbate disease progression.
Using the new information we generate, we will chose the gene targets that are the most changed in the immune cells as disease progresses, so that we can first confirm that they are the drivers of altered immune function, and we will also be able to test whether metabolic reprogramming is accelerating that.
By combining each of these pieces of new knowledge in a deep analysis of multiple individuals using the power of single cell genomics, we will better understand the potential personalised path of T1D progression, and from that hope to generate personalised treatments and cures. Importantly this project will validate the suitability and feasibility of the single cell multi omics approach as the appropriate strategy to be applied to the full ENDIA case cohort, when it becomes available. This will be significant because we can use the full cohort for validation , while strengthening the evidence for of target selection.
The medium term outcome of this study will be that we can commence the meta analysis of this immunogenomics data with other data collected on the ENDIA cohort and already analysed, including the virome, metabolome, and environmental factors collected as part of clinical follow up. This will shed light on any environmental impacts that coincide with the driver changes in the immune cells, which can then be evaluated as potential modifiable factors for intervention.
An additional medium term outcome will be the anility to cross mine our finding in other international T1D cohorts including TEDDY, which will be a critical resource for understanding geographical impacts on T1D, and testing universal target discovery vs. personalised target discovery. In this timeframe it will also be possible to add any additional omics data generated from newly initiated ENDIA collaborator studies using the PBMC, such as methylomics.
The long term outcome if we succeed is to bring personalised medicine to the treatment of Type 1 diabetes, which means therapies will be selected based on the molecular signature identified in each person. We hope that the combined approaches will enable both restoration of immune tolerance, and preservations of beta cell function.
Relevance to T1D
We believe our research program is very relevant to T1D because it is being applied to samples already collected by the ENDIA longitudinal Birth Pregnancy cohort, which has followed 1400 families at risk of T1D over 8 years. This is the only birth cohort currently established that has collected blood samples pre and post seroconversion, and on to insulin dependence, making it a world unique biobank. This biobank was made possible by the generosity of many families affected by type 1 diabetes. We know that over the first 5-10 years, some children will develop autoantibodies, and some will progress to insulin dependence. The ENDIA biobank is hence a living record allowing us to go back in time to find when the critical changes occurred that lead to progression.
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, potential causal links can be made for the first time.
This study aims to identify the changes in the immune system that drive progression, and as we are applying cutting edge genomics to the key cells of the immune system, we will build an atlas of the changes in individuals with risk if T1D.
Our new insights into the impact of metabolic stress have suggested that there is an effect on both the immune system and the pancreatic islets, and this may be an early driver of progression, so we are focused on mapping those changes so they can also be prevented in the future. Given that the loss of tight glucose regulation has a lifelong impact on metabolism, as glucose is one of the main energy sources for all cells, a better understanding of the impact of this on the immune system will provide better understanding of, and new ways to treat the sequalae of T1D.
A key to our approach is that our findings will enable building a more personalised genomics picture for individuals with T1D, as the genetic risk is not identical in all children at risk of T1D, but for those that progress, the outcome is the same. We believe that future immunotherapies that can prevent or reverse T1D will need to be tailored to the individuals genetic and environmental risks.