Periodic Reporting for period 4 - PAINTBOX (Pancreatic islet dysfunction and type 2 diabetes – beyond the genome)
Reporting period: 2022-04-01 to 2023-04-30
The prevalence of type 2 diabetes (T2D) is rapidly increasing worldwide and despite great efforts to prevent and cure the disease, the increase persist. There is an urgent need to develop innovative and effective prevention and treatment strategies for T2D. Recent genome-wide association studies were expected to uncover disease-causing mechanisms in common metabolic disorders. Surprisingly, the identified genetic variants only explain a small proportion of the estimated heritability of T2D, the world’s most rapidly increasing metabolic disorder. Novel studies, going beyond the genome, are therefore needed to identify key mechanisms causing T2D. These studies should focus on pancreatic islets, the tissue of primary pathogenetic importance for diabetes. Also, mechanisms that allow environmental factors to come into play are likely to be important. Epigenetics fulfill such criteria, and our previous work has pioneered epigenetic discoveries in T2D. PAINTBOX is a multidisciplinary program of research through which I will combine advanced sequencing technologies and novel epigenetic methods with mathematical modeling and clinical medicine to develop new prediction and treatment strategies for T2D. Innovative techniques and access to some of the world´s best patient cohorts permit us to address the role of epigenetics in the development of T2D in radically new ways.
I expect this novel interdisciplinary project to generate groundbreaking information that opens up new horizons for prediction, prevention and treatment of T2D and it will thereby help many patient with the disease and reduce health-related costs for society.
The overall objective is to find new mechanisms that contribute to the development of T2D as well as new biomarkers that predict the diease and potential new therapies.
I expect this novel interdisciplinary project to generate groundbreaking information that opens up new horizons for prediction, prevention and treatment of T2D and it will thereby help many patient with the disease and reduce health-related costs for society.
The overall objective is to find new mechanisms that contribute to the development of T2D as well as new biomarkers that predict the diease and potential new therapies.
Aim 1 Impact of T2D, and its risk factors, on the genome-wide epigenome in human pancreatic islets and Aim 3. Phenotypic analysis of human pancreatic islets and functional studies: These parts of PAINTBOX went very well. Within these two aims, we have written several manuscripts, including three review papers where we summarize current knowledge in the research field of epigenetics and type 2 diabetes (1, 11, 12). We have studied how glucose and palmitate impact DNA methylation and gene expression in human islets and beta-cells (3-4, 14) and how type 2 diabetes impacts the chromatin structure (ATAC-seq) in human islets (6). We have investigated how type 2 diabetes impacts gene expression in human islets and identified novel candidates e.g. PAX5, which influence insulin secretion (15). We have analyzed DNA methylation and gene expression in human islets from donors with type 2 diabetes and non-diabetic controls. In this unpublished manuscript, we also included data that covers aim 4 of PAINTBOX (ms i). We tested if DNA methylation in blood associate with future T2D. We have performed functional analysis, where we follow up some of the novel candidates that show differential DNA methylation and gene expression in human islets from donors with type 2 diabetes. Here, we silenced expression of several candidates in clonal beta-cells and human islets and these experiments identified mechanisms that can provide explanation for impaired insulin secretion in type 2 diabetes. These analyses have been performed by Charlotte Ling, Tina Rönn, Karl Bacos, Sabrina Ruhrmann, Jones Ofori and Alexander Perfilyev within our research group. Johanna Sernevi Säll is also still working on the analysis of histone modifications.
Unpublished manuscripts
i) Genes with epigenetic alterations in human pancreatic islets impact mitochondrial function, insulin secretion, and type 2 diabetes. Tina Rönn1, Alexander Perfilyev1, Jones K. Ofori1, Anna Wendt2, Petr Volkov3, Sabrina Ruhrmann1, Lena Eliasson2, Karl Bacos1 and Charlotte Ling1 (ms ready to be resubmitted to Nature Communications)
Aim 2. Development of a mathematical platform for analysis of genome-wide human data: Here, we have utilized a Machine Learning approach for performing an integrative analysis of methylation, gene expression, genetic variation and phenotypic information in human pancreatic islets from 110 individuals, with approximately 30% being cases with type 2 diabetes. Charlotte Ling, Tina Rönn and Alexander Perfilyev have been involved in this part of the project.
Unpublished manuscripts
ii) Predicting Type 2 Diabetes via Machine Learning Integration of Multiple OMICs from Human Pancreatic Islets. Tina Rönn, Alexander Perfilyev, Nikolay Oskolkov# and Charlotte Ling# #equal contribution
Aim 4. Epigenetic mechanisms for prediction and treatment of T2D: We have studied blood-based epigenetic biomarkers for prediction of both type 2 diabetes and therapy and studied epigenetics drugs. We published several papers related to these findings, including identifying DNA methylation of specific sites in blood associated with future type 2 diabetes (9), associated with response to metformin (8), and subgroups of type 2 diabetes (13). We also identified methylation sites in cord blood associated with a lifestyle intervention in obese pregnant women and with gestational weight gain (10, 16). We have also identified inhibitors of epigenetic enzymes that impact insulin secretion and may be further developed as novel therapies for type 2 diabetes (2, 7). We have also developed epigenetic editing tools. Charlotte Ling, Tina Rönn, Sabrina Ruhrmann, Josefine Jönsson, Karl Bacos and Alexander Perfilyev have been involved in this part of the project.
Unpublished manuscripts
i) Genes with epigenetic alterations in human pancreatic islets impact mitochondrial function, insulin secretion, and type 2 diabetes. Tina Rönn1, Alexander Perfilyev1, Jones K. Ofori1, Anna Wendt2, Petr Volkov3, Sabrina Ruhrmann1, Lena Eliasson2, Karl Bacos1 and Charlotte Ling1 (ms ready to be resubmitted to Nature Communications)
Aim 2. Development of a mathematical platform for analysis of genome-wide human data: Here, we have utilized a Machine Learning approach for performing an integrative analysis of methylation, gene expression, genetic variation and phenotypic information in human pancreatic islets from 110 individuals, with approximately 30% being cases with type 2 diabetes. Charlotte Ling, Tina Rönn and Alexander Perfilyev have been involved in this part of the project.
Unpublished manuscripts
ii) Predicting Type 2 Diabetes via Machine Learning Integration of Multiple OMICs from Human Pancreatic Islets. Tina Rönn, Alexander Perfilyev, Nikolay Oskolkov# and Charlotte Ling# #equal contribution
Aim 4. Epigenetic mechanisms for prediction and treatment of T2D: We have studied blood-based epigenetic biomarkers for prediction of both type 2 diabetes and therapy and studied epigenetics drugs. We published several papers related to these findings, including identifying DNA methylation of specific sites in blood associated with future type 2 diabetes (9), associated with response to metformin (8), and subgroups of type 2 diabetes (13). We also identified methylation sites in cord blood associated with a lifestyle intervention in obese pregnant women and with gestational weight gain (10, 16). We have also identified inhibitors of epigenetic enzymes that impact insulin secretion and may be further developed as novel therapies for type 2 diabetes (2, 7). We have also developed epigenetic editing tools. Charlotte Ling, Tina Rönn, Sabrina Ruhrmann, Josefine Jönsson, Karl Bacos and Alexander Perfilyev have been involved in this part of the project.
Several of the publication included in PAINTBOX generated results which advanced the research field beyond state of the art. For example we identified for the first time blood-based epigenetic biomarkers that can be used for precision medicine in type 2 diabetes (10.1126/scitranslmed.aaz1803) we showed for the first time that there are epigenetic differences between the novel subgroups of type 2 diabetes and these epigenetic markers are associated with future diabetic complications (10.2337/dc21-2489) we were the first to identify alterations in open chromatin structure in islets from individuals with type 2 diabetes versus controls (10.1038/s41598-019-44076-8) we developed a new mathematical platform for multiomics, and we identified novel candidates for type 2 diabetes such as PAX5 (10.1172/jci163612).