Deciphering the role of adipose tissue in common metabolic disease via adipose tissue proteomics
Lead Research Organisation:
King's College London
Department Name: Twin Research and Genetic Epidemiology
Abstract
Type 2 diabetes and metabolic disease are a major cause of disease worldwide and an increasing global problem affecting economic prosperity and quality of life. While increases in obesity and lifespan in modern societies are contributors to the increased prevalence of these diseases, it is clear that genetic factors are also important in determining who develops disease and who remains healthy. Adipose tissue (fat) is an endocrine organ which plays a central role in the regulation of metabolism, inflammation, synthesis and secretion of hormones. A significant portion of inter-individual differences in susceptibility to metabolic disorders is driven by molecular processes within adipose tissue. However, which genes are implicated in development of adipose tissue dysfunction and how they link to risk of metabolic disease is not well understood. Uniquely among the main tissues implicated in metabolic disease (pancreas, muscle, fat, liver), adipose tissue is relatively accessible from research volunteers. Adipose tissue thus provides an unparalleled opportunity to investigate molecular processes in a metabolic disease-relevant tissue in longitudinal population cohorts.
To address this opportunity, we have designed a project that will investigate the important relationships between metabolic health, adipose molecular function and genetic risk of disease, by unbiased profiling, in depth, of adipose tissue protein and protein post translational modification (PTM) levels, in a set of 1,000 deeply phenotyped twins, [with up to 30 years of detailed metabolic and clinical measurements]. There are many steps between genetic variants and disease, but at a cellular level it is the proteins produced from genes and the functions the proteins perform that drive disease susceptibility. Therefore, the focus of this study on deep, quantitative protein analysis in the relevant tissue will provide a major advance in the field that can reveal unique new insights. Proteins are also the target of most clinical drugs, providing a direct link to clinical intervention, while the modulation of protein function by reversible phosphorylation and other PTMs is a key feature of signalling pathways that control metabolism and that are targeted by many therapeutics. The comprehensive, systematic and unbiased global analysis of proteins and their PTMs at scale (defining 'the proteome') is only now possible thanks to recent technological advances in mass spectrometry (MS)-based methods, which can now support identification and quantification of deep proteomes (i.e. > 6,000- proteins), with isoform resolution and identification of post-translational modifications, across many hundreds of individual samples.
To our knowledge, this study will produce one of the largest solid tissue proteome datasets available from a human population cohort. We will use these novel data to characterize the breadth and genetic architecture of the adipose proteome and identify its role in development of metabolic disease and associated traits. Using the classic twin model, we will estimate the heritability of each quantified protein to assess the relative contribution of genetics and environment in this unique population dataset. Combining the adipose proteome with existing genomic data will identify genetic variants regulating protein levels. We will use concurrently measured clinical phenotypes collected on the profiled twins to identify proteins linked to changes in clinically relevant traits. We will use statistical methods to integrate our results with large Biobanks to identify molecular mechanisms underlying genetic risk of metabolic traits, and to identify new genetic regions driving risk of metabolic disease. This work will provide novel insights into how metabolic problems develop and discover key genes involved, identifying important targets for future therapy and diagnostics.
To address this opportunity, we have designed a project that will investigate the important relationships between metabolic health, adipose molecular function and genetic risk of disease, by unbiased profiling, in depth, of adipose tissue protein and protein post translational modification (PTM) levels, in a set of 1,000 deeply phenotyped twins, [with up to 30 years of detailed metabolic and clinical measurements]. There are many steps between genetic variants and disease, but at a cellular level it is the proteins produced from genes and the functions the proteins perform that drive disease susceptibility. Therefore, the focus of this study on deep, quantitative protein analysis in the relevant tissue will provide a major advance in the field that can reveal unique new insights. Proteins are also the target of most clinical drugs, providing a direct link to clinical intervention, while the modulation of protein function by reversible phosphorylation and other PTMs is a key feature of signalling pathways that control metabolism and that are targeted by many therapeutics. The comprehensive, systematic and unbiased global analysis of proteins and their PTMs at scale (defining 'the proteome') is only now possible thanks to recent technological advances in mass spectrometry (MS)-based methods, which can now support identification and quantification of deep proteomes (i.e. > 6,000- proteins), with isoform resolution and identification of post-translational modifications, across many hundreds of individual samples.
To our knowledge, this study will produce one of the largest solid tissue proteome datasets available from a human population cohort. We will use these novel data to characterize the breadth and genetic architecture of the adipose proteome and identify its role in development of metabolic disease and associated traits. Using the classic twin model, we will estimate the heritability of each quantified protein to assess the relative contribution of genetics and environment in this unique population dataset. Combining the adipose proteome with existing genomic data will identify genetic variants regulating protein levels. We will use concurrently measured clinical phenotypes collected on the profiled twins to identify proteins linked to changes in clinically relevant traits. We will use statistical methods to integrate our results with large Biobanks to identify molecular mechanisms underlying genetic risk of metabolic traits, and to identify new genetic regions driving risk of metabolic disease. This work will provide novel insights into how metabolic problems develop and discover key genes involved, identifying important targets for future therapy and diagnostics.
Technical Summary
Genome-wide associations studies (GWAS) have identified thousands of genetic variants associated with complex traits, but a key challenge for the field is identifying how these variants mechanistically impact risk of disease. Critically, on a cellular level, the unit of action of most genes is their protein products. As most drugs in use directly target proteins, and many drugs affect post-translational modifications of target proteins, linking proteins to genetic variants and clinical phenotypes can provide a direct route to clinical intervention. The comprehensive, unbiased analysis of proteins at a global scale is now possible, thanks to technological advances in mass spectrometry, but this has not yet been widely applied to human tissues at a population scale.
Adipose tissue (fat) is a dynamic endocrine organ that mediates a large fraction of known metabolic trait GWAS loci. To investigate the relationships between metabolic health, adipose tissue function and genetic risk of disease, this proposal will use state-of-the-art mass-spectrometry proteomics technology to profile protein levels and post-translational modifications (PTM) in adipose tissue in a cohort of 1,000 deeply phenotyped twins with existing longitudinal genetic, metabolic and health measurements. We will map protein, peptide and PTM Quantitative Trait Loci (QTLs) thereby identifying genetic variants regulating protein levels. We will use statistical techniques to integrate the adipose protein QTL data with large biobanks and GWAS studies to identify proteins that mediate genetic risk of metabolic disease and associated traits. The data produced, including protein quantifications and pQTLs/pepQTLs/PTMQTLs, will be freely released, empowering multiple downstream uses by the community. The expected outcomes of the study are important new insights into how metabolic problems develop, the discovery of key genes involved and the identification of protein targets for future therapy and diagnostics.
Adipose tissue (fat) is a dynamic endocrine organ that mediates a large fraction of known metabolic trait GWAS loci. To investigate the relationships between metabolic health, adipose tissue function and genetic risk of disease, this proposal will use state-of-the-art mass-spectrometry proteomics technology to profile protein levels and post-translational modifications (PTM) in adipose tissue in a cohort of 1,000 deeply phenotyped twins with existing longitudinal genetic, metabolic and health measurements. We will map protein, peptide and PTM Quantitative Trait Loci (QTLs) thereby identifying genetic variants regulating protein levels. We will use statistical techniques to integrate the adipose protein QTL data with large biobanks and GWAS studies to identify proteins that mediate genetic risk of metabolic disease and associated traits. The data produced, including protein quantifications and pQTLs/pepQTLs/PTMQTLs, will be freely released, empowering multiple downstream uses by the community. The expected outcomes of the study are important new insights into how metabolic problems develop, the discovery of key genes involved and the identification of protein targets for future therapy and diagnostics.
