Deciphering the role of adipose tissue in common metabolic disease via adipose tissue proteomics

2型糖尿病和代谢性疾病是世界范围内的主要疾病原因，也是影响经济繁荣和生活质量的日益严重的全球性问题。虽然现代社会中肥胖和寿命的增加是这些疾病流行率增加的原因，但显然遗传因素在决定谁患病和谁保持健康方面也很重要。脂肪组织（脂肪）是一种内分泌器官，在代谢、炎症、激素合成和分泌的调节中起着核心作用。代谢紊乱易感性的个体间差异的很大一部分是由脂肪组织内的分子过程驱动的。然而，哪些基因与脂肪组织功能障碍的发展有关，以及它们如何与代谢疾病的风险联系在一起，目前还不清楚。脂肪组织是参与代谢疾病的主要组织（胰腺、肌肉、脂肪、肝脏）中唯一的一种，相对而言，研究志愿者更容易获得脂肪组织。因此，脂肪组织提供了一个无与伦比的机会，研究代谢疾病相关组织中的分子过程在纵向人群cohols.To解决这个机会，我们设计了一个项目，将研究代谢健康，脂肪分子功能和疾病遗传风险之间的重要关系，通过无偏的分析，深入，脂肪组织蛋白质和蛋白质翻译后修饰（PTM）水平，在一组1,000个深表型双胞胎中，[长达30年的详细代谢和临床测量]。遗传变异和疾病之间有许多步骤，但在细胞水平上，是基因产生的蛋白质和蛋白质执行的功能驱动了疾病的易感性。因此，这项研究的重点是在相关组织中进行深入的定量蛋白质分析，这将是该领域的一个重大进展，可以揭示独特的新见解。蛋白质也是大多数临床药物的靶点，提供了与临床干预的直接联系，而通过可逆磷酸化和其他PTM调节蛋白质功能是控制代谢的信号传导途径的关键特征，并且是许多治疗剂的靶点。蛋白质及其PTM的全面，系统和无偏见的全球分析由于最近基于质谱（MS）的方法的技术进步，现在才有可能对蛋白质组（定义'蛋白质组'）进行鉴定和定量（即> 6，000种蛋白质），具有异构体解析和翻译后修饰的鉴定，跨越数百个个体样品。据我们所知，这项研究将产生一个最大的实体组织蛋白质组数据集，可从人群队列。我们将使用这些新的数据来表征脂肪蛋白质组的宽度和遗传结构，并确定其在代谢疾病和相关性状发展中的作用。使用经典的双胞胎模型，我们将估计每个量化蛋白质的遗传力，以评估遗传和环境在这个独特的人口数据集中的相对贡献。将脂肪蛋白质组与现有的基因组数据相结合，将确定调节蛋白质水平的遗传变异。我们将使用同时测量的临床表型收集的双胞胎，以确定蛋白质与临床相关性状的变化。我们将使用统计方法将我们的结果与大型生物库相结合，以确定代谢特征遗传风险的分子机制，并确定新的遗传区域驱动代谢疾病的风险。这项工作将为代谢问题的发展提供新的见解，并发现相关的关键基因，为未来的治疗和诊断确定重要的靶点。

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.