Feedback Loops of Adipogenesis Targeted (FAT) a multi-omics approach to decipher PPARG regulators

脂肪生成靶向 (FAT) 的反馈循环是一种破译 PPARG 调节因子的多组学方法

• 批准号: 417119862
• 负责人: Professor Dr. Robert Ahrends, Ph.D.
• 金额: --
• 依托单位: Institut für Analytische Chemie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/417119862?language=en
• 关键词: Feedback; Loops; Adipogenesis; Targeted; FAT

The obesity pandemic has become one of the most important global health issues. Worldwide, 1.4 billion people are affected causing an annual health burden of 2 trillion Euros. To uncover the origin of increased or decreased fat cell production and to find novel treatment strategies, a comprehensive understanding of the underlying regulatory mechanism is indispensable. However, so far study of fat cell differentiation has focused on single molecular layers and neglected network connectivity.FAT will apply a multi-layer network analysis combining different ‘omics’ techniques in concert with live-cell perturbation experiments for a system-wide study of network connectivity and will reveal alternative treatment strategies fat cell differentiation related dysfunction. The central question is: How is peroxisome proliferator-activated receptor gamma (PPARG), the lipid controlled master regulator of adipogenesis fine-tuned by feedback circuits and can these circuits be utilized to control the number of fat cells already at the onset of obesity? To address this, FAT will combine i) time-resolved elucidation of feedback circuits by proteomics; ii) screening and identification of lipids regulating PPARG activity using lipidomics; iii) computational network modeling to confirm known and predict new signaling nodes and iv) validate discovered regulators with in human cellular systems. Importantly, FAT will surpass classical approaches by providing a multiscale understanding of network connectivity. Monitoring absolute molecular quantities during differentiation, diet and perturbation will elucidate their connectivity and interdependence, reveal the signaling circuits and generate testable hypotheses on the function of the monitored proteins or lipids to control the number of fat cells.Thus, FAT will deliver novel insights into the molecular processes underlying obesity and open new avenues for treatment and the prevention of obesity.

肥胖症流行已成为最重要的全球健康问题之一。全世界有14亿人受到影响，每年造成2万亿欧元的健康负担。为了揭示脂肪细胞产生增加或减少的起源，并找到新的治疗策略，对潜在的调控机制的全面了解是必不可少的。然而，目前对脂肪细胞分化的研究主要集中在单分子层，而忽视了网络的连通性。FAT将应用多层网络分析，结合不同的“组学”技术和活细胞微扰实验，对网络连通性进行全系统的研究，并将揭示脂肪细胞分化相关功能障碍的替代治疗策略。核心问题是：过氧化物酶体增殖物激活受体γ（PPARG）是如何通过反馈回路进行微调的？这些回路是否可以用于控制肥胖症发作时脂肪细胞的数量？为了解决这个问题，FAT将联合收割机结合i）通过蛋白质组学对反馈回路的时间分辨阐明; ii）使用脂质组学筛选和鉴定调节PPARG活性的脂质; iii）计算网络建模以确认已知的和预测新的信号传导节点，以及iv）验证在人类细胞系统中发现的调节剂。重要的是，FAT将通过提供对网络连接的多尺度理解来超越经典方法。在分化、饮食和扰动过程中监测绝对分子量将阐明它们的连接性和相互依赖性，揭示信号通路，并就监测的蛋白质或脂质控制脂肪细胞数量的功能产生可验证的假设。因此，FAT将为肥胖的分子过程提供新的见解，并为治疗和预防肥胖开辟新的途径。