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

肥胖已成为全球最重要的健康问题之一。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。为了揭示脂肪细胞生成增加或减少的起源并找到新的治疗策略，全面了解潜在的调节机制是必不可少的。然而，迄今为止对脂肪细胞分化的研究主要集中在单分子层，而忽视了网络连接。FAT将应用多层网络分析，结合不同的“组学”技术，结合活细胞扰动实验，进行系统范围的网络连接研究，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：过氧化物酶体增殖体激活受体（PPARG），脂肪生成的脂质控制主调节器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发病时已经存在的脂肪细胞的数量？为了解决这个问题，FAT将结合i)蛋白质组学对反馈电路的时间分辨解析；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，以确认已知和预测新的信号节点；iv)验证在人类细胞系统中发现的调节因子。重要的是，FAT将通过提供对网络连接的多尺度理解而超越经典方法。监测分化、饮食和扰动过程中的绝对分子数量将阐明它们的连通性和相互依赖性，揭示信号通路，并对监测的蛋白质或脂质控制脂肪细胞数量的功能产生可测试的假设。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。

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.