Characterizing the mechanisms controlling the rate of adipogenesis in vitro and in vivo

肥胖构成了一个公共健康问题，并增加了患心血管疾病和胰岛素抵抗等代谢紊乱的风险。由于脂肪组织对新陈代谢的动态平衡控制是必不可少的，脂肪组织的失调扩张会影响整个身体。在肥胖症中，脂肪细胞在一个称为脂肪生成的过程中分化，从而增加了脂肪储备量。对脂肪生成潜在的调控电路的详细了解尚不清楚，可能会找到在病理条件下从治疗上控制脂肪组织生长的方法。在健康的条件下，通过平衡细胞增殖、分化和细胞死亡，组织的大小得以保持。因此，脂肪细胞在人体内以每年10%的速度更新。由于人体中的所有前脂肪细胞都受到相同的分化刺激，所以为什么不是所有增殖的前脂肪细胞分化为不分裂和积累脂肪的脂肪细胞，这一点仍然难以捉摸。玛丽·特鲁尔博士的实验室解决了这个问题，并意识到脂肪生成是由一个高度动态和相互关联的调控网络控制的。他们开发了独特的单细胞成像技术、计算建模和靶向蛋白质组学方法，揭示了细胞培养中的前脂肪细胞通过一种全有或全无的双稳开关机制分化为终末不分裂的脂肪细胞，该机制由关键调节蛋白和脂肪细胞分化的主要转录调节因子PPARGamma之间的七个反馈环驱动。这种调节网络与蛋白质表达的变化相结合，提供了一个可调节的系统，以维持终末脂肪细胞的低分化率。该项目旨在证明这种超高反馈环路架构与活体相关。在我的实验策略中，我将使用AdipoChaser小鼠模型，该模型允许通过对成熟脂肪细胞进行永久标记来确定从头生成脂肪的确切时间框架。在接下来的细胞培养实验中，我将从时间、顺序和连接性方面研究体内相关的反馈调节因子。为了评估在生理刺激下反馈调节蛋白的表达水平和时机如何影响成脂率，我将在同种异体移植模型中应用不同反馈介质的获得和功能丧失策略来量化体内的分化率。这项研究的结果将突出哪些反馈介质对于保持低脂肪细胞分化率是必不可少的，从而至少部分回答了生物如何维持脂肪组织大小的关键问题。此外，这项研究将更好地了解脂肪生成的调控机制，这可能有助于尝试从治疗上控制代谢性疾病的脂肪生成。

Obesity constitutes a public health problem and enhances the risk for cardiovascular disease and metabolic disorders such as insulin resistance. Since adipose tissue is essential for the homeostatic control of metabolism, dysregulated expansion of adipose tissue affects the whole body. In obesity fat depots increase by differentiation of fat cells in a process called adipogenesis. Detailed understanding of the regulatory circuits underlying adipogenesis is missing and could enable to find ways to therapeutically control growth of adipose tissue in pathological conditions. Upon healthy conditions tissue size is preserved by balancing cell proliferation, differentiation and cell death. Hence, adipocytes renew in the human body at a rate of 10% per year. As all preadipocytes in the human body are subjected to the same differentiation stimuli it remains elusive why not the whole population of proliferating preadipocytes differentiates into non-dividing and lipid accumulating adipocytes. The laboratory of Dr. Mary Teruel addressed this question and realized that adipogenesis is controlled by a highly dynamic and interconnected regulatory network. They developed unique single cell imaging techniques, computational modeling, and targeted proteomics approaches, which revealed that preadipocytes in cell culture differentiate into terminal non-dividing adipocytes through an all-or-none, bistable switch mechanism, which is driven by seven feedback loops between critical regulator proteins and PPARgamma, the master transcriptional regulator of adipocyte differentiation. This regulatory network coupled with variation in protein expression provides a tunable system to maintain low rates of terminal adipocyte differentiation. This project aims to prove that this ultra-high feedback loop architecture is relevant in vivo. In my experimental strategy, I will employ the AdipoChaser mouse model which allows determining the exact timeframe of de novo adipogenesis by permanent labeling of mature adipocytes. I will investigate in vivo relevant feedback regulators in terms of timing, sequential order, and connectivity in following cell culture experiments. To evaluate how expression levels and timing of the feedback regulator proteins affect rates of adipogenesis upon physiological stimuli, I will apply gain and loss of function strategies of different feedback mediators in allograft models to quantitate differentiation rates in vivo. Results from this study will highlight which feedback mediators are essential to keep adipocyte differentiation rates low and thus, at least in part, answer the key question how organisms maintain adipose tissue size. In addition this study will provide a better understanding of the regulatory mechanism underlying adipogenesis, which may contribute to attempts to therapeutically control adipogenesis in metabolic disease.