Control of gene expression by dynamic metabolic oscillations

总结 新出现的证据表明，细胞代谢的变化可以引起基因的广泛变化， 表达，但这种联系背后的机制还没有完全理解。先前 工作还没有检查代谢诱导基因的时间动态的影响， 表情系统生物学的最新研究表明，上游输入的振荡可以 通过基因表达机器过滤以调节基因表达， 称为动态滤波。此外，我们最近发现细胞代谢状态 响应于各种形式的代谢应激而迅速波动。这些周期驱动 转录因子的异步振荡活性，包括FOXO 3，一个关键的调节因子， 应激基因在衰老中起作用，TFEB是溶酶体的中心调节因子， 自噬基因因此，我们假设代谢状态的振荡驱动基因 表达程序与静态无应力条件下的表达程序不同。我们 提出动态敏感的基因表达程序可以影响细胞命运的决定， 例如分化、细胞生长、衰老、炎症和药物敏感性。在这 项目，我们将研究代谢振荡如何控制TFEB的表达， FOXO 3靶基因。我们将使用活细胞报告基因、诱导型表达构建体和其他 测量靶基因转录和翻译中关键动力学参数的方法。 为了确定代谢动力学调控的更广泛的基因表达程序，我们将使用 结合转录组水平分析的数学建模。功能测定将 用于测试动态敏感的基因表达程序如何改变细胞命运。我们预计 我们的研究旨在建立一个重要的未探索的机制，解释短期内 细胞代谢状态的调节影响包括癌症，糖尿病， 和衰老。我们的研究结果将解决药理代谢如何 抑制剂如二甲双胍在癌症和衰老中提供益处。生成的模型将 建立一种新的方法来评价候选药物化合物。

Summary Emerging evidence shows that changes in cellular metabolism can induce broad shifts in gene expression, but the mechanisms underlying this connection are not fully understood. Previous work has not examined the impact of temporal dynamics on metabolism-induced gene expression. Recent work in systems biology has shown that oscillations in upstream inputs can be filtered by gene expression machinery to modulate gene expression, through a process termed dynamic filtering. Additionally, we have recently shown that cellular metabolic status fluctuates rapidly in response to various forms of metabolic stress. These cycles drive asynchronous oscillating activity of transcription factors including FOXO3, a key regulator of stress genes that plays a role in aging, and TFEB, a central regulator of lysosome and autophagy genes. We therefore hypothesize that oscillations in metabolic state drive gene expression programs that are distinct from those under static unstressed conditions. We propose that dynamics-sensitive gene expression programs can influence cell fate decisions such as differentiation, cell growth, senescence, inflammation, and drug sensitivity. In this project, we will investigate how metabolic oscillations control the expression of TFEB and FOXO3 target genes. We will use live-cell reporters, inducible expression constructs, and other methods to measure key kinetic parameters in the transcription and translation of target genes. To identify broader gene expression programs modulated by metabolic dynamics, we will use mathematical modeling in combination with transcriptome-level profiling. Functional assays will be used to test how dynamically sensitive gene expression programs alter cell fates. We expect our study to establish an important unexplored mechanism that explains how short-term regulation of cellular metabolic status influences chronic diseases including cancer, diabetes, and aging. Our results will address the outstanding question of how pharmacological metabolic inhibitors such as metformin provide benefits in cancer and aging. The models generated will establish a new approach to evaluate candidate pharmacological compounds.