Control of gene expression by dynamic metabolic oscillations

摘要 新出现的证据表明，细胞新陈代谢的变化可以引起基因的广泛转移。 表达，但这种联系背后的机制还没有完全了解。上一首 目前还没有研究时间动力学对新陈代谢诱导基因的影响。 表情。最近在系统生物学方面的研究表明，上游输入的振荡可以 被基因表达机器过滤，通过一个过程调节基因表达 称为动态过滤。此外，我们最近发现，细胞的代谢状态 对各种形式的新陈代谢应激反应迅速波动。这些循环驱动 转录因子的异步振荡活性，包括转录因子的关键调控因子FOXO_3 在衰老中起作用的应激基因，以及溶酶体和蛋白质的中央调节因子TFEB。 自噬基因。因此，我们假设代谢状态的波动驱动基因 不同于静态非压力条件下的表达程序。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 如分化、细胞生长、衰老、炎症和药物敏感性。在这 我们将研究代谢波动如何控制TFEB和TFEB的表达 FOXO_3靶基因。我们将使用活细胞记者、可诱导表达结构和其他 方法测定靶基因转录和翻译过程中的关键动力学参数。 为了识别受代谢动力学调节的更广泛的基因表达程序，我们将使用 数学建模与转录组水平分析相结合。功能分析将 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 我们的研究建立了一个重要的未探索的机制，解释了如何在短期内 细胞代谢状态的调节影响慢性疾病，包括癌症、糖尿病、 和衰老。我们的结果将解决一个悬而未决的问题，即药物如何代谢 二甲双胍等抑制剂对癌症和衰老有好处。生成的模型将 建立评价候选药理化合物的新方法。

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.