Eames Alec, Chandrasekaran Sriram
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA.
PNAS Nexus. 2024 Jan 12;3(1):pgae013. doi: 10.1093/pnasnexus/pgae013. eCollection 2024 Jan.
Quiescence, a temporary withdrawal from the cell cycle, plays a key role in tissue homeostasis and regeneration. Quiescence is increasingly viewed as a continuum between shallow and deep quiescence, reflecting different potentials to proliferate. The depth of quiescence is altered in a range of diseases and during aging. Here, we leveraged genome-scale metabolic modeling (GEM) to define the metabolic and epigenetic changes that take place with quiescence deepening. We discovered contrasting changes in lipid catabolism and anabolism and diverging trends in histone methylation and acetylation. We then built a multi-cell type machine learning model that accurately predicts quiescence depth in diverse biological contexts. Using both machine learning and genome-scale flux simulations, we performed high-throughput screening of chemical and genetic modulators of quiescence and identified novel small molecule and genetic modulators with relevance to cancer and aging.
静止状态，即从细胞周期中的暂时退出，在组织稳态和再生中起着关键作用。静止状态越来越被视为浅静止和深静止之间的一个连续体，反映了不同的增殖潜能。在一系列疾病和衰老过程中，静止的深度会发生改变。在这里，我们利用基因组规模代谢建模（GEM）来定义随着静止状态加深而发生的代谢和表观遗传变化。我们发现了脂质分解代谢和合成代谢的对比变化以及组蛋白甲基化和乙酰化的不同趋势。然后，我们构建了一个多细胞类型的机器学习模型，该模型能够准确预测不同生物学背景下的静止深度。通过机器学习和基因组规模通量模拟，我们对静止的化学和遗传调节剂进行了高通量筛选，并鉴定出了与癌症和衰老相关的新型小分子和遗传调节剂。
