A mechanistic modeling framework reveals the key principles underlying tumor metabolism.
A mechanistic modeling framework reveals the key principles underlying tumor metabolism.
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机械建模框架揭示了肿瘤代谢的基础关键原理。
DOI：
10.1371/journal.pcbi.1009841
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发表时间：
2022-03
影响因子：
4.3
通讯作者：
Levine H
中科院分区：
文献类型：
--
作者：
Tripathi S;Park JH;Pudakalakatti S;Bhattacharya PK;Kaipparettu BA;Levine H
虽然有氧糖酵解，或瓦尔堡效应，长期以来一直被认为是肿瘤代谢的标志，但最近的研究揭示了一个更为复杂的画面。肿瘤细胞表现出广泛的代谢异质性，不仅表现在它们的瓦尔堡效应，而且还表现在它们所依赖的营养物质和代谢途径上，而且，肿瘤细胞可以响应于环境线索和治疗干预而在不同的代谢表型之间切换。然而，缺乏一个框架来分析所观察到的代谢异质性和可塑性。使用包括肿瘤细胞中活跃的关键代谢途径的机制模型，我们表明细胞质中过量ATP对磷酸果糖激酶的抑制可以驱动快速增殖肿瘤细胞中有氧糖酵解的偏好。因此，肿瘤细胞利用ATP的不同速率可以驱动关于瓦尔堡效应呈现的异质性。基于这一想法，我们将肿瘤细胞的代谢表型与其迁移表型结合起来，并表明我们的模型预测与以前的实验一致。接下来，我们报告说，增殖细胞对不同回补途径的依赖取决于葡萄糖和谷氨酰胺的相对可用性，并可以进一步驱动代谢异质性。最后，以BRAF抑制剂治疗黑色素瘤细胞为例，我们表明我们的模型可用于预测癌细胞对药物治疗的代谢和基因表达变化。通过与以前的肿瘤代谢建模方法相比，做出更具有普遍性和可解释性的预测，我们的框架确定了控制肿瘤细胞代谢的关键原则，以及报告的异质性和可塑性。这些原则可能是针对癌症代谢脆弱性的关键。肿瘤细胞在其代谢行为中表现出异质性和可塑性，依赖于不同的营养物质和代谢途径，并且在受到环境变化或药物的挑战时切换到依赖不同的途径。虽然以前的多项研究都集中在识别可以区分肿瘤细胞与非致瘤细胞的代谢特征上，但一直缺乏分析肿瘤中代谢异质性的框架。在这里，我们提出了一个机械的数学模型，在肿瘤细胞中活跃的一些关键代谢途径，并分析模型可以表现出的稳态行为。我们发现肿瘤细胞利用ATP的速率可能是肿瘤细胞利用葡萄糖的代谢途径的关键决定因素。我们进一步表明，肿瘤细胞可以利用不同的途径来满足相同的代谢要求，并探讨这种行为对肿瘤细胞对靶向肿瘤代谢药物的反应的影响。在每一步中，我们讨论了我们的模型预测如何与跨肿瘤类型的实验观察相适应。目前的建模框架代表了协调关于肿瘤代谢的广泛实验观察的重要一步，并朝着更有条理的方法来靶向肿瘤的代谢脆弱性。
While aerobic glycolysis, or the Warburg effect, has for a long time been considered a hallmark of tumor metabolism, recent studies have revealed a far more complex picture. Tumor cells exhibit widespread metabolic heterogeneity, not only in their presentation of the Warburg effect but also in the nutrients and the metabolic pathways they are dependent on. Moreover, tumor cells can switch between different metabolic phenotypes in response to environmental cues and therapeutic interventions. A framework to analyze the observed metabolic heterogeneity and plasticity is, however, lacking. Using a mechanistic model that includes the key metabolic pathways active in tumor cells, we show that the inhibition of phosphofructokinase by excess ATP in the cytoplasm can drive a preference for aerobic glycolysis in fast-proliferating tumor cells. The differing rates of ATP utilization by tumor cells can therefore drive heterogeneity with respect to the presentation of the Warburg effect. Building upon this idea, we couple the metabolic phenotype of tumor cells to their migratory phenotype, and show that our model predictions are in agreement with previous experiments. Next, we report that the reliance of proliferating cells on different anaplerotic pathways depends on the relative availability of glucose and glutamine, and can further drive metabolic heterogeneity. Finally, using treatment of melanoma cells with a BRAF inhibitor as an example, we show that our model can be used to predict the metabolic and gene expression changes in cancer cells in response to drug treatment. By making predictions that are far more generalizable and interpretable as compared to previous tumor metabolism modeling approaches, our framework identifies key principles that govern tumor cell metabolism, and the reported heterogeneity and plasticity. These principles could be key to targeting the metabolic vulnerabilities of cancer. Tumor cells exhibit heterogeneity and plasticity in their metabolic behavior, relying on distinct nutrients and metabolic pathways, and switching to reliance on different pathways when challenged by an environmental change or a drug. While multiple previous studies have focused on identifying metabolic signatures that can distinguish tumor cells from non-tumorigenic ones, frameworks to analyze the metabolic heterogeneity in tumors have been lacking. Here, we present a mechanistic mathematical model of some of the key metabolic pathways active in tumor cells and analyze the steady state behaviors the model can exhibit. We find that the rate of ATP use by tumor cells can be a key determinant of the metabolic pathway via which tumor cells utilize glucose. We further show that tumor cells can utilize different pathways for satisfying the same metabolic requirements, and explore the implications of such behavior for the response of tumor cells to drugs targeting tumor metabolism. At each step, we discuss how our model predictions fit within the context of experimental observations made across tumor types. The present modeling framework represents an important step towards reconciling the wide array of experimental observations concerning tumor metabolism, and towards a more methodical approach to targeting tumors’ metabolic vulnerabilities.
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影响因子：
4.2
作者：
Brunengraber, Henri;Roe, Charles R.
通讯作者：
Roe, Charles R.
影响因子：
4.3
作者：
Huang B;Lu M;Jia D;Ben-Jacob E;Levine H;Onuchic JN
通讯作者：
Onuchic JN
影响因子：
64.5
作者：
Faubert B;Li KY;Cai L;Hensley CT;Kim J;Zacharias LG;Yang C;Do QN;Doucette S;Burguete D;Li H;Huet G;Yuan Q;Wigal T;Butt Y;Ni M;Torrealba J;Oliver D;Lenkinski RE;Malloy CR;Wachsmann JW;Young JD;Kernstine K;DeBerardinis RJ
通讯作者：
DeBerardinis RJ
影响因子：
64.5
作者：
Fang, Min;Shen, Zhirong;Wang, Xiaodong
通讯作者：
Wang, Xiaodong
影响因子：
4.1
作者：
ENGEL, PC;DALZIEL, K
通讯作者：
DALZIEL, K