Roy Mahua, Finley Stacey D
Biomedical Engineering, University of Southern CaliforniaLos Angeles, CA, USA.
Chemical Engineering, University of Southern CaliforniaLos Angeles, CA, USA.
Front Physiol. 2017 Apr 12;8:217. doi: 10.3389/fphys.2017.00217. eCollection 2017.
Reprogramming of energy metabolism is a hallmark of cancer that enables the cancer cells to meet the increased energetic requirements due to uncontrolled proliferation. One prominent example is pancreatic ductal adenocarcinoma, an aggressive form of cancer with an overall 5-year survival rate of 5%. The reprogramming mechanism in pancreatic cancer involves deregulated uptake of glucose and glutamine and other opportunistic modes of satisfying energetic demands in a hypoxic and nutrient-poor environment. In the current study, we apply systems biology approaches to enable a better understanding of the dynamics of the distinct metabolic alterations in KRAS-mediated pancreatic cancer, with the goal of impeding early cell proliferation by identifying the optimal metabolic enzymes to target. We have constructed a kinetic model of metabolism represented as a set of ordinary differential equations that describe time evolution of the metabolite concentrations in glycolysis, glutaminolysis, tricarboxylic acid cycle and the pentose phosphate pathway. The model is comprised of 46 metabolites and 53 reactions. The mathematical model is fit to published enzyme knockdown experimental data. We then applied the model to perform enzyme modulations and evaluate the effects on cell proliferation. Our work identifies potential combinations of enzyme knockdown, metabolite inhibition, and extracellular conditions that impede cell proliferation. Excitingly, the model predicts novel targets that can be tested experimentally. Therefore, the model is a tool to predict the effects of inhibiting specific metabolic reactions within pancreatic cancer cells, which is difficult to measure experimentally, as well as test further hypotheses toward targeted therapies.
能量代谢重编程是癌症的一个标志，它使癌细胞能够满足由于不受控制的增殖而增加的能量需求。一个突出的例子是胰腺导管腺癌，这是一种侵袭性癌症，总体5年生存率为5%。胰腺癌中的重编程机制涉及葡萄糖和谷氨酰胺摄取失调以及在缺氧和营养匮乏环境中满足能量需求的其他机会主义模式。在当前的研究中，我们应用系统生物学方法来更好地理解KRAS介导的胰腺癌中不同代谢改变的动态，目标是通过识别最佳的代谢酶靶点来阻碍早期细胞增殖。我们构建了一个代谢动力学模型，该模型表示为一组常微分方程，描述了糖酵解、谷氨酰胺分解、三羧酸循环和磷酸戊糖途径中代谢物浓度的时间演变。该模型由46种代谢物和53个反应组成。该数学模型与已发表的酶敲低实验数据拟合。然后，我们应用该模型进行酶调节并评估对细胞增殖的影响。我们的工作确定了阻碍细胞增殖的酶敲低、代谢物抑制和细胞外条件的潜在组合。令人兴奋的是，该模型预测了可以通过实验测试的新靶点。因此，该模型是一种工具，可用于预测抑制胰腺癌细胞内特定代谢反应的效果，这在实验中很难测量，同时也可用于测试针对靶向治疗的进一步假设。
