Marín-Hernández Alvaro, Gallardo-Pérez Juan Carlos, Rodríguez-Enríquez Sara, Encalada Rusely, Moreno-Sánchez Rafael, Saavedra Emma
Departamento de Bioquímica, Instituto Nacional de Cardiología, México D.F. 14080, México.
Biochim Biophys Acta. 2011 Jun;1807(6):755-67. doi: 10.1016/j.bbabio.2010.11.006. Epub 2010 Nov 24.
Most cancer cells exhibit an accelerated glycolysis rate compared to normal cells. This metabolic change is associated with the over-expression of all the pathway enzymes and transporters (as induced by HIF-1α and other oncogenes), and with the expression of hexokinase (HK) and phosphofructokinase type 1 (PFK-1) isoenzymes with different regulatory properties. Hence, a control distribution of tumor glycolysis, modified from that observed in normal cells, can be expected. To define the control distribution and to understand the underlying control mechanisms, kinetic models of glycolysis of rodent AS-30D hepatoma and human cervix HeLa cells were constructed with experimental data obtained here for each pathway step (enzyme kinetics; steady-state pathway metabolite concentrations and fluxes). The models predicted with high accuracy the fluxes and metabolite concentrations found in living cancer cells under physiological O(2) and glucose concentrations as well as under hypoxic and hypoglycemic conditions prevailing during tumor progression. The results indicated that HK≥HPI>GLUT in AS-30D whereas glycogen degradation≥GLUT>HK in HeLa were the main flux- and ATP concentration-control steps. Modeling also revealed that, in order to diminish the glycolytic flux or the ATP concentration by 50%, it was required to decrease GLUT or HK or HPI by 76% (AS-30D), and GLUT or glycogen degradation by 87-99% (HeLa), or decreasing simultaneously the mentioned steps by 47%. Thus, these proteins are proposed to be the foremost therapeutic targets because their simultaneous inhibition will have greater antagonistic effects on tumor energy metabolism than inhibition of all other glycolytic, non-controlling, enzymes.
与正常细胞相比,大多数癌细胞表现出糖酵解速率加快。这种代谢变化与所有途径酶和转运蛋白的过表达(由缺氧诱导因子-1α和其他癌基因诱导)以及具有不同调节特性的己糖激酶(HK)和1型磷酸果糖激酶(PFK-1)同工酶的表达有关。因此,可以预期肿瘤糖酵解的控制分布与正常细胞中观察到的不同。为了确定控制分布并了解潜在的控制机制,利用此处获得的每个途径步骤的实验数据(酶动力学;稳态途径代谢物浓度和通量)构建了啮齿动物AS-30D肝癌细胞和人宫颈癌HeLa细胞的糖酵解动力学模型。这些模型高精度地预测了在生理氧和葡萄糖浓度下以及肿瘤进展过程中普遍存在的缺氧和低血糖条件下活癌细胞中的通量和代谢物浓度。结果表明,AS-30D细胞中HK≥HPI>GLUT,而HeLa细胞中糖原降解≥GLUT>HK是主要的通量和ATP浓度控制步骤。建模还表明,为了将糖酵解通量或ATP浓度降低50%,需要将GLUT或HK或HPI降低76%(AS-30D),将GLUT或糖原降解降低87-99%(HeLa),或者同时将上述步骤降低47%。因此,这些蛋白质被认为是最重要的治疗靶点,因为它们的同时抑制对肿瘤能量代谢的拮抗作用将大于抑制所有其他糖酵解非控制酶。