Testing biochemistry revisited: how in vivo metabolism can be understood from in vitro enzyme kinetics.
Testing biochemistry revisited: how in vivo metabolism can be understood from in vitro enzyme kinetics.
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DOI：
10.1371/journal.pcbi.1002483
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发表时间：
2012
影响因子：
4.3
通讯作者：
Bakker BM
中科院分区：
文献类型：
--
作者：
van Eunen K;Kiewiet JA;Westerhoff HV;Bakker BM
十年前，包括我们两人在内的一个生物化学家团队对酵母糖酵解进行了建模，并表明，根据细胞提取物中测量的组成酶的动力学特性，无法完全理解研究最多的生化途径之一。此外，当同一模型后来应用于不同的实验稳态条件时，它经常表现出不受限制的代谢物积累。在这里，我们通过证明这种从头开始建模的结果通过（i）包括适当的变构调节和（ii）在类似于细胞内环境的条件下测量酶动力学参数而得到显着改善，从而解决了这个问题。以下修改被证明是至关重要的：(i) 实施己糖激酶和丙酮酸激酶的变构调节，(ii) 实施在类似于酵母细胞质的条件下测量的 Vmax 值，以及 (iii) 重新测定生理条件下甘油醛-3-磷酸脱氢酶的动力学参数。在五种不同的酵母生长和饥饿条件下对模型预测和实验进行了比较。当使用原始模型（缺乏重要的变构调节）时，或者在通常最适合高酶活性的条件下测量酶参数时，果糖 1,6-二磷酸和一些其他糖酵解中间体往往会积累到不切实际的高浓度。结合所有调整，在所有五种稳态和动态条件下，模型和实验之间产生了准确的对应关系。这增强了我们在体外生物化学方面对体内代谢的理解。面包酵母广泛应用于现代生物技术，例如用于生产异源蛋白质或生物燃料。对于此类应用，彻底了解昆虫的中心能量代谢至关重要。然而，即使对于这种众所周知的生物体，尝试根据独立测量的催化剂（酶）的特性从头开始构建模型也很少给出可靠的结果。该领域的一个关键问题是酶特性通常是在与细胞内环境不同的非生理条件下研究的。在这项研究中，我们测量了生理条件下的酶特性，并将结果组装成酵母能量代谢的计算模型。我们证明这个简单的技巧极大地提高了计算模型的预测价值。这使我们能够正确预测酵母细胞如何适应氮饥饿，这是一种与工业相关的情况，其中蛋白质组的重塑强烈影响细胞能量代谢。
A decade ago, a team of biochemists including two of us, modeled yeast glycolysis and showed that one of the most studied biochemical pathways could not be quite understood in terms of the kinetic properties of the constituent enzymes as measured in cell extract. Moreover, when the same model was later applied to different experimental steady-state conditions, it often exhibited unrestrained metabolite accumulation. Here we resolve this issue by showing that the results of such ab initio modeling are improved substantially by (i) including appropriate allosteric regulation and (ii) measuring the enzyme kinetic parameters under conditions that resemble the intracellular environment. The following modifications proved crucial: (i) implementation of allosteric regulation of hexokinase and pyruvate kinase, (ii) implementation of Vmax values measured under conditions that resembled the yeast cytosol, and (iii) redetermination of the kinetic parameters of glyceraldehyde-3-phosphate dehydrogenase under physiological conditions. Model predictions and experiments were compared under five different conditions of yeast growth and starvation. When either the original model was used (which lacked important allosteric regulation), or the enzyme parameters were measured under conditions that were, as usual, optimal for high enzyme activity, fructose 1,6-bisphosphate and some other glycolytic intermediates tended to accumulate to unrealistically high concentrations. Combining all adjustments yielded an accurate correspondence between model and experiments for all five steady-state and dynamic conditions. This enhances our understanding of in vivo metabolism in terms of in vitro biochemistry. Baker's yeast is widely applied in modern biotechnology, for instance for production of heterologous protein or biofuel. For such applications a thorough understanding of the central energy metabolism of the bug is crucial. Nevertheless, even for this well-known organism, attempts to build models ab initio, based on independently measured characteristics of the catalysts (the enzymes), seldom gives reliable results. A key problem in this field is that enzyme characteristics are often studied under non-physiological conditions that do not resemble the environment inside the cell. In this study we measured the enzyme characteristics under physiological conditions and assembled the results into a computational model of yeast energy metabolism. We show that this simple trick greatly improves the predictive value of the computational model. This allowed us to predict correctly how yeast cells adapt to nitrogen starvation, an industrially relevant situation, in which remodeling of the proteome strongly affects cellular energy metabolism.
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DOI：
10.1073/pnas.72.10.3829
发表时间：
1975-01-01
影响因子：
11.1
作者：
BOITEUX, A;GOLDBETER, A;HESS, B
通讯作者：
HESS, B
影响因子：
13.8
作者：
Teusink, B;Walsh, MC;Westerhoff, HV
通讯作者：
Westerhoff, HV
影响因子：
1.6
作者：
BOITEUX, A;BUSSE, HG
通讯作者：
BUSSE, HG
影响因子：
3.4
作者：
CORTASSA, S;AON, MA
通讯作者：
AON, MA
影响因子：
1.6
作者：
RICHTER, O;BETZ, A;GIERSCH, C
通讯作者：
GIERSCH, C