Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany.
Plant Cell. 2010 Aug;22(8):2872-93. doi: 10.1105/tpc.110.076653. Epub 2010 Aug 10.
Natural genetic diversity provides a powerful resource to investigate how networks respond to multiple simultaneous changes. In this work, we profile maximum catalytic activities of 37 enzymes from central metabolism and generate a matrix to investigate species-wide connectivity between metabolites, enzymes, and biomass. Most enzyme activities change in a highly coordinated manner, especially those in the Calvin-Benson cycle. Metabolites show coordinated changes in defined sectors of metabolism. Little connectivity was observed between maximum enzyme activities and metabolites, even after applying multivariate analysis methods. Measurements of posttranscriptional regulation will be required to relate these two functional levels. Individual enzyme activities correlate only weakly with biomass. However, when they are used to estimate protein abundances, and the latter are summed and expressed as a fraction of total protein, a significant positive correlation to biomass is observed. The correlation is additive to that obtained between starch and biomass. Thus, biomass is predicted by two independent integrative metabolic biomarkers: preferential investment in photosynthetic machinery and optimization of carbon use.
自然遗传多样性为研究网络如何应对多种同时发生的变化提供了有力的资源。在这项工作中,我们对中心代谢途径中的 37 种酶的最大催化活性进行了分析,并生成了一个矩阵来研究代谢物、酶和生物量之间的全物种连接性。大多数酶活性以高度协调的方式变化,特别是卡尔文-本森循环中的那些。代谢物在代谢的特定区域表现出协调的变化。即使应用多元分析方法,也观察到最大酶活性与代谢物之间几乎没有连接。将这两个功能水平联系起来还需要测量转录后调控。单个酶活性与生物量的相关性很弱。然而,当它们被用来估计蛋白质丰度时,将后者相加并表示为总蛋白的一部分,就会观察到与生物量呈显著正相关。这种相关性与淀粉和生物量之间的相关性是相加的。因此,生物量由两个独立的综合代谢生物标志物预测:对光合作用机器的优先投资和碳利用的优化。