Department of Plant Sciences, The Weizmann Institute of Science, Rehovot, Israel.
PLoS Comput Biol. 2011 Oct;7(10):e1002166. doi: 10.1371/journal.pcbi.1002166. Epub 2011 Oct 6.
What governs the concentrations of metabolites within living cells? Beyond specific metabolic and enzymatic considerations, are there global trends that affect their values? We hypothesize that the physico-chemical properties of metabolites considerably affect their in-vivo concentrations. The recently achieved experimental capability to measure the concentrations of many metabolites simultaneously has made the testing of this hypothesis possible. Here, we analyze such recently available data sets of metabolite concentrations within E. coli, S. cerevisiae, B. subtilis and human. Overall, these data sets encompass more than twenty conditions, each containing dozens (28-108) of simultaneously measured metabolites. We test for correlations with various physico-chemical properties and find that the number of charged atoms, non-polar surface area, lipophilicity and solubility consistently correlate with concentration. In most data sets, a change in one of these properties elicits a ~100 fold increase in metabolite concentrations. We find that the non-polar surface area and number of charged atoms account for almost half of the variation in concentrations in the most reliable and comprehensive data set. Analyzing specific groups of metabolites, such as amino-acids or phosphorylated nucleotides, reveals even a higher dependence of concentration on hydrophobicity. We suggest that these findings can be explained by evolutionary constraints imposed on metabolite concentrations and discuss possible selective pressures that can account for them. These include the reduction of solute leakage through the lipid membrane, avoidance of deleterious aggregates and reduction of non-specific hydrophobic binding. By highlighting the global constraints imposed on metabolic pathways, future research could shed light onto aspects of biochemical evolution and the chemical constraints that bound metabolic engineering efforts.
是什么控制了细胞内代谢物的浓度?除了特定的代谢和酶学考虑因素外,是否存在影响其值的全局趋势?我们假设代谢物的物理化学性质会极大地影响其在体内的浓度。最近实现的同时测量许多代谢物浓度的实验能力使得检验这一假设成为可能。在这里,我们分析了大肠杆菌、酿酒酵母、枯草芽孢杆菌和人类中最近可用的代谢物浓度数据集。这些数据集总共涵盖了二十多种条件,每种条件都包含数十种(28-108 种)同时测量的代谢物。我们检验了与各种物理化学性质的相关性,发现带电荷原子的数量、非极性表面积、亲脂性和溶解度与浓度一致相关。在大多数数据集中,这些性质中的一个变化会导致代谢物浓度增加约 100 倍。我们发现,非极性表面积和带电荷原子的数量几乎可以解释最可靠和最全面的数据集中浓度变化的一半。分析特定的代谢物组,如氨基酸或磷酸核苷酸,甚至可以发现浓度对疏水性的依赖性更高。我们认为这些发现可以用代谢物浓度的进化限制来解释,并讨论可能的选择压力来解释这些发现。这些包括通过脂质膜减少溶质渗漏、避免有害聚集和减少非特异性疏水结合。通过强调对代谢途径施加的全局限制,未来的研究可以揭示生物化学进化的方面和限制代谢工程努力的化学限制。