Hydrophobicity and charge shape cellular metabolite concentrations

Abstract

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.

Figure 1. Schematic representation of physico-chemical parameters of metabolites (Materials and Methods), exemplified using 2-ketoglutarate.

(a) Purple - polar surface area (PSA, oxygen and nitrogen atoms that are able to form hydrogen bonds, including hydrogen atoms attached to them). Blue - non-polar surface area (NPSA) which contributes to the hydrophobic effect. Yellow trapezes represent hydrogen bonds that the molecule can form with the solvent or with other solute molecules (HBI - hydrogen bond inventory). Charges are marked by red ellipses (NCA – number of charged atoms). Curved, dashed grey arrows correspond to rotatable bonds (NRB – number of rotatable bonds). (b) LogP (left) is the logarithm of the equilibrium ratio of concentrations of a metabolite in the two phases of a mixture of octanol and water. LogS (right) is the logarithm of the water solubility. See Materials and Methods for details on the calculation of these parameters.

Figure 2. Correlation (R) between the logarithm of metabolites concentrations in each data set and the physico-chemical parameters of metabolites.

Only metabolites with MW<300 were included in this analysis (see Text S1 and Figure S1). We computed the p-value of each R² and determined its significance, as explained in the Methods. A correlation that was found to be significant (false discovery rate of 0.01, see Methods) is denoted by *. Parameters abbreviations are as in Figure 1.

Figure 3. Physico-chemical parameters significantly correlate with the logarithm of the metabolite concentrations in glucose grown E. coli, as measured by Bennett et al. .

(a) Metabolites are ordered (top to bottom) by increasing concentration. Physico-chemical parameters are ordered based on their correlation with concentrations, from the most negative correlation on the left to the most positive correlation on the right. Compound properties were normalized by subtracting the mean and dividing by the standard deviation, enabling consistent color coding of their values. R² values are given at the top of the columns. p-values were calculated as described in the Methods, where ** correspond to a p-value<10^-4 and * to a p-value<10^-2. Parameter abbreviations are as in Figure 1. (b) A linear regression using NPSA and NCA explains about half of the variability in metabolites concentration, as shown by a Log-Log correlation between the expected and measured concentrations.