Title
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 similar to 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.
Year
DOI
Venue
2011
10.1371/journal.pcbi.1002166
PLOS COMPUTATIONAL BIOLOGY
Keywords
Field
DocType
computational biology,metabolic engineering,amino acid,surface area,escherichia coli,nucleotides,metabolic pathway,electrochemistry
Biochemical evolution,Enzyme,Biology,Biochemistry,Metabolic pathway,Metabolic engineering,Lipid bilayer,Solubility,Lipophilicity,Metabolite
Journal
Volume
Issue
ISSN
7
10
1553-7358
Citations 
PageRank 
References 
5
0.54
4
Authors
5
Name
Order
Citations
PageRank
Arren Bar-Even1656.17
Elad Noor2757.95
Avi I Flamholz3714.00
Joerg M. Buescher460.89
Ron Milo51069.22