title: Stability of metabolic correlations under changing environmental conditions in Escherichia coli--a systems approach.
abstract: BACKGROUND
  Biological systems adapt to changing environments by reorganizing their cellular and physiological program with metabolites representing one important response level. Different stresses lead to both conserved and specific responses on the metabolite level which should be reflected in the underlying metabolic network.
  
  
  RESULTS
  Starting from experimental data obtained by a GC-MS based high-throughput metabolic profiling technology we here develop an approach that: (1) extracts network representations from metabolic condition-dependent data by using pairwise correlations, (2) determines the sets of stable and condition-dependent correlations based on a combination of statistical significance and homogeneity tests, and (3) can identify metabolites related to the stress response, which goes beyond simple observations about the changes of metabolic concentrations. The approach was tested with Escherichia coli as a model organism observed under four different environmental stress conditions (cold stress, heat stress, oxidative stress, lactose diauxie) and control unperturbed conditions. By constructing the stable network component, which displays a scale free topology and small-world characteristics, we demonstrated that: (1) metabolite hubs in this reconstructed correlation networks are significantly enriched for those contained in biochemical networks such as EcoCyc, (2) particular components of the stable network are enriched for functionally related biochemical pathways, and (3) independently of the response scale, based on their importance in the reorganization of the correlation network a set of metabolites can be identified which represent hypothetical candidates for adjusting to a stress-specific response.
  
  
  CONCLUSIONS
  Network-based tools allowed the identification of stress-dependent and general metabolic correlation networks. This correlation-network-based approach does not rely on major changes in concentration to identify metabolites important for stress adaptation, but rather on the changes in network properties with respect to metabolites. This should represent a useful complementary technique in addition to more classical approaches.
authors.affiliation: Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany.
authors.forename: Jedrzej
authors.initials: J
authors.lastname: Szymanski
authors.forename: Szymon
authors.initials: S
authors.lastname: Jozefczuk
authors.forename: Zoran
authors.initials: Z
authors.lastname: Nikoloski
authors.forename: Joachim
authors.initials: J
authors.lastname: Selbig
authors.forename: Victoria
authors.initials: V
authors.lastname: Nikiforova
authors.forename: Gareth
authors.initials: G
authors.lastname: Catchpole
authors.forename: Lothar
authors.initials: L
authors.lastname: Willmitzer
mesh_terms: D001669:Biochemical Phenomena; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D004926:Escherichia coli / Q000378:metabolism*; D029968:Escherichia coli Proteins / Q000378:metabolism*; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods; D015964:Gene Expression Regulation, Bacterial; D005947:Glucose / Q000378:metabolism; D006358:Hot Temperature; D007785:Lactose / Q000378:metabolism; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D018384:Oxidative Stress; D012984:Software; D049490:Systems Biology
chemical_list: D029968:Escherichia coli Proteins; D005947:Glucose; D007785:Lactose