A network perspective on the topological importance of enzymes and their phylogenetic conservation

BackgroundA metabolic network is the sum of all chemical transformations or reactions in the cell, with the metabolites being interconnected by enzyme-catalyzed reactions. Many enzymes exist in numerous species while others occur only in a few. We ask if there are relationships between the phylogenetic profile of an enzyme, or the number of different bacterial species that contain it, and its topological importance in the metabolic network. Our null hypothesis is that phylogenetic profile is independent of topological importance. To test our null hypothesis we constructed an enzyme network from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. We calculated three network indices of topological importance: the degree or the number of connections of a network node; closeness centrality, which measures how close a node is to others; and betweenness centrality measuring how frequently a node appears on all shortest paths between two other nodes.ResultsEnzyme phylogenetic profile correlates best with betweenness centrality and also quite closely with degree, but poorly with closeness centrality. Both betweenness and closeness centralities are non-local measures of topological importance and it is intriguing that they have contrasting power of predicting phylogenetic profile in bacterial species. We speculate that redundancy in an enzyme network may be reflected by betweenness centrality but not by closeness centrality. We also discuss factors influencing the correlation between phylogenetic profile and topological importance.ConclusionOur analysis falsifies the hypothesis that phylogenetic profile of enzymes is independent of enzyme network importance. Our results show that phylogenetic profile correlates better with degree and betweenness centrality, but less so with closeness centrality. Enzymes that occur in many bacterial species tend to be those that have high network importance. We speculate that this phenomenon originates in mechanisms driving network evolution. Closeness centrality reflects phylogenetic profile poorly. This is because metabolic networks often consist of distinct functional modules and some are not in the centre of the network. Enzymes in these peripheral parts of a network might be important for cell survival and should therefore occur in many bacterial species. They are, however, distant from other enzymes in the same network.

[1]  D. Eisenberg,et al.  Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[2]  N H Horowitz,et al.  On the Evolution of Biochemical Syntheses. , 1945, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Arne Elofsson,et al.  Preferential attachment in the evolution of metabolic networks , 2005, BMC Genomics.

[4]  R. Albert Scale-free networks in cell biology , 2005, Journal of Cell Science.

[5]  H. Ochman,et al.  Molecular archaeology of the Escherichia coli genome. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Alessandro Giuliani,et al.  Metabolic pathways variability and sequence/networks comparisons , 2006, BMC Bioinformatics.

[7]  A. Lehninger Principles of Biochemistry , 1984 .

[8]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[9]  Yoshihiro Yamanishi,et al.  Supervised enzyme network inference from the integration of genomic data and chemical information , 2005, ISMB.

[10]  J. Nielsen,et al.  Uncovering transcriptional regulation of metabolism by using metabolic network topology. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Dongxiao Zhu,et al.  BMC Bioinformatics BioMed Central , 2005 .

[12]  C. Ouzounis,et al.  Expansion of the BioCyc collection of pathway/genome databases to 160 genomes , 2005, Nucleic acids research.

[13]  Ferenc Jordán,et al.  Topological keystone species : measures of positional importance in food webs , 2006 .

[14]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[15]  Sara Light,et al.  Network analysis of metabolic enzyme evolution in Escherichia coli , 2004, BMC Bioinformatics.

[16]  S. Schuster,et al.  Metabolic network structure determines key aspects of functionality and regulation , 2002, Nature.

[17]  Ney Lemke,et al.  Essentiality and damage in metabolic networks , 2004, Bioinform..

[18]  R. Jensen Enzyme recruitment in evolution of new function. , 1976, Annual review of microbiology.

[19]  Susumu Goto,et al.  The KEGG databases at GenomeNet , 2002, Nucleic Acids Res..

[20]  B. Palsson,et al.  Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth , 2002, Nature.

[21]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

[22]  Jason A. Papin,et al.  Extreme pathway lengths and reaction participation in genome-scale metabolic networks. , 2002, Genome research.

[23]  B. Palsson,et al.  Metabolic modelling of microbes: the flux-balance approach. , 2002, Environmental microbiology.

[24]  An-Ping Zeng,et al.  The Connectivity Structure, Giant Strong Component and Centrality of Metabolic Networks , 2003, Bioinform..

[25]  Gary D Bader,et al.  Global Mapping of the Yeast Genetic Interaction Network , 2004, Science.

[26]  B. Palsson,et al.  Genome-scale models of microbial cells: evaluating the consequences of constraints , 2004, Nature Reviews Microbiology.

[27]  D. Fell,et al.  The small world inside large metabolic networks , 2000, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[28]  Petter Holme,et al.  Subnetwork hierarchies of biochemical pathways , 2002, Bioinform..

[29]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[30]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[31]  John Scott What is social network analysis , 2010 .