Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation.

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.

[1]  Olga Kagan,et al.  Proteome-wide dataset supporting the study of ancient metazoan macromolecular complexes , 2015, Data in brief.

[2]  H. Wan,et al.  Evidence for Dsg3 in regulating Src signaling by competing with it for binding to caveolin-1 , 2015, Data in brief.

[3]  Fan-Gang Tseng,et al.  Essentials of Single-Cell Analysis , 2016 .

[4]  K. Jijakli,et al.  Genome-wide expression analysis offers new insights into the origin and evolution of Physcomitrella patens stress response , 2015, Scientific Reports.

[5]  Erik F. Y. Hom,et al.  Whole-Genome Resequencing Reveals Extensive Natural Variation in the Model Green Alga Chlamydomonas reinhardtii[OPEN] , 2015, Plant Cell.

[6]  P. Bahri,et al.  Biomass and Biofuels from Microalgae , 2015 .

[7]  D. Nelson,et al.  Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling , 2014, Front. Bioeng. Biotechnol..

[8]  Amphun Chaiboonchoe,et al.  Computational Approaches for Microalgal Biofuel Optimization: A Review , 2014, BioMed research international.

[9]  M. Ghirardi,et al.  Identification of Global Ferredoxin Interaction Networks in Chlamydomonas reinhardtii* , 2013, The Journal of Biological Chemistry.

[10]  M. Kanehisa.  Chemical and genomic evolution of enzyme‐catalyzed reaction networks , 2013, FEBS letters.

[11]  P. Mulo,et al.  Plant type ferredoxins and ferredoxin-dependent metabolism. , 2013, Plant, cell & environment.

[12]  Karla D. Passalacqua,et al.  Transcriptomic and phylogenetic analysis of a bacterial cell cycle reveals strong associations between gene co-expression and evolution , 2013, BMC Genomics.

[13]  Sean R. Collins,et al.  Hierarchical modularity and the evolution of genetic interactomes across species. , 2012, Molecular cell.

[14]  Adam Frost,et al.  Functional Repurposing Revealed by Comparing S. pombe and S. cerevisiae Genetic Interactions , 2012, Cell.

[15]  B. Palsson,et al.  Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods , 2012, Nature Reviews Microbiology.

[16]  W. Marsden.  I and J , 2012 .

[17]  A. Conesa,et al.  Differential expression in RNA-seq: a matter of depth. , 2011, Genome research.

[18]  Jason A. Papin,et al.  Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism , 2011, Molecular systems biology.

[19]  Haiyuan Yu,et al.  Genome-wide functional annotation and structural verification of metabolic ORFeome of Chlamydomonas reinhardtii , 2011, BMC Genomics.

[20]  Amaury de Montaigu,et al.  A Soluble Guanylate Cyclase Mediates Negative Signaling by Ammonium on Expression of Nitrate Reductase in Chlamydomonas[W] , 2010, Plant Cell.

[21]  Jeffrey D Orth,et al.  What is flux balance analysis? , 2010, Nature Biotechnology.

[22]  Y. Li,et al.  Prevalent positive epistasis in E. coli and S. cerevisiae metabolic networks , 2009, Nature Genetics.

[23]  Nevan J Krogan,et al.  Quantitative genetic interaction mapping using the E-MAP approach. , 2010, Methods in enzymology.

[24]  A. Barabasi,et al.  Network medicine : a network-based approach to human disease , 2010 .

[25]  Danail Bonchev,et al.  Evolution of metabolic network organization , 2010, BMC Systems Biology.

[26]  Andreas Wagner,et al.  Evolutionary Plasticity and Innovations in Complex Metabolic Reaction Networks , 2009, PLoS Comput. Biol..

[27]  Ines Thiele,et al.  Three-Dimensional Structural View of the Central Metabolic Network of Thermotoga maritima , 2009, Science.

[28]  S. Baldauf,et al.  Molecular phylogeny of choanoflagellates, the sister group to Metazoa , 2008, Proceedings of the National Academy of Sciences.

[29]  Sean R. Collins,et al.  Conservation and Rewiring of Functional Modules Revealed by an Epistasis Map in Fission Yeast , 2008, Science.

[30]  B. Williams,et al.  Mapping and quantifying mammalian transcriptomes by RNA-Seq , 2008, Nature Methods.

[31]  M Madan Babu,et al.  Interplay between network structures, regulatory modes and sensing mechanisms of transcription factors in the transcriptional regulatory network of E. coli. , 2007, Journal of molecular biology.

[32]  U. Alon,et al.  Environmental variability and modularity of bacterial metabolic networks , 2007, BMC Evolutionary Biology.

[33]  Jing Zhao,et al.  Modular co-evolution of metabolic networks , 2007, BMC Bioinformatics.

[34]  Lindsey Leach,et al.  Impacts of yeast metabolic network structure on enzyme evolution , 2007, Genome Biology.

[35]  D. J. Kiviet,et al.  Empirical fitness landscapes reveal accessible evolutionary paths , 2007, Nature.

[36]  Ronan M. T. Fleming,et al.  Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0 , 2007, Nature Protocols.

[37]  J. Botto,et al.  The plant cell , 2007, Plant Molecular Biology Reporter.

[38]  Tatsuya Akutsu,et al.  Correlation between structure and temperature in prokaryotic metabolic networks , 2007, BMC Bioinformatics.

[39]  J. J. Díaz-Mejía,et al.  A network perspective on the evolution of metabolism by gene duplication , 2007, Genome Biology.

[40]  Dan S. Tawfik,et al.  Robustness–epistasis link shapes the fitness landscape of a randomly drifting protein , 2006, Nature.

[41]  M. Gerstein,et al.  Design principles of molecular networks revealed by global comparisons and composite motifs , 2006, Genome Biology.

[42]  Neema Jamshidi,et al.  Systems biology of SNPs , 2006, Molecular systems biology.

[43]  S. Teichmann,et al.  Evolutionary dynamics of prokaryotic transcriptional regulatory networks. , 2006, Journal of molecular biology.

[44]  S. Oliver,et al.  Chance and necessity in the evolution of minimal metabolic networks , 2006, Nature.

[45]  D. Vitkup,et al.  Influence of metabolic network structure and function on enzyme evolution , 2006, Genome Biology.

[46]  Martin Kuiper,et al.  BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks , 2005, Bioinform..

[47]  刘金明,et al.  IL-13受体α2降低血吸虫病肉芽肿的炎症反应并延长宿主存活时间[英]/Mentink-Kane MM,Cheever AW，Thompson RW，et al//Proc Natl Acad Sci U S A , 2005 .

[48]  B. Palsson,et al.  Properties of metabolic networks: structure versus function. , 2005, Biophysical journal.

[49]  Bernhard O Palsson,et al.  Hierarchical thinking in network biology: the unbiased modularization of biochemical networks. , 2004, Trends in biochemical sciences.

[50]  C. Pál,et al.  Metabolic network analysis of the causes and evolution of enzyme dispensability in yeast , 2004, Nature.

[51]  M. Gerstein,et al.  Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. , 2004, Genome research.

[52]  A. Barabasi,et al.  Global organization of metabolic fluxes in the bacterium Escherichia coli , 2004, Nature.

[53]  C. Schilling,et al.  Flux coupling analysis of genome-scale metabolic network reconstructions. , 2004, Genome research.

[54]  P. Bork,et al.  Genome evolution reveals biochemical networks and functional modules , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[55]  Edward M Marcotte,et al.  Discovery of uncharacterized cellular systems by genome-wide analysis of functional linkages , 2003, Nature Biotechnology.

[56]  Gary D. Bader,et al.  An automated method for finding molecular complexes in large protein interaction networks , 2003, BMC Bioinformatics.

[57]  Michael J E Sternberg,et al.  Evolution of enzymes in metabolism: a network perspective. , 2002, Journal of molecular biology.

[58]  J. Castresana,et al.  Comparative genomics and bioenergetics. , 2001, Biochimica et biophysica acta.

[59]  S. Carroll.  Chance and necessity: the evolution of morphological complexity and diversity , 2001, Nature.

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

[61]  D. Eisenberg,et al.  Protein function in the post-genomic era , 2000, Nature.

[62]  M. Vidal,et al.  Protein interaction mapping in C. elegans using proteins involved in vulval development. , 2000, Science.

[63]  M. Saraste,et al.  FEBS Lett , 2000 .

[64]  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.

[65]  S. Kauffman,et al.  Coevolution to the edge of chaos: coupled fitness landscapes, poised states, and coevolutionary avalanches. , 1991, Journal of theoretical biology.

[66]  Nature Genetics , 1991, Nature.

[67]  E. D. Weinberger,et al.  The NK model of rugged fitness landscapes and its application to maturation of the immune response. , 1989, Journal of theoretical biology.

[68]  R. Cammack,et al.  Midpoint redox potentials of plant and algal ferredoxins. , 1977, The Biochemical journal.

[69]  S. Wright.  Evolution in mendelian populations , 1931 .