Underground metabolism: network-level perspective and biotechnological potential.

A key challenge in molecular systems biology is understanding how new pathways arise during evolution and how to exploit them for biotechnological applications. New pathways in metabolic networks often evolve by recruiting weak promiscuous activities of pre-existing enzymes. Here we describe recent systems biology advances to map such 'underground' activities and to predict and analyze their contribution to new metabolic functions. Underground activities are prevalent in cellular metabolism and can form novel pathways that either enable evolutionary adaptation to new environments or provide bypass to genetic lesions. We also illustrate the potential of integrating computational models of underground metabolism and experimental approaches to study the evolution of novel metabolic phenotypes and advance the field of biotechnology.

[1]  P. R. Jensen,et al.  A novel cell factory for efficient production of ethanol from dairy waste , 2016, Biotechnology for Biofuels.

[2]  F. Hollfelder,et al.  Ultrahigh-throughput discovery of promiscuous enzymes by picodroplet functional metagenomics , 2015, Nature Communications.

[3]  P. Schieberle,et al.  Characterization of the key aroma compounds in a commercial Amontillado sherry wine by means of the sensomics approach. , 2015, Journal of agricultural and food chemistry.

[4]  Pablo Carbonell,et al.  XTMS: pathway design in an eXTended metabolic space , 2014, Nucleic Acids Res..

[5]  Juhan Kim,et al.  Inhibitory cross-talk upon introduction of a new metabolic pathway into an existing metabolic network , 2012, Proceedings of the National Academy of Sciences.

[6]  Richard N. Armstrong,et al.  Large-Scale Determination of Sequence, Structure, and Function Relationships in Cytosolic Glutathione Transferases across the Biosphere , 2014, PLoS biology.

[7]  N. Tokuriki,et al.  Connectivity between catalytic landscapes of the metallo-β-lactamase superfamily. , 2014, Journal of molecular biology.

[8]  William Stafford Noble,et al.  Machine learning applications in genetics and genomics , 2015, Nature Reviews Genetics.

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

[10]  Hua Huang,et al.  Panoramic view of a superfamily of phosphatases through substrate profiling , 2015, Proceedings of the National Academy of Sciences.

[11]  J. Aguilar,et al.  Experimental evolution of a metabolic pathway for ethylene glycol utilization by Escherichia coli , 1983, Journal of bacteriology.

[12]  Wayne M Patrick,et al.  Multicopy suppression underpins metabolic evolvability. , 2007, Molecular biology and evolution.

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

[14]  L. Wackett.  Evolution of Enzymes for the Metabolism of New Chemical Inputs into the Environment* , 2004, Journal of Biological Chemistry.

[15]  Adam M. Feist,et al.  A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011 , 2011, Molecular systems biology.

[16]  Giuseppe Manco,et al.  The latent promiscuity of newly identified microbial lactonases is linked to a recently diverged phosphotriesterase. , 2006, Biochemistry.

[17]  Richard A. Notebaart,et al.  Network-level architecture and the evolutionary potential of underground metabolism , 2014, Proceedings of the National Academy of Sciences.

[18]  Antje Chang,et al.  BRENDA in 2017: new perspectives and new tools in BRENDA , 2016, Nucleic Acids Res..

[19]  S. Copley,et al.  Evolution of a metabolic pathway for degradation of a toxic xenobiotic: the patchwork approach. , 2000, Trends in biochemical sciences.

[20]  Bernhard Ø. Palsson,et al.  Adaptive Evolution of Escherichia coli K-12 MG1655 during Growth on a Nonnative Carbon Source, l-1,2-Propanediol , 2010, Applied and Environmental Microbiology.

[21]  Johannes C. Hermann,et al.  Structure-based activity prediction for an enzyme of unknown function , 2007, Nature.

[22]  Juhan Kim,et al.  Three serendipitous pathways in E. coli can bypass a block in pyridoxal-5′-phosphate synthesis , 2010, Molecular systems biology.

[23]  Carole L. Linster,et al.  Metabolite damage and its repair or pre-emption. , 2013, Nature chemical biology.

[24]  Pablo Carbonell,et al.  Semisupervised Gaussian Process for Automated Enzyme Search. , 2016, ACS synthetic biology.

[25]  L. Harvey,et al.  Microbial Production of Food Ingredients, Enzymes and Nutraceuticals , 2013 .

[26]  Liran Carmel,et al.  Genome-wide Analysis of Substrate Specificities of the Escherichia coli Haloacid Dehalogenase-like Phosphatase Family* , 2006, Journal of Biological Chemistry.

[27]  N. Tokuriki,et al.  Evolution of Enzyme Superfamilies: Comprehensive Exploration of Sequence-Function Relationships. , 2016, Biochemistry.

[28]  Ryan T Gill,et al.  Genome-wide mapping of mutations at single-nucleotide resolution for protein, metabolic and genome engineering , 2016, Nature Biotechnology.

[29]  J. Irwin,et al.  Docking and chemoinformatic screens for new ligands and targets. , 2009, Current opinion in biotechnology.

[30]  Balázs Papp,et al.  Systems-biology approaches for predicting genomic evolution , 2011, Nature Reviews Genetics.

[31]  Mark N. Wass,et al.  Convergent evolution of enzyme active sites is not a rare phenomenon. , 2007, Journal of molecular biology.

[32]  H. Mori,et al.  Complete set of ORF clones of Escherichia coli ASKA library (a complete set of E. coli K-12 ORF archive): unique resources for biological research. , 2006, DNA research : an international journal for rapid publication of reports on genes and genomes.

[33]  David Baker,et al.  A novel semi-biosynthetic route for artemisinin production using engineered substrate-promiscuous P450(BM3). , 2009, ACS chemical biology.

[34]  Dan S. Tawfik,et al.  Enzyme promiscuity: a mechanistic and evolutionary perspective. , 2010, Annual review of biochemistry.

[35]  Nir London,et al.  Covalent Docking Predicts Substrates for Haloalkanoate Dehalogenase Superfamily Phosphatases , 2014, Biochemistry.

[36]  Adam M. Feist,et al.  Generation of an atlas for commodity chemical production in Escherichia coli and a novel pathway prediction algorithm, GEM-Path. , 2014, Metabolic engineering.

[37]  István Nagy,et al.  A highly precise and portable genome engineering method allows comparison of mutational effects across bacterial species , 2016, Proceedings of the National Academy of Sciences.

[38]  U. Sauer,et al.  Nontargeted in vitro metabolomics for high-throughput identification of novel enzymes in Escherichia coli , 2016, Nature Methods.

[39]  R. Last,et al.  Something Old, Something New: Conserved Enzymes and the Evolution of Novelty in Plant Specialized Metabolism1 , 2015, Plant Physiology.

[40]  Georg Steinkellner,et al.  Identification of promiscuous ene-reductase activity by mining structural databases using active site constellations , 2014, Nature Communications.

[41]  D. Vertommen,et al.  A conserved phosphatase destroys toxic glycolytic side products in mammals and yeast. , 2016, Nature Chemical Biology.

[42]  Gaelen T. Hess,et al.  Directed evolution using dCas9-targeted somatic hypermutation in mammalian cells , 2016, Nature Methods.

[43]  J. Weissenbach,et al.  Revealing the hidden functional diversity of an enzyme family. , 2014, Nature chemical biology.

[44]  Fangfang Xia,et al.  Systems-Wide Prediction of Enzyme Promiscuity Reveals a New Underground Alternative Route for Pyridoxal 5’-Phosphate Production in E. coli , 2016, PLoS Comput. Biol..

[45]  Pablo Carbonell,et al.  Molecular signatures-based prediction of enzyme promiscuity , 2010, Bioinform..

[46]  R. Mortlock.  Microorganisms as Model Systems for Studying Evolution , 1984, Monographs in Evolutionary Biology.

[47]  Dan S. Tawfik,et al.  Diminishing returns and tradeoffs constrain the laboratory optimization of an enzyme , 2012, Nature Communications.

[48]  Janet M Thornton,et al.  Ligand selectivity and competition between enzymes in silico , 2004, Nature Biotechnology.