A latent capacity for evolutionary innovation through exaptation in metabolic systems

Some evolutionary innovations may originate non-adaptively as exaptations, or pre-adaptations, which are by-products of other adaptive traits. Examples include feathers, which originated before they were used in flight, and lens crystallins, which are light-refracting proteins that originated as enzymes. The question of how often adaptive traits have non-adaptive origins has profound implications for evolutionary biology, but is difficult to address systematically. Here we consider this issue in metabolism, one of the most ancient biological systems that is central to all life. We analyse a metabolic trait of great adaptive importance: the ability of a metabolic reaction network to synthesize all biomass from a single source of carbon and energy. We use novel computational methods to sample randomly many metabolic networks that can sustain life on any given carbon source but contain an otherwise random set of known biochemical reactions. We show that when we require such networks to be viable on one particular carbon source, they are typically also viable on multiple other carbon sources that were not targets of selection. For example, viability on glucose may entail viability on up to 44 other sole carbon sources. Any one adaptation in these metabolic systems typically entails multiple potential exaptations. Metabolic systems thus contain a latent potential for evolutionary innovations with non-adaptive origins. Our observations suggest that many more metabolic traits may have non-adaptive origins than is appreciated at present. They also challenge our ability to distinguish adaptive from non-adaptive traits.

[1]  Eric L. Miller,et al.  The Ascent of the Abundant: How Mutational Networks Constrain Evolution , 2008, PLoS Comput. Biol..

[2]  Francisco Bolívar,et al.  Genetic changes during a laboratory adaptive evolution process that allowed fast growth in glucose to an Escherichia coli strain lacking the major glucose transport system , 2012, BMC Genomics.

[3]  S. Carroll,et al.  Gene co-option in physiological and morphological evolution. , 2002, Annual review of cell and developmental biology.

[4]  Martin J. Lercher,et al.  Horizontal gene transfer depends on gene content of the host , 2005, ECCB/JBI.

[5]  Robert R. Sokal,et al.  A statistical method for evaluating systematic relationships , 1958 .

[6]  Richard W. Hamming,et al.  Error detecting and error correcting codes , 1950 .

[7]  Kenneth J. Kauffman,et al.  Advances in flux balance analysis. , 2003, Current opinion in biotechnology.

[8]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[9]  B. Kahng,et al.  database of chemical compounds and reactions in biological pathways. Nucleic , 2009 .

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

[11]  P. Schuster,et al.  From sequences to shapes and back: a case study in RNA secondary structures , 1994, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[12]  J. Piatigorsky,et al.  Lens Crystallins of Invertebrates , 1996 .

[13]  B. Griffin,et al.  Network Context and Selection in the Evolution to Enzyme Specificity , 2014 .

[14]  Bret J. Pearson,et al.  Recruitment of a hedgehog regulatory circuit in butterfly eyespot evolution. , 1999, Science.

[15]  Denis Duboule,et al.  Hox genes in digit development and evolution , 1999, Cell and Tissue Research.

[16]  B. Hove-Jensen,et al.  d-Allose Catabolism ofEscherichia coli: Involvement of alsI and Regulation of als Regulon Expression by Allose and Ribose , 1999, Journal of bacteriology.

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

[18]  O. Berg,et al.  Selection-Driven Gene Loss in Bacteria , 2012, PLoS genetics.

[19]  Mamoru Yamada,et al.  Occurrence of a Bound Ubiquinone and Its Function in Escherichia coli Membrane-bound Quinoprotein Glucose Dehydrogenase* , 2004, Journal of Biological Chemistry.

[20]  A. Wagner,et al.  Multifunctionality and robustness trade-offs in model genetic circuits. , 2008, Biophysical journal.

[21]  Susumu Goto,et al.  LIGAND: database of chemical compounds and reactions in biological pathways , 2002, Nucleic Acids Res..

[22]  J. H. Winde,et al.  l-Arabinose Efficient Utilization of d-Xylose and S 12 for Pseudomonas putida Engineering , 2008 .

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

[24]  F. Neidhardt,et al.  Escherichia Coli and Salmonella: Typhimurium Cellular and Molecular Biology , 1987 .

[25]  Adam M. Feist,et al.  A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information , 2007, Molecular systems biology.

[26]  J. Piatigorsky,et al.  Lens crystallins of invertebrates--diversity and recruitment from detoxification enzymes and novel proteins. , 1996, European journal of biochemistry.

[27]  Kurt Binder,et al.  Monte Carlo Simulation in Statistical Physics , 1992, Graduate Texts in Physics.

[28]  E. Raineri,et al.  Evolvability and hierarchy in rewired bacterial gene networks , 2008, Nature.

[29]  Andreas Wagner,et al.  Genotype networks in metabolic reaction spaces , 2010, BMC Systems Biology.

[30]  W. Fontana,et al.  Plasticity, evolvability, and modularity in RNA. , 2000, The Journal of experimental zoology.

[31]  N. Moran,et al.  Calibrating bacterial evolution. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

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

[33]  C. Darwin.  On the Origin of Species by Means of Natural Selection: Or, The Preservation of Favoured Races in the Struggle for Life , 2019 .

[34]  Andreas Wagner,et al.  A comparison of genotype-phenotype maps for RNA and proteins. , 2012, Biophysical journal.

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

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

[37]  Susumu Goto,et al.  KEGG for representation and analysis of molecular networks involving diseases and drugs , 2009, Nucleic Acids Res..

[38]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[39]  Dan S. Tawfik,et al.  Latent evolutionary potentials under the neutral mutational drift of an enzyme. , 2007, HFSP journal.

[40]  Telmo Pievani,et al.  Exaptation in human evolution: how to test adaptive vs exaptive evolutionary hypotheses. , 2011, Journal of anthropological sciences = Rivista di antropologia : JASS.

[41]  Susumu Goto,et al.  LIGAND: chemical database of enzyme reactions , 2000, Nucleic Acids Res..

[42]  S. Gould,et al.  Exaptation—a Missing Term in the Science of Form , 1982, Paleobiology.

[43]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[44]  A. Wagner,et al.  Superessential reactions in metabolic networks , 2012, Proceedings of the National Academy of Sciences.

[45]  Andreas Wagner,et al.  Genotype networks, innovation, and robustness in sulfur metabolism , 2010, BMC Systems Biology.

[46]  Rick L. Stevens,et al.  High-throughput generation, optimization and analysis of genome-scale metabolic models , 2010, Nature Biotechnology.

[47]  D. Lipman,et al.  Modelling neutral and selective evolution of protein folding , 1991, Proceedings of the Royal Society of London. Series B: Biological Sciences.