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      Reconstruction of Ancestral Metabolic Enzymes Reveals Molecular Mechanisms Underlying Evolutionary Innovation through Gene Duplication

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          Abstract

          Resurrection of ancient fungal maltase enzymes uncovers the molecular details of how repeated gene duplications allow the evolution of protein variants with different functions.

          Abstract

          Gene duplications are believed to facilitate evolutionary innovation. However, the mechanisms shaping the fate of duplicated genes remain heavily debated because the molecular processes and evolutionary forces involved are difficult to reconstruct. Here, we study a large family of fungal glucosidase genes that underwent several duplication events. We reconstruct all key ancestral enzymes and show that the very first preduplication enzyme was primarily active on maltose-like substrates, with trace activity for isomaltose-like sugars. Structural analysis and activity measurements on resurrected and present-day enzymes suggest that both activities cannot be fully optimized in a single enzyme. However, gene duplications repeatedly spawned daughter genes in which mutations optimized either isomaltase or maltase activity. Interestingly, similar shifts in enzyme activity were reached multiple times via different evolutionary routes. Together, our results provide a detailed picture of the molecular mechanisms that drove divergence of these duplicated enzymes and show that whereas the classic models of dosage, sub-, and neofunctionalization are helpful to conceptualize the implications of gene duplication, the three mechanisms co-occur and intertwine.

          Author Summary

          Darwin's theory of evolution is one of gradual change, yet evolution sometimes takes remarkable leaps. Such evolutionary innovations are often linked to gene duplication through one of three basic scenarios: an extra copy can increase protein levels, different ancestral subfunctions can be split over the copies and evolve distinct regulation, or one of the duplicates can develop a novel function. Although there are numerous examples for all these trajectories, the underlying molecular mechanisms remain obscure, mostly because the preduplication genes and proteins no longer exist. Here, we study a family of fungal metabolic enzymes that hydrolyze disaccharides, and that all originated from the same ancestral gene through repeated duplications. By resurrecting the ancient genes and proteins using high-confidence predictions from many fungal genome sequences available, we show that the very first preduplication enzyme was promiscuous, preferring maltose-like substrates but also showing trace activity towards isomaltose-like sugars. After duplication, specific mutations near the active site of one copy optimized the minor activity at the expense of the major ancestral activity, while the other copy further specialized in maltose and lost the minor activity. Together, our results reveal how the three basic trajectories for gene duplicates cannot be separated easily, but instead intertwine into a complex evolutionary path that leads to innovation.

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          MRBAYES: Bayesian inference of phylogenetic trees.

          The program MRBAYES performs Bayesian inference of phylogeny using a variant of Markov chain Monte Carlo. MRBAYES, including the source code, documentation, sample data files, and an executable, is available at http://brahms.biology.rochester.edu/software.html.
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            ProtTest: selection of best-fit models of protein evolution.

            Using an appropriate model of amino acid replacement is very important for the study of protein evolution and phylogenetic inference. We have built a tool for the selection of the best-fit model of evolution, among a set of candidate models, for a given protein sequence alignment. ProtTest is available under the GNU license from http://darwin.uvigo.es
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              AWTY (are we there yet?): a system for graphical exploration of MCMC convergence in Bayesian phylogenetics.

              A key element to a successful Markov chain Monte Carlo (MCMC) inference is the programming and run performance of the Markov chain. However, the explicit use of quality assessments of the MCMC simulations-convergence diagnostics-in phylogenetics is still uncommon. Here, we present a simple tool that uses the output from MCMC simulations and visualizes a number of properties of primary interest in a Bayesian phylogenetic analysis, such as convergence rates of posterior split probabilities and branch lengths. Graphical exploration of the output from phylogenetic MCMC simulations gives intuitive and often crucial information on the success and reliability of the analysis. The tool presented here complements convergence diagnostics already available in other software packages primarily designed for other applications of MCMC. Importantly, the common practice of using trace-plots of a single parameter or summary statistic, such as the likelihood score of sampled trees, can be misleading for assessing the success of a phylogenetic MCMC simulation. The program is available as source under the GNU General Public License and as a web application at http://ceb.scs.fsu.edu/awty.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, USA )
                1544-9173
                1545-7885
                December 2012
                December 2012
                11 December 2012
                : 10
                : 12
                : e1001446
                Affiliations
                [1 ]VIB Laboratory for Systems Biology, Leuven, Belgium
                [2 ]CMPG Laboratory for Genetics and Genomics, KU Leuven, Leuven, Belgium
                [3 ]Fathom Information Design, Boston, Massachusetts, United States of America
                [4 ]Faculty of Arts and Sciences Center for Systems Biology, Harvard University, Cambridge, Massachusetts, United States of America
                [5 ]Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America
                [6 ]VIB Department of Plant Systems Biology, Gent, Belgium
                [7 ]Department of Plant Biotechnology and Bioinformatics, Ghent University, Gent, Belgium
                [8 ]Laboratory for Molecular en Structural Biology, KU Leuven, Leuven, Belgium
                University of Chicago, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                The author(s) have made the following declarations about their contributions: Conceived and designed the experiments: C. Brown, S. Maere, K. Verstrepen. Performed the experiments: K. Voordeckers, C. Brown, E. van der Zande. Analyzed the data: K. Voordeckers, C. Brown, K. Vanneste, A. Voet, S. Maere, K. Verstrepen. Wrote the paper: K. Voordeckers, C. Brown, K. Vanneste, A. Voet, S. Maere, K. Verstrepen. Carried out molecular evolution analyses: C. Brown, K. Vanneste, S. Maere. Carried out structural analyses: A. Voet.

                Article
                PBIOLOGY-D-12-00784
                10.1371/journal.pbio.1001446
                3519909
                23239941
                c7f4eced-c918-41ac-9d6d-d5e82275afc9
                Copyright @ 2012

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 February 2012
                : 30 October 2012
                Page count
                Pages: 17
                Funding
                S. Maere and K. Vanneste are fellows of the Fund for Scientific Research-Flanders (FWO). Research in the lab of KJV is supported by the Human Frontier Science Program, ERC Starting Grant 241426, VIB, EMBO YIP program, KU Leuven, FWO, IWT and the AB InBev Baillet-Latour foundation. Research in the lab of SM is supported by VIB, Ghent University, FWO and IWT. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Biochemistry
                Enzymes
                Evolutionary Biology
                Evolutionary Processes

                Life sciences
                Life sciences

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