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      Evolution of substrate specificity in a retained enzyme driven by gene loss

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          Abstract

          The connection between gene loss and the functional adaptation of retained proteins is still poorly understood. We apply phylogenomics and metabolic modeling to detect bacterial species that are evolving by gene loss, with the finding that Actinomycetaceae genomes from human cavities are undergoing sizable reductions, including loss of L-histidine and L-tryptophan biosynthesis. We observe that the dual-substrate phosphoribosyl isomerase A or priA gene, at which these pathways converge, appears to coevolve with the occurrence of trp and his genes. Characterization of a dozen PriA homologs shows that these enzymes adapt from bifunctionality in the largest genomes, to a monofunctional, yet not necessarily specialized, inefficient form in genomes undergoing reduction. These functional changes are accomplished via mutations, which result from relaxation of purifying selection, in residues structurally mapped after sequence and X-ray structural analyses. Our results show how gene loss can drive the evolution of substrate specificity from retained enzymes.

          DOI: http://dx.doi.org/10.7554/eLife.22679.001

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          Most cited references28

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          Extreme genome reduction in symbiotic bacteria.

          Since 2006, numerous cases of bacterial symbionts with extraordinarily small genomes have been reported. These organisms represent independent lineages from diverse bacterial groups. They have diminutive gene sets that rival some mitochondria and chloroplasts in terms of gene numbers and lack genes that are considered to be essential in other bacteria. These symbionts have numerous features in common, such as extraordinarily fast protein evolution and a high abundance of chaperones. Together, these features point to highly degenerate genomes that retain only the most essential functions, often including a considerable fraction of genes that serve the hosts. These discoveries have implications for the concept of minimal genomes, the origins of cellular organelles, and studies of symbiosis and host-associated microbiota.
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            The effects of alternate optimal solutions in constraint-based genome-scale metabolic models.

            Genome-scale constraint-based models of several organisms have now been constructed and are being used for model driven research. A key issue that may arise in the use of such models is the existence of alternate optimal solutions wherein the same maximal objective (e.g., growth rate) can be achieved through different flux distributions. Herein, we investigate the effects that alternate optimal solutions may have on the predicted range of flux values calculated using currently practiced linear (LP) and quadratic programming (QP) methods. An efficient LP-based strategy is described to calculate the range of flux variability that can be present in order to achieve optimal as well as suboptimal objective states. Sample results are provided for growth predictions of E. coli using glucose, acetate, and lactate as carbon substrates. These results demonstrate the extent of flux variability to be highly dependent on environmental conditions and network composition. In addition we examined the impact of alternate optima for growth under gene knockout conditions as calculated using QP-based methods. It was observed that calculations using QP-based methods can show significant variation in growth rate if the flux variability among alternate optima is high. The underlying biological significance and general source of such flux variability is further investigated through the identification of redundancies in the network (equivalent reaction sets) that lead to alternate solutions. Collectively, these results illustrate the variability inherent in metabolic flux distributions and the possible implications of this heterogeneity for constraint-based modeling approaches. These methods also provide an efficient and robust method to calculate the range of flux distributions that can be derived from quantitative fermentation data.
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              High-throughput generation, optimization and analysis of genome-scale metabolic models.

              Genome-scale metabolic models have proven to be valuable for predicting organism phenotypes from genotypes. Yet efforts to develop new models are failing to keep pace with genome sequencing. To address this problem, we introduce the Model SEED, a web-based resource for high-throughput generation, optimization and analysis of genome-scale metabolic models. The Model SEED integrates existing methods and introduces techniques to automate nearly every step of this process, taking approximately 48 h to reconstruct a metabolic model from an assembled genome sequence. We apply this resource to generate 130 genome-scale metabolic models representing a taxonomically diverse set of bacteria. Twenty-two of the models were validated against available gene essentiality and Biolog data, with the average model accuracy determined to be 66% before optimization and 87% after optimization.
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                Author and article information

                Contributors
                Role: Reviewing editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                31 March 2017
                2017
                : 6
                : e22679
                Affiliations
                [1 ]deptEvolution of Metabolic Diversity Laboratory , Unidad de Genómica Avanzada (Langebio), Cinvestav-IPN , Irapuato, Mexico
                [2 ]deptComputing, Environment and Life Sciences Directorate , Argonne National Laboratory , Lemont, United States
                [3 ]Computation Institute , University of Chicago, Chicago
                [4 ]deptMidwest Center for Structural Genomics, Biosciences Division , Argonne National Laboratory , Lemont, United States
                [5 ]deptStructural Biology Center, Biosciences Division , Argonne National Laboratory , Lemont, United States
                [6 ]deptDepartment of Microbiology and Molecular Genetics , University of Texas Health Science Center , Houston, United States
                [7 ]Cinvestav-IPN , Mexico
                [8 ]deptDepartment of Biochemistry and Molecular Biology , University of Chicago , Chicago, United States
                Barcelona Supercomputing Center - BSC , Spain
                Barcelona Supercomputing Center - BSC , Spain
                Author notes
                [†]

                Department of Biological Chemistry, Weizmann Institute of Science, Rehovot, Israel.

                [‡]

                Department of Molecular and Cell Biology, University of California, Berkeley, United States.

                [§]

                Ciencias de la Computación, Centro de Investigación en Matemáticas, Guanajuato, México.

                Author information
                http://orcid.org/0000-0003-1492-9497
                Article
                22679
                10.7554/eLife.22679
                5404923
                28362260
                fde2fbec-e800-48b5-a318-12af9823419b

                This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 25 October 2016
                : 25 March 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003141, Consejo Nacional de Ciencia y Tecnología;
                Award ID: 179290
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: 1611952
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: GM094585
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000072, National Institute of Dental and Craniofacial Research;
                Award ID: DE017382
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003141, Consejo Nacional de Ciencia y Tecnología;
                Award ID: 132376
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000015, U.S. Department of Energy;
                Award ID: DE-AC02-06CH11357
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Biochemistry
                Genomics and Evolutionary Biology
                Custom metadata
                2.5
                An integrated biochemical and evolutionary analysis shows how enzyme specificity evolves after gene loss during genome decay, implicating relaxation of purifying selection as a driving force for functional divergence.

                Life sciences
                evolution by gene loss,genome decay,enzyme substrate specificity,actinomyces,histidine and tryprophan biosynthesis,phosphoribosyl isomerase a,human,other

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