Literature DB >> 21051442

Copy number alterations among mammalian enzymes cluster in the metabolic network.

Michaël Bekaert1, Gavin C Conant.   

Abstract

Using two high-quality human metabolic networks, we employed comparative genomics techniques to infer metabolic network structures for seven other mammals. We then studied copy number alterations (CNAs) in these networks. Using a graph-theoretic approach, we show that the pattern of CNAs is distinctly different from the random distributions expected under genetic drift. Instead, we find that changes in copy number are most common among transporter genes and that the CNAs differ depending on the mammalian lineage in question. Thus, we find an excess of transporter genes in cattle involved in the milk production, secretion, and regulation. These results suggest a potential role for dosage selection in the evolution of mammalian metabolic networks.

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Year:  2010        PMID: 21051442     DOI: 10.1093/molbev/msq296

Source DB:  PubMed          Journal:  Mol Biol Evol        ISSN: 0737-4038            Impact factor:   16.240


  15 in total

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3.  The players may change but the game remains: network analyses of ruminal microbiomes suggest taxonomic differences mask functional similarity.

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5.  Molecular evolution of psbA gene in ferns: unraveling selective pressure and co-evolutionary pattern.

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6.  Selection for higher gene copy number after different types of plant gene duplications.

Authors:  Corey M Hudson; Emily E Puckett; Michaël Bekaert; J Chris Pires; Gavin C Conant
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7.  Gene duplication and phenotypic changes in the evolution of mammalian metabolic networks.

Authors:  Michaël Bekaert; Gavin C Conant
Journal:  PLoS One       Date:  2014-01-28       Impact factor: 3.240

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9.  Diet shifts provoke complex and variable changes in the metabolic networks of the ruminal microbiome.

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Journal:  Microbiome       Date:  2017-06-08       Impact factor: 14.650

10.  Genome-Guided Phylo-Transcriptomic Methods and the Nuclear Phylogentic Tree of the Paniceae Grasses.

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Journal:  Sci Rep       Date:  2017-10-19       Impact factor: 4.379

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