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Swimming in Light: A Large-Scale Computational Analysis of the Metabolism of Dinoroseobacter shibae

Overview of attention for article published in PLoS Computational Biology, October 2013
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Title
Swimming in Light: A Large-Scale Computational Analysis of the Metabolism of Dinoroseobacter shibae
Published in
PLoS Computational Biology, October 2013
DOI 10.1371/journal.pcbi.1003224
Pubmed ID
Authors

Rene Rex, Nelli Bill, Kerstin Schmidt-Hohagen, Dietmar Schomburg

Abstract

The Roseobacter clade is a ubiquitous group of marine α-proteobacteria. To gain insight into the versatile metabolism of this clade, we took a constraint-based approach and created a genome-scale metabolic model (iDsh827) of Dinoroseobacter shibae DFL12T. Our model is the first accounting for the energy demand of motility, the light-driven ATP generation and experimentally determined specific biomass composition. To cover a large variety of environmental conditions, as well as plasmid and single gene knock-out mutants, we simulated 391,560 different physiological states using flux balance analysis. We analyzed our results with regard to energy metabolism, validated them experimentally, and revealed a pronounced metabolic response to the availability of light. Furthermore, we introduced the energy demand of motility as an important parameter in genome-scale metabolic models. The results of our simulations also gave insight into the changing usage of the two degradation routes for dimethylsulfoniopropionate, an abundant compound in the ocean. A side product of dimethylsulfoniopropionate degradation is dimethyl sulfide, which seeds cloud formation and thus enhances the reflection of sunlight. By our exhaustive simulations, we were able to identify single-gene knock-out mutants, which show an increased production of dimethyl sulfide. In addition to the single-gene knock-out simulations we studied the effect of plasmid loss on the metabolism. Moreover, we explored the possible use of a functioning phosphofructokinase for D. shibae.

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Geographical breakdown

Country Count As %
Portugal 2 5%
Brazil 1 2%
India 1 2%
United Kingdom 1 2%
United States 1 2%
Unknown 35 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 41%
Researcher 9 22%
Student > Master 5 12%
Professor 3 7%
Student > Postgraduate 2 5%
Other 2 5%
Unknown 3 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 41%
Biochemistry, Genetics and Molecular Biology 8 20%
Immunology and Microbiology 4 10%
Environmental Science 3 7%
Engineering 2 5%
Other 3 7%
Unknown 4 10%