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Microb Cell Fact. 2015 Nov 25;14:188. doi: 10.1186/s12934-015-0377-3.

Genome-scale metabolic reconstructions and theoretical investigation of methane conversion in Methylomicrobium buryatense strain 5G(B1).

Author information

1
Biology Department, San Diego State University, North Life Science Room 406, San Diego, CA, 92182-4614, USA. andrea.delatorre@mail.sdsu.edu.
2
Biology Department, San Diego State University, North Life Science Room 406, San Diego, CA, 92182-4614, USA. ametivier@mail.sdsu.edu.
3
Department of Chemical Engineering, University of Washington, Seattle, USA. fchu@uw.edu.
4
National Bioenergy Center, National Renewable Energy Laboratory, Golden, CO, USA. Lieve.Laurens@nrel.gov.
5
Department of Chemical Engineering, University of Washington, Seattle, USA. dacb@uw.edu.
6
eScience Institute, University of Washington, Seattle, USA. dacb@uw.edu.
7
National Bioenergy Center, National Renewable Energy Laboratory, Golden, CO, USA. Philip.Pienkos@nrel.gov.
8
Department of Chemical Engineering, University of Washington, Seattle, USA. lidstrom@uw.edu.
9
Department of Microbiology, University of Washington, Seattle, USA. lidstrom@uw.edu.
10
Biology Department, San Diego State University, North Life Science Room 406, San Diego, CA, 92182-4614, USA. mkalyuzhnaya@mail.sdsu.edu.
11
Viral Information Institute, San Diego State University, San Diego, USA. mkalyuzhnaya@mail.sdsu.edu.

Abstract

BACKGROUND:

Methane-utilizing bacteria (methanotrophs) are capable of growth on methane and are attractive systems for bio-catalysis. However, the application of natural methanotrophic strains to large-scale production of value-added chemicals/biofuels requires a number of physiological and genetic alterations. An accurate metabolic model coupled with flux balance analysis can provide a solid interpretative framework for experimental data analyses and integration.

RESULTS:

A stoichiometric flux balance model of Methylomicrobium buryatense strain 5G(B1) was constructed and used for evaluating metabolic engineering strategies for biofuels and chemical production with a methanotrophic bacterium as the catalytic platform. The initial metabolic reconstruction was based on whole-genome predictions. Each metabolic step was manually verified, gapfilled, and modified in accordance with genome-wide expression data. The final model incorporates a total of 841 reactions (in 167 metabolic pathways). Of these, up to 400 reactions were recruited to produce 118 intracellular metabolites. The flux balance simulations suggest that only the transfer of electrons from methanol oxidation to methane oxidation steps can support measured growth and methane/oxygen consumption parameters, while the scenario employing NADH as a possible source of electrons for particulate methane monooxygenase cannot. Direct coupling between methane oxidation and methanol oxidation accounts for most of the membrane-associated methane monooxygenase activity. However the best fit to experimental results is achieved only after assuming that the efficiency of direct coupling depends on growth conditions and additional NADH input (about 0.1-0.2 mol of incremental NADH per one mol of methane oxidized). The additional input is proposed to cover loss of electrons through inefficiency and to sustain methane oxidation at perturbations or support uphill electron transfer. Finally, the model was used for testing the carbon conversion efficiency of different pathways for C1-utilization, including different variants of the ribulose monophosphate pathway and the serine cycle.

CONCLUSION:

We demonstrate that the metabolic model can provide an effective tool for predicting metabolic parameters for different nutrients and genetic perturbations, and as such, should be valuable for metabolic engineering of the central metabolism of M. buryatense strains.

PMID:
26607880
PMCID:
PMC4658805
DOI:
10.1186/s12934-015-0377-3
[Indexed for MEDLINE]
Free PMC Article

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