Expression of phosphofructokinase in Neisseria meningitidis

1 Netherlands Vaccine Institute (NVI), Unit Research and Development, PO Box 457, 3720 AL Bilthoven, The Netherlands 2 Wageningen University, Food and Bioprocess Engineering Group, PO Box 8129, 6700 EV Wageningen, The Netherlands 3 PAT consultancy, Kerkstraat 66, 4132 BG Vianen, The Netherlands Neis...

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Published in:Microbiology (Society for General Microbiology) Vol. 156; no. 2; pp. 530 - 542
Main Authors: Baart, Gino J. E, Langenhof, Marc, van de Waterbeemd, Bas, Hamstra, Hendrik-Jan, Zomer, Bert, van der Pol, Leo A, Beuvery, E. C, Tramper, Johannes, Martens, Dirk E
Format: Journal Article
Language:English
Published: Reading Soc General Microbiol 01-02-2010
Society for General Microbiology
Microbiology Society
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Summary:1 Netherlands Vaccine Institute (NVI), Unit Research and Development, PO Box 457, 3720 AL Bilthoven, The Netherlands 2 Wageningen University, Food and Bioprocess Engineering Group, PO Box 8129, 6700 EV Wageningen, The Netherlands 3 PAT consultancy, Kerkstraat 66, 4132 BG Vianen, The Netherlands Neisseria meningitidis serogroup B is a pathogen that can infect diverse sites within the human host. According to the N. meningitidis genomic information and experimental observations, glucose can be completely catabolized through the Entner–Doudoroff pathway and the pentose phosphate pathway. The Embden–Meyerhof–Parnas pathway is not functional, because the gene for phosphofructokinase (PFK) is not present. The phylogenetic distribution of PFK indicates that in most obligate aerobic organisms, PFK is lacking. We conclude that this is because of the limited contribution of PFK to the energy supply in aerobically grown organisms in comparison with the energy generated through oxidative phosphorylation. Under anaerobic or microaerobic conditions, the available energy is limiting, and PFK provides an advantage, which explains the presence of PFK in many (facultatively) anaerobic organisms. In accordance with this, in silico flux balance analysis predicted an increase of biomass yield as a result of PFK expression. However, analysis of a genetically engineered N. meningitidis strain that expressed a heterologous PFK showed that the yield of biomass on substrate decreased in comparison with a pfkA -deficient control strain, which was associated mainly with an increase in CO 2 production, whereas production of by-products was similar in the two strains. This might explain why the pfkA gene has not been obtained by horizontal gene transfer, since it is initially unfavourable for biomass yield. No large effects related to heterologous expression of pfkA were observed in the transcriptome. Although our results suggest that introduction of PFK does not contribute to a more efficient strain in terms of biomass yield, achievement of a robust, optimal metabolic network that enables a higher growth rate or a higher biomass yield might be possible after adaptive evolution of the strain, which remains to be investigated. Correspondence Gino J. E. Baart gino.baart{at}ugent.be Abbreviations: dcw, dry cell weight; ED, Entner–Doudoroff; EMP, Embden–Meyerhof–Parnas; F6P, fructose 6-phosphate; FBA, flux balance analysis; FR, fold ratio; HGT, horizontal gene transfer; LP, linear programming; MCS, Monte Carlo simulation; NVI, Netherlands Vaccine Institute; PEP, phosphoenolpyruvate; PP, pentose phosphate, PTS system, phosphotransferase system Present address: Ghent University, Department of Plant Systems Biology/Department of Biology, Technologiepark 927, 9052 Ghent, Belgium. The microarray data discussed in this paper are available under GEO accession number GSE18951 . Three supplementary tables, showing the relationships in bacterial species between PFK (encoded by pfkA ), the glucose-specific PTS transporter EIIB Glc (encoded by ptsG ) and glucokinase, GK (encoded by glk ), are available with the online version of this paper. Additional data are also available with the online version of this paper in the form of the Excel file Additional data.xls, which includes six worksheets. The first worksheet, named ‘model’, contains the simplified metabolic model. The second worksheet, named ‘abbreviations’, contains a list of abbreviations of the metabolites. The third worksheet, named ‘flux distributions’, contains the calculated flux distributions of all experimental datasets. The fourth worksheet, named ‘phylogenetic profiling’, contains the phylogenetic profile of 373 species, and is available as a separate PDF. The fifth worksheet, named ‘phylo reactions’, contains the relevant reactions list and the amino acid sequences used for the phylogenetic profiling. The sixth worksheet, named ‘transcriptome’, contains the gene expression data.
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Present address: Ghent University, Department of Plant Systems Biology/Department of Biology, Technologiepark 927, 9052 Ghent, Belgium.
ISSN:1350-0872
1465-2080
DOI:10.1099/mic.0.031641-0