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Appl Microbiol Biotechnol. 2002 May;58(6):813-22. Epub 2002 Mar 15.

Integration of the information from gene expression and metabolic fluxes for the analysis of the regulatory mechanisms in Synechocystis.

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Department of Biochemical Engineering and Science, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan., Japan.


Synechocystis was grown under autotrophic, mixotrophic and heterotrophic conditions, and the gene expression patterns at the mRNA and protein levels were measured by using semi-quantitative RT-PCR and two-dimensional electrophoresis (2DE), respectively. Moreover, the intracellular metabolic flux distributions in Synechocystis grown under different trophic conditions were also determined using the carbon isotope labeling technique. By combining the information obtained from the transcript levels, protein abundance and metabolic fluxes, the regulatory mechanisms of some enzymes involved in the central metabolism of Synechocystis during growth in the different culture conditions were analyzed. It was found that depending on the energy source available to cyanobacterial cells, the enzymes required for central metabolism were differently regulated according to different mechanisms. The expression of several genes, such as rbcLS and gap2, was light-regulated transcriptionally, while the gene gnd was regulated in response to an apparent flux requirement but by an unknown mechanism. The expression of other genes was independent of the presence of light. The reactions catalyzed by G6PDH, Fbp, PfkA and FbaA were not regulated through enzyme synthesis but by a change in metabolite concentrations. The enzyme PrK was post-translationally regulated by light, probably through the operation of ferredoxin/thioredoxin system. For the enzyme RubisCO, both transcriptional and post-translational regulation was observed. These findings demonstrate that the information obtained from the analysis of mRNA expression, protein expression, and metabolic flux distribution is necessary to understand the regulatory events in complex cellular networks.

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