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      Systems analysis of ethanol production in the genetically engineered cyanobacterium Synechococcus sp. PCC 7002

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          Abstract

          Background

          Future sustainable energy production can be achieved using mass cultures of photoautotrophic microorganisms, which are engineered to synthesize valuable products directly from CO 2 and sunlight. As cyanobacteria can be cultivated in large scale on non-arable land, these phototrophic bacteria have become attractive organisms for production of biofuels. Synechococcus sp. PCC 7002, one of the cyanobacterial model organisms, provides many attractive properties for biofuel production such as tolerance of seawater and high light intensities.

          Results

          Here, we performed a systems analysis of an engineered ethanol-producing strain of the cyanobacterium Synechococcus sp. PCC 7002, which was grown in artificial seawater medium over 30 days applying a 12:12 h day–night cycle. Biosynthesis of ethanol resulted in a final accumulation of 0.25% (v/v) ethanol, including ethanol lost due to evaporation. The cultivation experiment revealed three production phases. The highest production rate was observed in the initial phase when cells were actively growing. In phase II growth of the producer strain stopped, but ethanol production rate was still high. Phase III was characterized by a decrease of both ethanol production and optical density of the culture. Metabolomics revealed that the carbon drain due to ethanol diffusion from the cell resulted in the expected reduction of pyruvate-based intermediates. Carbon-saving strategies successfully compensated the decrease of central intermediates of carbon metabolism during the first phase of fermentation. However, during long-term ethanol production the producer strain showed clear indications of intracellular carbon limitation. Despite the decreased levels of glycolytic and tricarboxylic acid cycle intermediates, soluble sugars and even glycogen accumulated in the producer strain. The changes in carbon assimilation patterns are partly supported by proteome analysis, which detected decreased levels of many enzymes and also revealed the stress phenotype of ethanol-producing cells. Strategies towards improved ethanol production are discussed.

          Conclusions

          Systems analysis of ethanol production in Synechococcus sp. PCC 7002 revealed initial compensation followed by increasing metabolic limitation due to excessive carbon drain from primary metabolism.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13068-017-0741-0) contains supplementary material, which is available to authorized users.

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          Most cited references60

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          Evolution of photosynthesis.

          Energy conversion of sunlight by photosynthetic organisms has changed Earth and life on it. Photosynthesis arose early in Earth's history, and the earliest forms of photosynthetic life were almost certainly anoxygenic (non-oxygen evolving). The invention of oxygenic photosynthesis and the subsequent rise of atmospheric oxygen approximately 2.4 billion years ago revolutionized the energetic and enzymatic fundamentals of life. The repercussions of this revolution are manifested in novel biosynthetic pathways of photosynthetic cofactors and the modification of electron carriers, pigments, and existing and alternative modes of photosynthetic carbon fixation. The evolutionary history of photosynthetic organisms is further complicated by lateral gene transfer that involved photosynthetic components as well as by endosymbiotic events. An expanding wealth of genetic information, together with biochemical, biophysical, and physiological data, reveals a mosaic of photosynthetic features. In combination, these data provide an increasingly robust framework to formulate and evaluate hypotheses concerning the origin and evolution of photosynthesis.
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            TagFinder for the quantitative analysis of gas chromatography--mass spectrometry (GC-MS)-based metabolite profiling experiments.

            Typical GC-MS-based metabolite profiling experiments may comprise hundreds of chromatogram files, which each contain up to 1000 mass spectral tags (MSTs). MSTs are the characteristic patterns of approximately 25-250 fragment ions and respective isotopomers, which are generated after gas chromatography (GC) by electron impact ionization (EI) of the separated chemical molecules. These fragment ions are subsequently detected by time-of-flight (TOF) mass spectrometry (MS). MSTs of profiling experiments are typically reported as a list of ions, which are characterized by mass, chromatographic retention index (RI) or retention time (RT), and arbitrary abundance. The first two parameters allow the identification, the later the quantification of the represented chemical compounds. Many software tools have been reported for the pre-processing, the so-called curve resolution and deconvolution, of GC-(EI-TOF)-MS files. Pre-processing tools generate numerical data matrices, which contain all aligned MSTs and samples of an experiment. This process, however, is error prone mainly due to (i) the imprecise RI or RT alignment of MSTs and (ii) the high complexity of biological samples. This complexity causes co-elution of compounds and as a consequence non-selective, in other words impure MSTs. The selection and validation of optimal fragment ions for the specific and selective quantification of simultaneously eluting compounds is, therefore, mandatory. Currently validation is performed in most laboratories under human supervision. So far no software tool supports the non-targeted and user-independent quality assessment of the data matrices prior to statistical analysis. TagFinder may fill this gap. TagFinder facilitates the analysis of all fragment ions, which are observed in GC-(EI-TOF)-MS profiling experiments. The non-targeted approach allows the discovery of novel and unexpected compounds. In addition, mass isotopomer resolution is maintained by TagFinder processing. This feature is essential for metabolic flux analyses and highly useful, but not required for metabolite profiling. Whenever possible, TagFinder gives precedence to chemical means of standardization, for example, the use of internal reference compounds for retention time calibration or quantitative standardization. In addition, external standardization is supported for both compound identification and calibration. The workflow of TagFinder comprises, (i) the import of fragment ion data, namely mass, time and arbitrary abundance (intensity), from a chromatography file interchange format or from peak lists provided by other chromatogram pre-processing software, (ii) the annotation of sample information and grouping of samples into classes, (iii) the RI calculation, (iv) the binning of observed fragment ions of equal mass from different chromatograms into RI windows, (v) the combination of these bins, so-called mass tags, into time groups of co-eluting fragment ions, (vi) the test of time groups for intensity correlated mass tags, (vii) the data matrix generation and (viii) the extraction of selective mass tags supported by compound identification. Thus, TagFinder supports both non-targeted fingerprinting analyses and metabolite targeted profiling. Exemplary TagFinder workspaces and test data sets are made available upon request to the contact authors. TagFinder is made freely available for academic use from http://www-en.mpimp-golm.mpg.de/03-research/researchGroups/01-dept1/Root_Metabolism/smp/TagFinder/index.html.
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              The tricarboxylic acid cycle in cyanobacteria.

              It is generally accepted that cyanobacteria have an incomplete tricarboxylic acid (TCA) cycle because they lack 2-oxoglutarate dehydrogenase and thus cannot convert 2-oxoglutarate to succinyl-coenzyme A (CoA). Genes encoding a novel 2-oxoglutarate decarboxylase and succinic semialdehyde dehydrogenase were identified in the cyanobacterium Synechococcus sp. PCC 7002. Together, these two enzymes convert 2-oxoglutarate to succinate and thus functionally replace 2-oxoglutarate dehydrogenase and succinyl-CoA synthetase. These genes are present in all cyanobacterial genomes except those of Prochlorococcus and marine Synechococcus species. Closely related genes occur in the genomes of some methanogens and other anaerobic bacteria, which are also thought to have incomplete TCA cycles.
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                Author and article information

                Contributors
                kopka@mpimp-golm.mpg.de
                sschmidt@mpimp-golm.mpg.de
                frederik_dethloff@psych.mpg.de
                nadin-pade@web.de
                sfberendt@googlemail.com
                Marco.Schottkowski@algenol.com
                Nico.Martin@algenol.com
                Ulf.Duehring@algenol.com
                E.Kuchmina@gmx.de
                Heike.Enke@cyano-biotech.com
                Dan.Kramer@cyano-biotech.com
                annegret.wilde@biologie.uni-freiburg.de
                martin.hagemann@uni-rostock.de
                Alexandra.Friedrich@algenol.com
                Journal
                Biotechnol Biofuels
                Biotechnol Biofuels
                Biotechnology for Biofuels
                BioMed Central (London )
                1754-6834
                6 March 2017
                6 March 2017
                2017
                : 10
                : 56
                Affiliations
                [1 ]ISNI 0000 0004 0491 976X, GRID grid.418390.7, , Max-Planck-Institute of Molecular Plant Physiology, ; Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
                [2 ]ISNI 0000 0000 9497 5095, GRID grid.419548.5, , Max-Planck-Institute of Psychiatry, ; Kraepelinstraße 2-10, 80804 Munich, Germany
                [3 ]ISNI 0000000121858338, GRID grid.10493.3f, Institute of Biological Sciences, Plant Physiology, , University of Rostock, ; Albert-Einstein-Str. 3, 18059 Rostock, Germany
                [4 ]Algenol Biofuels Germany GmbH, Magnusstraße 11, 12489 Berlin, Germany
                [5 ]GRID grid.5963.9, Institute of Biology III, , University of Freiburg, ; Schänzlestr. 1, 79104 Freiburg, Germany
                [6 ]Cyano Biotech GmbH, Magnusstraße 11, 12489 Berlin, Germany
                Article
                741
                10.1186/s13068-017-0741-0
                5340023
                28286551
                e7ffcb46-e737-42c8-8343-58d7e74bc793
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 21 October 2016
                : 23 February 2017
                Funding
                Funded by: Bundesministerium für Bildung und Forschung (DE)
                Award ID: Cyanosys 0316183
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                Biotechnology
                synechococcus sp. pcc 7002,carbon assimilation,carbon partitioning,cyanobacteria,ethanol,glycolysis,metabolomics,proteomics,pyruvate

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