Detailing the optimality of photosynthesis in cyanobacteria through systems biology analysis

Nogales, Juan; Guđmundsson, Steinn; Knight, Eric M.; Palsson, Bernhard Ø.; Thiele, Ines

doi:10.1073/pnas.1117907109

Cited by 276 publications

(342 citation statements)

References 40 publications

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“…2). An FBA analysis of photosynthetic cyanobacterial metabolism was recently used to explore some of these alternative pathways (Nogales et al, 2012). It was confirmed that NADPH/ATP rebalancing is required to match the energy demand of the metabolic system as a whole, not just the CalvinBenson cycle, and the importance of these alternative pathways increased with increasing light intensity.…”

Section: Energy Metabolism In Photosynthetic Tissuesmentioning

confidence: 99%

Inference and prediction of metabolic network fluxes

Nikoloski

Perez-Storey

2015

Plant Physiol.

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In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping.The metabolic systems of plants utilize a continual energy stream (i.e. light in the case of photosynthetic tissues and chemical energy in the case of heterotrophic tissues) to drive a complex network of hundreds of chemical reactions away from equilibrium. Ultimately, this leads to the catalyzed biosynthesis of the ordered polymeric biomolecules that make up the biomass of the cells in each tissue and underpins the growth and development of the plant (Smith and Stitt, 2007). To operate on a time scale relevant for life, the system is dependent upon the acceleration of its chemistry by enzymes. Additionally, because of the highly compartmented nature of plant cells, transporter proteins are required to permit the movement of metabolites (i.e. the substrates and products of biochemical reactions) between subcellular compartments. Transporter proteins are also required to bring substrates into cells and to allow the excretion of waste products and other metabolites such as defense compounds. The sum total of expressed genes encoding enzymes and metabolite transporters determines the metabolic capabilities of a cell. In a growing tissue, the net output of this metabolism is anabolic (i.e. leading to the biosynthesis of biomass constituents such as starch, fructans, cell wall, lipid, and protein), but catabolic metabolism is also required. Most importantly, catabolism generates universal energy currencies such as ATP and NAD(P)H, whose turnover is used to provide the energetic driving force for anabolism. Catabolism is also important to allow the turnover of cellular components for regulatory and repair purposes (Linster et al., 2013;Ishihara et al., 2015). The turnover and resynthesis of cellular components, along with the maintenance of electrochemical potentials across membranes, are important facets of metabolism that need to be considered alongside the biosynthesis of macromolecules for growth (Stitt, 2013;Sweetlove et al., 2013).Metabolism, then, is the entirety of c...

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Section: Energy Metabolism In Photosynthetic Tissuesmentioning

confidence: 99%

Inference and prediction of metabolic network fluxes

Nikoloski

Perez-Storey

2015

Plant Physiol.

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“…To put the maximal turnover capacity of the LDHs in the cell extract into a quantitative perspective, it is relevant to realize that for Synechocystis to grow at max , i.e., with an 8-h doubling time, a minimal flux of carbon through the Calvin-Benson cycle of 3.70 mmol carbon/g DW/h is necessary (based on an estimated cellular composition of the biomass of C 4 H 7 O 2 N [29,35]). Hence, the measured in vitro enzyme activity of the lactate dehydrogenases demonstrates that in order to achieve the goal of a true "cell factory" for lactic acid (a cell that is able to channel the large majority of the fixed carbon into lactic acid), at least 10-fold-higher LDH activity is required than was achieved in this study.…”

Section: Discussionmentioning

confidence: 99%

Engineering a Cyanobacterial Cell Factory for Production of Lactic Acid

Angermayr

Paszota

Hellingwerf

2012

Appl Environ Microbiol

163

144

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Metabolic engineering of microorganisms has become a versatile tool to facilitate production of bulk chemicals, fuels, etc. Accordingly, CO 2 has been exploited via cyanobacterial metabolism as a sustainable carbon source of biofuel and bioplastic precursors. Here we extended these observations by showing that integration of an ldh gene from Bacillus subtilis (encoding an L-lactate dehydrogenase) into the genome of Synechocystis sp. strain PCC6803 leads to L-lactic acid production, a phenotype which is shown to be stable for prolonged batch culturing. Coexpression of a heterologous soluble transhydrogenase leads to an even higher lactate production rate and yield (lactic acid accumulating up to a several-millimolar concentration in the extracellular medium) than those for the single ldh mutant. The expression of a transhydrogenase alone, however, appears to be harmful to the cells, and a mutant carrying such a gene is rapidly outcompeted by a revertant(s) with a wild-type growth phenotype. Furthermore, our results indicate that the introduction of a lactate dehydrogenase rescues this phenotype by preventing the reversion.C oncerns about shrinking supplies of fossil fuel and about climate change have been a strong incentive during the past decade for the development of sustainable alternatives to provide fuel and chemical feedstock. Because of these concerns, several different ways to produce solar biofuel have been proposed. Initially the proposed procedures were based on the conversion of agricultural crops into short-chain alcohols. This unfortunately results in a direct competition for resources between the energy sector and food supply. Therefore, second-generation types of processes were proposed, in which one aims at the conversion of the triglyceride fraction from, e.g., green algae into biodiesel, which relaxes the competition between energy and the food supply. Nevertheless, the conversion of CO 2 into biofuel, driven by solar energy, is still rather indirect even in this second-generation approach: CO 2 is first converted into the complex building blocks of biomass, from which subsequently the triglyceride fraction must be extracted and converted into biodiesel.For these reasons, a third-generation type of process has been proposed, in which CO 2 is converted into biofuel directly, accomplished by the introduction of genes encoding a metabolic-e.g., fermentative-pathway into a cyanobacterium, resulting in the emergence of a "photofermentative" chimera. For several biofuel and chemical feedstock products, including ethanol, ethylene, isoprene, D-lactic acid, glucose, sucrose, isobutyraldehyde, and 1-butanol, the proof of principle of this approach has been provided (see references 3,8,10,24,25,28, and 39 and references therein). Ideally, this process would be carried out in such a way that the majority of the CO 2 that is fixed in the cyanobacterial CalvinBenson cycle is converted into the selected product. This implies that the central metabolism in the selected chimera should be largely redirected tow...

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“…This translation results in a genome scale metabolic reconstruction, which represents a biochemical, genetic and genomic (BIGG) knowledge base [11] for a target organism. These reconstructions may then be used to investigate fundamental biological questions, guide industrial strain design, and provide a systems perspective for analysis of the expanding ocean of omics data [12][13][14].…”

Section: Systems Biologymentioning

confidence: 99%