The Metabolic Network of Synechocystis sp. PCC 6803: Systemic Properties of Autotrophic Growth    

Abstract

and Manchester Interdisciplinary Biocentre, University of Manchester, M1 7DN Manchester, United Kingdom (R.S.) Unicellular cyanobacteria have attracted growing attention as potential host organisms for the production of valuable organic products and provide an ideal model to understand oxygenic photosynthesis and phototrophic metabolism. To obtain insight into the functional properties of phototrophic growth, we present a detailed reconstruction of the primary metabolic network of the autotrophic prokaryote Synechocystis sp. PCC 6803. The reconstruction is based on multiple data sources and extensive manual curation and significantly extends currently available repositories of cyanobacterial metabolism. A systematic functional analysis, utilizing the framework of flux-balance analysis, allows the prediction of essential metabolic pathways and reactions and allows the identification of inconsistencies in the current annotation. As a counterintuitive result, our computational model indicates that photorespiration is beneficial to achieve optimal growth rates. The reconstruction process highlights several obstacles currently encountered in the context of large-scale reconstructions of metabolic networks.Cyanobacteria are among the evolutionarily oldest organisms and are the only known prokaryotes capable of plant-like oxygenic photosynthesis. As primary producers in aquatic environments, they play an important role in global CO 2 assimilation and oxygen recycling. Recently, cyanobacteria have also attracted growing attention for economic purposes, including drug discovery and as prolific producers of natural products (Sielaff et al., 2006;Tan, 2007). In particular their ability to directly convert atmospheric CO 2 into biomass and organic compounds, driven by sunlight, offers considerable potential as a novel and renewable resource for bioenergy (Deng and Coleman, 1999;Atsumi et al., 2009;Mascarelli, 2009;Lindberg et al., 2010).Among the diverse cyanobacterial strains, Synechocystis sp. PCC 6803 is one of the most extensively studied model organisms for the analysis of photosynthetic processes. With a rich compendium of genomic, biochemical, and physiological data available, Synechocystis sp. PCC 6803, therefore, offers an ideal starting point to obtain insights into the systemic properties of phototrophic metabolism. The prerequisite for such a systemic description is a detailed reconstruction of the metabolic network of the organism: that is, a reconstruction of the comprehensive set of enzyme-catalyzed reactions required to support cellular growth and maintenance. Once a metabolic reconstruction is available, the vast array of methods developed by computational systems biology over the past decades allows us to dissect the functioning and interplay of possible metabolic routes and biochemical interconversions. In this respect, constraint-based modeling, most notably flux-balance analysis (FBA), has become a quasi-standard in the field. FBA is increasingly utilized to elucidate and characterize largescale network...

An experimentally anchored map of transcriptional start sites in the model cyanobacterium Synechocystis sp. PCC6803

“…Alternatively, some internal sense transcripts may have a regulatory scavenger function, acting as target mimicry for ncRNAs, as has been reported for plant miRNAs (37), or may act as independent ncRNAs. In summary, the annotated primary transcriptome of Synechocystis 6803, together with its recently modeled metabolic network (38), will greatly facilitate the use of this organism as a simple photosynthetic model in fundamental and systems biology and for the establishment of biofuel-producing microalgae.…”

Metabolic and Transcriptomic Phenotyping of Inorganic Carbon Acclimation in the Cyanobacterium Synechococcus elongatus PCC 7942

“…Another possible explanation for the coordinated increase of 2OG and succinate is to assume the existence of a modified rather than open TCA cycle. Experimental work on Synechocystis 6803 (Cooley et al, 2000) and a metabolic network model for this cyanobacterial strain (Knoop et al, 2010) suggest that a shunt exists that closes the TCA cycle, which starts from 2OG, goes over Glu, 4-aminobutanoate, succinate-semialdehyde, and goes back to succinate. Regardless of whether the TCA cycle is open or somehow closed, the newly fixed organic C is accumulated throughout the path from 3PGA via 2PGA to PEP and subsequently into intermediates of the TCA cycle.…”

Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale

“…source-sink interactions). Almost all FBA models of plant metabolism are restricted to one cell type (Boyle and Morgan, 2009;Knoop et al, 2010;Montagud et al, 2010;Cogne et al, 2011;Dal'Molin et al, 2011), one tissue or one organ (Grafahrend-Belau et al, 2009b;Schwender, 2011a, 2011b;Pilalis et al, 2011;Mintz-Oron et al, 2012), and only one model exists taking into account the interaction between two cell types by specifying the interaction between mesophyll and bundle sheath cells in C4 photosynthesis . So far, no model representing metabolism at the whole-plant scale exists.…”

“…In recent years, the FBA approach has been applied to several different plant species, such as maize (Zea mays;Dal'Molin et al, 2010;Saha et al, 2011), barley (Hordeum vulgare;Grafahrend-Belau et al, 2009b;Melkus et al, 2011;Rolletschek et al, 2011), rice (Oryza sativa; Lakshmanan et al, 2013), Arabidopsis (Arabidopsis thaliana; Poolman et al, 2009;de Oliveira Dal'Molin et al, 2010;Radrich et al, 2010;Williams et al, 2010;Mintz-Oron et al, 2012;Cheung et al, 2013), and rapeseed (Brassica napus; Schwender, 2011a, 2011b;Pilalis et al, 2011), as well as algae (Boyle and Morgan, 2009;Cogne et al, 2011;Dal'Molin et al, 2011) and photoautotrophic bacteria (Knoop et al, 2010;Montagud et al, 2010;Boyle and Morgan, 2011). These models have been used to study different aspects of metabolism, including the prediction of optimal metabolic yields and energy efficiencies Boyle and Morgan, 2011), changes in flux under different environmental and genetic backgrounds (Grafahrend-Belau et al, 2009b;Dal'Molin et al, 2010;Melkus et al, 2011), and nonintuitive metabolic pathways that merit subsequent experimental investigations (Poolman et al, 2009;Knoop et al, 2010;Rolletschek et al, 2011). Although FBA of plant metabolic models was shown to be capable of reproducing experimentally determined flux distributions (Williams et al, 2010;Hay and Schwender, 2011b) and generating new insights into metabolic behavior, capacities, and efficiencies (Sweetlove and Ratcliffe, 2011), challenges remain to advance the utility and predictive power of the models.…”