Cyanobacteria are versatile unicellular phototrophic microorganisms that are highly abundant in many environments. Owing to their capability to utilize solar energy and atmospheric carbon dioxide for growth, cyanobacteria are increasingly recognized as a prolific resource for the synthesis of valuable chemicals and various biofuels. To fully harness the metabolic capabilities of cyanobacteria necessitates an in-depth understanding of the metabolic interconversions taking place during phototrophic growth, as provided by genome-scale reconstructions of microbial organisms. Here we present an extended reconstruction and analysis of the metabolic network of the unicellular cyanobacterium Synechocystis sp. PCC 6803. Building upon several recent reconstructions of cyanobacterial metabolism, unclear reaction steps are experimentally validated and the functional consequences of unknown or dissenting pathway topologies are discussed. The updated model integrates novel results with respect to the cyanobacterial TCA cycle, an alleged glyoxylate shunt, and the role of photorespiration in cellular growth. Going beyond conventional flux-balance analysis, we extend the computational analysis to diurnal light/dark cycles of cyanobacterial metabolism.

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...

Cyanobacteria are an integral part of Earth's biogeochemical cycles and a promising resource for the synthesis of renewable bioproducts from atmospheric CO 2 . Growth and metabolism of cyanobacteria are inherently tied to the diurnal rhythm of light availability. As yet, however, insight into the stoichiometric and energetic constraints of cyanobacterial diurnal growth is limited. Here, we develop a computational framework to investigate the optimal allocation of cellular resources during diurnal phototrophic growth using a genome-scale metabolic reconstruction of the cyanobacterium Synechococcus elongatus PCC 7942. We formulate phototrophic growth as an autocatalytic process and solve the resulting time-dependent resource allocation problem using constraint-based analysis. Based on a narrow and well-defined set of parameters, our approach results in an ab initio prediction of growth properties over a full diurnal cycle. The computational model allows us to study the optimality of metabolite partitioning during diurnal growth. The cyclic pattern of glycogen accumulation, an emergent property of the model, has timing characteristics that are in qualitative agreement with experimental findings. The approach presented here provides insight into the time-dependent resource allocation problem of phototrophic diurnal growth and may serve as a general framework to assess the optimality of metabolic strategies that evolved in phototrophic organisms under diurnal conditions. constraint-based analysis | whole-cell models | bioenergetics | metabolism | circadian clock C yanobacterial photoautotrophic growth requires a highly coordinated distribution of cellular resources to different intracellular processes, including the de novo synthesis of proteins, ribosomes, lipids, and other cellular components. For unicellular organisms, the optimal allocation of limiting resources is a key determinant of evolutionary fitness. Owing to the importance of cellular resource allocation for understanding evolutionary trade-offs in bacterial metabolism, the cellular "protein economy" and its implications for bacterial growth laws have been studied extensively, albeit almost exclusively for heterotrophic organisms under stationary environmental conditions (1-7). For photoautotrophic organisms, including cyanobacteria, growthdependent resource allocation is further subject to diurnal lightdark (LD) cycles that partition cellular metabolism into distinct phases. Recent experimental results have demonstrated the relevance of time-specific synthesis for cellular survival and growth (8-10). Nonetheless, the implications and consequences of a diurnal environment for the cellular resource allocation problem are insufficiently understood, and computational approaches hitherto developed for heterotrophic growth are not straightforwardly applicable to diurnal phototrophic growth (11).Here, we propose a computational framework to quantitatively assess the optimality of diurnal resource allocation for phototrophic growth. We are primarily interested i...

BackgroundCyanobacteria are among the most abundant organisms on Earth and represent one of the oldest and most widespread clades known in modern phylogenetics. As the only known prokaryotes capable of oxygenic photosynthesis, cyanobacteria are considered to be a promising resource for renewable fuels and natural products. Our efforts to harness the sun's energy using cyanobacteria would greatly benefit from an increased understanding of the genomic diversity across multiple cyanobacterial strains. In this respect, the advent of novel sequencing techniques and the availability of several cyanobacterial genomes offers new opportunities for understanding microbial diversity and metabolic organization and evolution in diverse environments.ResultsHere, we report a whole genome comparison of multiple phototrophic cyanobacteria. We describe genetic diversity found within cyanobacterial genomes, specifically with respect to metabolic functionality. Our results are based on pair-wise comparison of protein sequences and concomitant construction of clusters of likely ortholog genes. We differentiate between core, shared and unique genes and show that the majority of genes are associated with a single genome. In contrast, genes with metabolic function are strongly overrepresented within the core genome that is common to all considered strains. The analysis of metabolic diversity within core carbon metabolism reveals parts of the metabolic networks that are highly conserved, as well as highly fragmented pathways.ConclusionsOur results have direct implications for resource allocation and further sequencing projects. It can be extrapolated that the number of newly identified genes still significantly increases with increasing number of new sequenced genomes. Furthermore, genome analysis of multiple phototrophic strains allows us to obtain a detailed picture of metabolic diversity that can serve as a starting point for biotechnological applications and automated metabolic reconstructions.

An endogenous molecular clockwork drives various cellular pathways including metabolism and the cell cycle. Its dysregulation is able to prompt pathological phenotypes including cancer. Besides dramatic metabolic alterations, cancer cells display severe changes in the clock phenotype with likely consequences in tumor progression and treatment response. In this study, we use a comprehensive systems-driven approach to investigate the effect of clock disruption on metabolic pathways and its impact on drug response in a cellular model of colon cancer progression. We identified distinctive time-related transcriptomic and metabolic features of a primary tumor and its metastatic counterpart. A mapping of the expression data to a comprehensive genome-scale reconstruction of human metabolism allowed for the in-depth functional characterization of 24 h-oscillating transcripts and pointed to a clock-driven metabolic reprogramming in tumorigenesis. In particular, we identified a set of five clock–regulated glycolysis genes, ALDH3A2, ALDOC, HKDC1, PCK2, and PDHB with differential temporal expression patterns. These findings were validated in organoids and in primary fibroblasts isolated from normal colon and colon adenocarcinoma from the same patient. We further identified a reciprocal connection of HKDC1 to the clock in the primary tumor, which is lost in the metastatic cells. Interestingly, a disruption of the core-clock gene BMAL1 impacts on HKDC1 and leads to a time-dependent rewiring of metabolism, namely an increase in glycolytic activity, as well as changes in treatment response. This work provides novel evidence regarding the complex interplay between the circadian clock and metabolic alterations in carcinogenesis and identifies new connections between both systems with pivotal roles in cancer progression and response to therapy.