Henning Knoop

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

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.

Flux balance analysis of cyanobacterial metabolism: the metabolic network of Synechocystis sp. PCC 6803.

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.

The diversity of cyanobacterial metabolism: genome analysis of multiple phototrophic microorganisms

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 suns 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.

Modelling cyanobacteria: from metabolism to integrative models of phototrophic growth

Cyanobacteria are phototrophic microorganisms of global importance and have recently attracted increasing attention due to their capability to convert sunlight and atmospheric CO(2) directly into organic compounds, including carbon-based biofuels. The utilization of cyanobacteria as a biological chassis to generate third-generation biofuels would greatly benefit from an increased understanding of cyanobacterial metabolism and its interplay with other cellular processes. In this respect, metabolic modelling has been proposed as a way to overcome the traditional trial and error methodology that is often employed to introduce novel pathways. In particular, flux balance analysis and related methods have proved to be powerful tools to investigate the organization of large-scale metabolic networks-with the prospect of predicting modifications that are likely to increase the yield of a desired product and thereby to streamline the experimental progress and avoid futile avenues. This contribution seeks to describe the utilization of metabolic modelling as a research tool to understand the metabolism and phototrophic growth of cyanobacteria. The focus of the contribution is on a mathematical description of the metabolic network of Synechocystis sp. PCC 6803 and its analysis using constraint-based methods. A particular challenge is to integrate the description of the metabolic network with other cellular processes, such as the circadian clock, the photosynthetic light reactions, carbon concentration mechanism, and transcriptional regulation-aiming at a predictive model of a cyanobacterium in silico.

Physiological tolerance and stoichiometric potential of cyanobacteria for hydrocarbon fuel production

Cyanobacteria are capable of directly converting sunlight, carbon dioxide and water into hydrocarbon fuel or precursors thereof. Many biological and non-biological factors will influence the ability of such a production system to become economically sustainable. We evaluated two factors in engineerable cyanobacteria which could potentially limit economic sustainability: (i) tolerance of the host to the intended end-product, and (ii) stoichiometric potential for production. Alcohols, when externally added, inhibited growth the most, followed by aldehydes and acids, whilst alkanes were the least inhibitory. The growth inhibition became progressively greater with increasing chain-length for alcohols, whilst the intermediate C6 alkane caused more inhibition than both C3 and C11 alkane. Synechocystis sp. PCC 6803 was more tolerant to some of the tested chemicals than Synechococcus elongatus PCC 7942, particularly ethanol and undecane. Stoichiometric evaluation of the potential yields suggested that there is no difference in the potential productivity of harvestable energy between any of the studied fuels, with the exception of ethylene, for which maximal stoichiometric yield is considerably lower. In summary, it was concluded that alkanes would constitute the best choice metabolic end-product for fuel production using cyanobacteria if high-yielding strains can be developed.

Cyanobacterial biofuels: new insights and strain design strategies revealed by computational modeling

BackgroundCyanobacteria are increasingly recognized as promising cell factories for the production of renewable biofuels and chemical feedstocks from sunlight, CO2, and water. However, most biotechnological applications of these organisms are still characterized by low yields. Increasing the production performance of cyanobacteria remains therefore a crucial step.ResultsIn this work we use a stoichiometric network model of Synechocystis sp. PCC 6803 in combination with CASOP and minimal cut set analysis to systematically identify and characterize suitable strain design strategies for biofuel synthesis, specifically for ethanol and isobutanol. As a key result, improving upon other works, we demonstrate that higher-order knockout strategies exist in the model that lead to coupling of growth with high-yield biofuel synthesis under phototrophic conditions. Enumerating all potential knockout strategies (cut sets) reveals a unifying principle behind the identified strain designs, namely to reduce the ratio of ATP to NADPH produced by the photosynthetic electron transport chain. Accordingly, suitable knockout strategies seek to block cyclic and other alternate electron flows, such that ATP and NADPH are exclusively synthesized via the linear electron flow whose ATP/NADPH ratio is below that required for biomass synthesis. The products of interest are then utilized by the cell as sinks for reduction equivalents in excess. Importantly, the calculated intervention strategies do not rely on the assumption of optimal growth and they ensure that maintenance metabolism in the absence of light remains feasible. Our analyses furthermore suggest that a moderately increased ATP turnover, realized, for example, by ATP futile cycles or other ATP wasting mechanisms, represents a promising target to achieve increased biofuel yields.ConclusionOur study reveals key principles of rational metabolic engineering strategies in cyanobacteria towards biofuel production. The results clearly show that achieving obligatory coupling of growth and product synthesis in photosynthetic bacteria requires fundamentally different intervention strategies compared to heterotrophic organisms.

A computational analysis of stoichiometric constraints and trade-offs in cyanobacterial biofuel production.

Cyanobacteria are a promising biological chassis for the synthesis of renewable fuels and chemical bulk commodities. Significant efforts have been devoted to improve the yields of cyanobacterial products. However, while the introduction and heterologous expression of product-forming pathways is often feasible, the interactions and incompatibilities of product synthesis with the host metabolism are still insufficiently understood. In this work, we investigate the stoichiometric properties and trade-offs that underlie cyanobacterial product formation using a computational reconstruction of cyanobacterial metabolism. First, we evaluate the synthesis requirements of a selection of cyanobacterial products of potential biotechnological interest. Second, the large-scale metabolic reconstruction allows us to perform in silico experiments that mimic and predict the metabolic changes that must occur in the transition from a growth-only phenotype to a production-only phenotype. Applied to the synthesis of ethanol, ethylene, and propane, these in silico transition experiments point to bottlenecks and potential modification targets in cyanobacterial metabolism. Our analysis reveals incompatibilities between biotechnological product synthesis and native host metabolism, such as shifts in ATP/NADPH demand and the requirement to reintegrate metabolic by-products. Similar strategies can be employed for a large class of cyanobacterial products to identify potential stoichiometric bottlenecks.

A quantitative evaluation of ethylene production in the recombinant cyanobacterium Synechocystis sp. PCC 6803 harboring the ethylene-forming enzyme by membrane inlet mass spectrometry.

The prediction of the worlds future energy consumption and global climate change makes it desirable to identify new technologies to replace or augment fossil fuels by environmentally sustainable alternatives. One appealing sustainable energy concept is harvesting solar energy via photosynthesis coupled to conversion of CO2 into chemical feedstock and fuel. In this work, the production of ethylene, the most widely used petrochemical produced exclusively from fossil fuels, in the model cyanobacterium Synechocystis sp. PCC 6803 is studied. A novel instrumentation setup for quantitative monitoring of ethylene production using a combination of flat-panel photobioreactor coupled to a membrane-inlet mass spectrometer is introduced. Carbon partitioning is estimated using a quantitative model of cyanobacterial metabolism. The results show that ethylene is produced under a wide range of light intensities with an optimum at modest irradiances. The results allow production conditions to be optimized in a highly controlled setup.

Identification of the light-independent phosphoserine pathway as an additional source of serine in the cyanobacterium Synechocystis sp. PCC 6803.

L-serine is one of the proteinogenic amino acids and participates in several essential processes in all organisms. In plants, the light-dependent photorespiratory and the light-independent phosphoserine pathways contribute to serine biosynthesis. In cyanobacteria, the light-dependent photorespiratory pathway for serine synthesis is well characterized, but the phosphoserine pathway has not been identified. Here, we investigated three candidate genes for enzymes of the phosphoserine pathway in Synechocystis sp. PCC 6803. Only the gene for the D-3-phosphoglycerate dehydrogenase is correctly annotated in the genome database, whereas the 3-phosphoserine transaminase and 3-phosphoserine phosphatase (PSP) proteins are incorrectly annotated and were identified here. All enzymes were obtained as recombinant proteins and showed the activities necessary to catalyse the three-step phosphoserine pathway. The genes coding for the phosphoserine pathway were found in most cyanobacterial genomes listed in CyanoBase. The pathway seems to be essential for cyanobacteria, because it was impossible to mutate the gene coding for PSP in Synechocystis sp. PCC 6803 or in Synechococcus elongatus PCC 7942. A model approach indicates a 30-60% contribution of the phosphoserine pathway to the overall serine pool. Hence, this study verified that cyanobacteria, similar to plants, use the phosphoserine pathway in addition to photorespiration for serine biosynthesis.

Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth

Significance Cyanobacteria are important players in Earth’s biogeochemical cycles and a promising resource for the synthesis of renewable raw materials. Of particular interest are the cellular organization that enables fast growth and the corresponding intracellular limits on growth rates. Here, we develop a constraint-based computational model of phototrophic growth to investigate the optimal allocation of cellular resources in a diurnal light environment. The model-derived optimal metabolite partitioning during diurnal growth is in qualitative agreement with recent experimental data. Our results suggest that phototrophic metabolism at fast growth rates is highly optimized and strongly dependent on the timing characteristics of enzyme synthesis. Furthermore, we demonstrate that the experimentally observed pattern of glycogen accumulation is in agreement with predictions based on optimal resource allocation. Cyanobacteria are an integral part of Earth’s biogeochemical cycles and a promising resource for the synthesis of renewable bioproducts from atmospheric CO2. 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.