Reconstruction and analysis of genome-scale metabolic model of a photosynthetic bacterium

<p>Abstract</p> <p>Background</p> <p><it>Synechocystis </it>sp. PCC6803 is a cyanobacterium considered as a candidate photo-biological production platform - an attractive cell factory capable of using CO₂and light as carbon and energy source, respectively. In order to enable efficient use of metabolic potential of <it>Synechocystis </it>sp. PCC6803, it is of importance to develop tools for uncovering stoichiometric and regulatory principles in the <it>Synechocystis </it>metabolic network.</p> <p>Results</p> <p>We report the most comprehensive metabolic model of <it>Synechocystis </it>sp. PCC6803 available, <it>i</it>Syn669, which includes 882 reactions, associated with 669 genes, and 790 metabolites. The model includes a detailed biomass equation which encompasses elementary building blocks that are needed for cell growth, as well as a detailed stoichiometric representation of photosynthesis. We demonstrate applicability of <it>i</it>Syn669 for stoichiometric analysis by simulating three physiologically relevant growth conditions of <it>Synechocystis </it>sp. PCC6803, and through <it>in silico </it>metabolic engineering simulations that allowed identification of a set of gene knock-out candidates towards enhanced succinate production. Gene essentiality and hydrogen production potential have also been assessed. Furthermore, <it>i</it>Syn669 was used as a transcriptomic data integration scaffold and thereby we found metabolic hot-spots around which gene regulation is dominant during light-shifting growth regimes.</p> <p>Conclusions</p> <p><it>i</it>Syn669 provides a platform for facilitating the development of cyanobacteria as microbial cell factories.</p

Modeling the Interplay between Photosynthesis, CO2 Fixation, and the Quinone Pool in a Purple Non-Sulfur Bacterium

Rhodopseudomonas palustris CGA009 is a purple non-sulfur bacterium that can fix carbon dioxide (CO 2) and nitrogen or break down organic compounds for its carbon and nitrogen requirements. Light, inorganic, and organic compounds can all be used for its source of energy. Excess electrons produced during its metabolic processes can be exploited to produce hydrogen gas or biodegradable polyesters. A genome-scale metabolic model of the bacterium was reconstructed to study the interactions between photosynthesis, CO2 fixation, and the redox state of the quinone pool. A comparison of model-predicted flux values with available Metabolic Flux Analysis (MFA) fluxes yielded predicted errors of 5–19% across four different growth substrates. The model predicted the presence of an unidentified sink responsible for the oxidation of excess quinols generated by the TC A cycle. Furthermore, light-dependent energy production was found to be highly dependent on the quinol oxidation rate. Finally, the extent of CO2 fixation was predicted to be dependent on the amount of ATP generated through the electron transport chain, with excess ATP going toward the energy-demanding Calvin-Benson-Bassham (CBB) pathway. Based on this analysis, it is hypothesized that the quinone redox state acts as a feed-forward controller of the CBB pathway, signaling the amount of ATP available

iRsp1095: A genome-scale reconstruction of the Rhodobacter sphaeroides metabolic network

<p>Abstract</p> <p>Background</p> <p><it>Rhodobacter sphaeroides </it>is one of the best studied purple non-sulfur photosynthetic bacteria and serves as an excellent model for the study of photosynthesis and the metabolic capabilities of this and related facultative organisms. The ability of <it>R. sphaeroides </it>to produce hydrogen (H₂), polyhydroxybutyrate (PHB) or other hydrocarbons, as well as its ability to utilize atmospheric carbon dioxide (CO₂) as a carbon source under defined conditions, make it an excellent candidate for use in a wide variety of biotechnological applications. A genome-level understanding of its metabolic capabilities should help realize this biotechnological potential.</p> <p>Results</p> <p>Here we present a genome-scale metabolic network model for <it>R. sphaeroides </it>strain 2.4.1, designated iRsp1095, consisting of 1,095 genes, 796 metabolites and 1158 reactions, including <it>R. sphaeroides</it>-specific biomass reactions developed in this study. Constraint-based analysis showed that iRsp1095 agreed well with experimental observations when modeling growth under respiratory and phototrophic conditions. Genes essential for phototrophic growth were predicted by single gene deletion analysis. During pathway-level analyses of <it>R. sphaeroides </it>metabolism, an alternative route for CO₂assimilation was identified. Evaluation of photoheterotrophic H₂production using iRsp1095 indicated that maximal yield would be obtained from growing cells, with this predicted maximum ~50% higher than that observed experimentally from wild type cells. Competing pathways that might prevent the achievement of this theoretical maximum were identified to guide future genetic studies.</p> <p>Conclusions</p> <p>iRsp1095 provides a robust framework for future metabolic engineering efforts to optimize the solar- and nutrient-powered production of biofuels and other valuable products by <it>R. sphaeroides </it>and closely related organisms.</p

Automation on the generation of genome scale metabolic models

Background: Nowadays, the reconstruction of genome scale metabolic models is a non-automatized and interactive process based on decision taking. This lengthy process usually requires a full year of one person's work in order to satisfactory collect, analyze and validate the list of all metabolic reactions present in a specific organism. In order to write this list, one manually has to go through a huge amount of genomic, metabolomic and physiological information. Currently, there is no optimal algorithm that allows one to automatically go through all this information and generate the models taking into account probabilistic criteria of unicity and completeness that a biologist would consider. Results: This work presents the automation of a methodology for the reconstruction of genome scale metabolic models for any organism. The methodology that follows is the automatized version of the steps implemented manually for the reconstruction of the genome scale metabolic model of a photosynthetic organism, {\it Synechocystis sp. PCC6803}. The steps for the reconstruction are implemented in a computational platform (COPABI) that generates the models from the probabilistic algorithms that have been developed. Conclusions: For validation of the developed algorithm robustness, the metabolic models of several organisms generated by the platform have been studied together with published models that have been manually curated. Network properties of the models like connectivity and average shortest mean path of the different models have been compared and analyzed.Comment: 24 pages, 2 figures, 2 table

Generation and Evaluation of a Genome-Scale Metabolic Network Model of Synechococcus elongatus PCC7942

[EN] The reconstruction of genome-scale metabolic models and their applications represent a great advantage of systems biology. Through their use as metabolic flux simulation models, production of industrially-interesting metabolites can be predicted. Due to the growing number of studies of metabolic models driven by the increasing genomic sequencing projects, it is important to conceptualize steps of reconstruction and analysis. We have focused our work in the cyanobacterium Synechococcus elongatus PCC7942, for which several analyses and insights are unveiled. A comprehensive approach has been used, which can be of interest to lead the process of manual curation and genome-scale metabolic analysis. The final model, iSyf715 includes 851 reactions and 838 metabolites. A biomass equation, which encompasses elementary building blocks to allow cell growth, is also included. 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Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

International audienceIncreasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system

Development and Application of Fluxomics Tools for Analyzing Metabolisms in Non-Model Microorganisms

Decoding microbial metabolism is of great importance in revealing the mechanisms governing the physiology of microbes and rewiring the cellular functions in metabolic engineering. Complementing the genomics, transcriptomics, proteinomics and metabolomics analysis of microbial metabolism, fluxomics tools can measure and simulate the in vivo enzymatic reactions as direct readouts of microbial metabolism. This dissertation develops and applies broad-scope tools in metabolic flux analysis to investigate metabolic insights of non-model environmental microorganisms. 13C-based pathway analysis has been applied to analyze specific carbon metabolic routes by tracing and analyzing isotopomer labeling patterns of different metabolites after growing cells with 13C-labeled substrates. Novel pathways, including Re-type citrate synthase in tricarboxylic acid cycle and citramalate pathways as an alternate route for isoleucine biosynthesis, have been identified in Thermoanaerobacter X514 and other environmental microorganisms. Via the same approach, the utilizations of diverse carbon/nitrogen substrates and productions of hydrogen during mixotrophic metabolism in Cyanothece 51142 have been characterized, and the medium for a slow-growing bacterium, Dehalococcoides ethenogenes 195, has been optimized. In addition, 13C-based metabolic flux analysis has been developed to quantitatively profile flux distributions in central metabolisms in a green sulfur bacterium, Chlorobaculum tepidum, and thermophilic ethanol-producing Thermoanaerobacter X514. The impact of isotope discrimination on 13C-based metabolic flux analysis has also been estimated. A constraint-based flux analysis approach was newly developed to integrate the bioprocess model into genome-scale flux balance analysis to decipher the dynamic metabolisms of Shewanella oneidensis MR-1. The sub-optimal metabolism and the time-dependent metabolic fluxes were profiled in a genome-scale metabolic network. A web-based platform was constructed for high-throughput metabolic model drafting to bridge the gap between fast-paced genome-sequencing and slow-paced metabolic model reconstruction. The platform provides over 1,000 sequenced genomes for model drafting and diverse customized tools for model reconstruction. The in silico simulation of flux distributions in both metabolic steady state and dynamic state can be achieved via flux balance analysis and dynamic flux balance analysis embedded in this platform. Cutting-edge fluxomics tools for functional characterization and metabolic prediction continue to be developed in the future. Broad-scope systems biology tools with integration of transcriptomics, proteinomics and fluxomics can reveal cell-wide regulations and speed up the metabolic engineering of non-model microorganisms for diverse bioenergy and environmental applications

Genome-scale reconstruction and system level investigation of the metabolic network of Methylobacterium extorquens AM1

Background: Methylotrophic microorganisms are playing a key role in biogeochemical processes - especially the global carbon cycle - and have gained interest for biotechnological purposes. Significant progress was made in the recent years in the biochemistry, genetics, genomics, and physiology of methylotrophic bacteria, showing that methylotrophy is much more widespread and versatile than initially assumed. Despite such progress, system-level description of the methylotrophic metabolism is currently lacking, and much remains to understand regarding the network-scale organization and properties of methylotrophy, and how the methylotrophic capacity emerges from this organization, especially in facultative organisms. Results: In this work, we report on the integrated, system-level investigation of the metabolic network of the facultative methylotroph Methylobacterium extorquens AM1, a valuable model of methylotrophic bacteria. The genome-scale metabolic network of the bacterium was reconstructed and contains 1139 reactions and 977 metabolites. The sub-network operating upon methylotrophic growth was identified from both in silico and experimental investigations, and 13C-fluxomics was applied to measure the distribution of metabolic fluxes under such conditions. The core metabolism has a highly unusual topology, in which the unique enzymes that catalyse the key steps of C1 assimilation are tightly connected by several, large metabolic cycles (serine cycle, ethylmalonyl- CoA pathway, TCA cycle, anaplerotic processes). The entire set of reactions must operate as a unique process to achieve C1 assimilation, but was shown to be structurally fragile based on network analysis. This observation suggests that in nature a strong pressure of selection must exist to maintain the methylotrophic capability. Nevertheless, substantial substrate cycling could be measured within C2/C3/C4 inter-conversions, indicating that the metabolic network is highly versatile around a flexible backbone of central reactions that allows rapid switching to multi-carbon sources. Conclusions: This work emphasizes that the metabolism of M. extorquens AM1 is adapted to its lifestyle not only in terms of enzymatic equipment, but also in terms of network-level structure and regulation. It suggests that the metabolism of the bacterium has evolved both structurally and functionally to an efficient but transitory utilization of methanol. Besides, this work provides a basis for metabolic engineering to convert methanol into value-added products