Reconstruction of the absorption spectrum of Synechocystis sp. PCC 6803 optical mutants from the in vivo signature of individual pigments.

In this work, we reconstructed the absorption spectrum of different Synechocystis sp. PCC 6803 optical strains by summing the computed signature of all pigments present in this organism. To do so, modifications to in vitro pigment spectra were first required: namely wavelength shift, curve smoothing, and the package effect calculation derived from high pigment densities were applied. As a result, we outlined a plausible shape for the in vivo absorption spectrum of each chromophore. These are flatter and slightly broader in physiological conditions yet the mean weight-specific absorption coefficient remains identical to the in vitro conditions. Moreover, we give an estimate of all pigment concentrations without applying spectrophotometric correlations, which are often prone to error. The computed cell spectrum reproduces in an accurate manner the experimental spectrum for all the studied wavelengths in the wild-type, Olive, and PAL strain. The gathered pigment concentrations are in agreement with reported values in literature. Moreover, different illumination set-ups were evaluated to calculate the mean absorption cross-section of each chromophore. Finally, a qualitative estimate of light-limited cellular growth at each wavelength is given. This investigation describes a novel way to approach the cell absorption spectrum and shows all its inherent potential for photosynthesis research.

Evaluation of electrical signals in pine trees in a mediterranean forest ecosystem.

Electric potential differences in living plants are explained by theories based on sap flow. In order to acquire more advanced knowledge about the spatial distribution of these electric potential measures in trees, this research aims to analyze electrical signals in a population of Aleppo pines (Pinus halepensis Mill.) in a representative Mediterranean forest ecosystem. The specific research objective is to assess some of the most significant factors that influence the distribution pattern of those electric signals: tree age, measurement type and electrode placement. The research has been conducted in representative forest stands, obtaining measurements of different representative trees. After a statistical evaluation of the obtained results, the main conclusions of our research are: A.Tree maturity influences directly on electric potential. B.Maximum electrical signals can be measured in young pines showing values of 0.6 V and 0.6 µA for voltage and current, respectively. C.The distribution patterns of both voltage and short-circuit current depending on electrode placement are uniform.

Large scale evaluation of differences between network-based and pairwise sequence-alignment-based methods of dendrogram reconstruction.

Dendrograms are a way to represent relationships between organisms. Nowadays, these are inferred based on the comparison of genes or protein sequences by taking into account their differences and similarities. The genetic material of choice for the sequence alignments (all the genes or sets of genes) results in distinct inferred dendrograms. In this work, we evaluate differences between dendrograms reconstructed with different methodologies and for different sets of organisms chosen at random from a much larger set. A statistical analysis is performed to estimate fluctuations between the results obtained from the different methodologies that allows us to validate a systematic approach, based on the comparison of the organisms' metabolic networks for inferring dendrograms. This has the advantage that it allows the comparison of organisms very far away in the evolutionary tree even if they have no known ortholog gene in common. Our results show that dendrograms built using information from metabolic networks are similar to the standard sequence-based dendrograms and can be a complement to them.

Improving a Synechocystis-based photoautotrophic chassis through systematic genome mapping and validation of neutral sites.

The use of microorganisms as cell factories frequently requires extensive molecular manipulation. Therefore, the identification of genomic neutral sites for the stable integration of ectopic DNA is required to ensure a successful outcome. Here we describe the genome mapping and validation of five neutral sites in the chromosome of Synechocystis sp. PCC 6803, foreseeing the use of this cyanobacterium as a photoautotrophic chassis. To evaluate the neutrality of these loci, insertion/deletion mutants were produced, and to assess their functionality, a synthetic green fluorescent reporter module was introduced. The constructed integrative vectors include a BioBrick-compatible multiple cloning site insulated by transcription terminators, constituting robust cloning interfaces for synthetic biology approaches. Moreover, Synechocystis mutants (chassis) ready to receive purpose-built synthetic modules/circuits are also available. This work presents a systematic approach to map and validate chromosomal neutral sites in cyanobacteria, and that can be extended to other organisms.

Synechocystis sp. PCC6803 metabolic models for the enhanced production of hydrogen.

In the present economy, difficulties to access energy sources are real drawbacks to maintain our current lifestyle. In fact, increasing interests have been gathered around efficient strategies to use energy sources that do not generate high CO2 titers. Thus, science-funding agencies have invested more resources into research on hydrogen among other biofuels as interesting energy vectors. This article reviews present energy challenges and frames it into the present fuel usage landscape. Different strategies for hydrogen production are explained and evaluated. Focus is on biological hydrogen production; fermentation and photon-fuelled hydrogen production are compared. Mathematical models in biology can be used to assess, explore and design production strategies for industrially relevant metabolites, such as biofuels. We assess the diverse construction and uses of genome-scale metabolic models of cyanobacterium Synechocystis sp. PCC6803 to efficiently obtain biofuels. This organism has been studied as a potential photon-fuelled production platform for its ability to grow from carbon dioxide, water and photons, on simple culture media. Finally, we review studies that propose production strategies to weigh this organism's viability as a biofuel production platform. Overall, the work presented in this review unveils the industrial capabilities of cyanobacterium Synechocystis sp. PCC6803 to evolve interesting metabolites as a clean biofuel production platform.

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

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. The applicability of the model is finally demonstrated by simulating autotrophic growth conditions of Synechococcus elongatus PCC7942.

New approach for phylogenetic tree recovery based on genome-scale metabolic networks.

A wide range of applications and research has been done with genome-scale metabolic models. In this work, we describe an innovative methodology for comparing metabolic networks constructed from genome-scale metabolic models and how to apply this comparison in order to infer evolutionary distances between different organisms. Our methodology allows a quantification of the metabolic differences between different species from a broad range of families and even kingdoms. This quantification is then applied in order to reconstruct phylogenetic trees for sets of various organisms.

Experimental and modeling analysis of Synechocystis sp. PCC 6803 growth.

BACKGROUND/AIMS: The influence of different parameters such as temperature, irradiance, nitrate concentration, pH, and an external carbon source on Synechocystis PCC 6803 growth was evaluated. METHODS: 4.5-ml cuvettes containing 2 ml of culture, a high-throughput system equivalent to batch cultures, were used with gas exchange ensured by the use of a Parafilm™ cover. The effect of the different variables on maximum growth was assessed by a multi-way statistical analysis. RESULTS: Temperature and pH were identified as the key factors. It was observed that Synechocystis cells have a strong influence on the external pH. The optimal growth temperature was 33°C while light-saturating conditions were reached at 40 µE·m⁻²·s⁻¹. CONCLUSION: It was demonstrated that Synechocystis exhibits a marked difference in behavior between autotrophic and glucose-based mixotrophic conditions, and that nitrate concentrations did not have a significant influence, probably due to endogenous nitrogen reserves. Furthermore, a dynamic metabolic model of Synechocystis photosynthesis was developed to gain insights on the underlying mechanism enabling this cyanobacterium to control the levels of external pH. The model showed a coupled effect between the increase of the pH and ATP production which in turn allows a higher carbon fixation rate.

Construction of a chassis for hydrogen production: physiological and molecular characterization of a Synechocystis sp. PCC 6803 mutant lacking a functional bidirectional hydrogenase.

Cyanobacteria are photosynthetic prokaryotes that are promising 'low-cost' microbial cell factories due to their simple nutritional requirements and metabolic plasticity, and the availability of tools for their genetic manipulation. The unicellular non-nitrogen-fixing Synechocystis sp. PCC 6803 is the best studied cyanobacterial strain and its genome was the first to be sequenced. The vast amount of physiological and molecular data available, together with a relatively small genome, makes Synechocystis suitable for computational metabolic modelling and to be used as a photoautotrophic chassis in synthetic biology applications. To prepare it for the introduction of a synthetic hydrogen producing device, a Synechocystis sp. PCC 6803 deletion mutant lacking an active bidirectional hydrogenase (ΔhoxYH) was produced and characterized at different levels: physiological, proteomic and transcriptional. The results showed that, under conditions favouring hydrogenase activity, 17 of the 210 identified proteins had significant differential fold changes in comparisons of the mutant with the wild-type. Most of these proteins are related to the redox and energy state of the cell. Transcriptional studies revealed that only six genes encoding those proteins exhibited significant differences in transcript levels. Moreover, the mutant exhibits similar growth behaviour compared with the wild-type, reflecting Synechocystis plasticity and metabolic adaptability. Overall, this study reveals that the Synechocystis ΔhoxYH mutant is robust and can be used as a photoautotrophic chassis for the integration of synthetic constructs, i.e. molecular constructs assembled from well characterized biological and/or synthetic parts (e.g. promoters, regulators, coding regions, terminators) designed for a specific purpose.

Flux coupling and transcriptional regulation within the metabolic network of the photosynthetic bacterium Synechocystis sp. PCC6803.

Synechocystis sp. PCC6803 is a model cyanobacterium capable of producing biofuels with CO(2) as carbon source and with its metabolism fueled by light, for which it stands as a potential production platform of socio-economic importance. Compilation and characterization of Synechocystis genome-scale metabolic model is a pre-requisite toward achieving a proficient photosynthetic cell factory. To this end, we report iSyn811, an upgraded genome-scale metabolic model of Synechocystis sp. PCC6803 consisting of 956 reactions and accounting for 811 genes. To gain insights into the interplay between flux activities and metabolic physiology, flux coupling analysis was performed for iSyn811 under four different growth conditions, viz., autotrophy, mixotrophy, heterotrophy, and light-activated heterotrophy (LH). Initial steps of carbon acquisition and catabolism formed the versatile center of the flux coupling networks, surrounded by a stable core of pathways leading to biomass building blocks. This analysis identified potential bottlenecks for hydrogen and ethanol production. Integration of transcriptomic data with the Synechocystis flux coupling networks lead to identification of reporter flux coupling pairs and reporter flux coupling groups - regulatory hot spots during metabolic shifts triggered by the availability of light. Overall, flux coupling analysis provided insight into the structural organization of Synechocystis sp. PCC6803 metabolic network toward designing of a photosynthesis-based production platform.

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

BACKGROUND: Synechocystis sp. PCC6803 is a cyanobacterium considered as a candidate photo-biological production platform--an attractive cell factory capable of using CO2 and light as carbon and energy source, respectively. In order to enable efficient use of metabolic potential of Synechocystis sp. PCC6803, it is of importance to develop tools for uncovering stoichiometric and regulatory principles in the Synechocystis metabolic network. RESULTS: We report the most comprehensive metabolic model of Synechocystis sp. PCC6803 available, iSyn669, 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 iSyn669 for stoichiometric analysis by simulating three physiologically relevant growth conditions of Synechocystis sp. PCC6803, and through in silico 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, iSyn669 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. CONCLUSIONS: iSyn669 provides a platform for facilitating the development of cyanobacteria as microbial cell factories.