Arnau Montagud

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

BackgroundSynechocystis 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.ResultsWe report the most comprehensive metabolic model of Synechocystis sp. PCC6803 available, i 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 i Syn669 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, i 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.Conclusionsi Syn669 provides a platform for facilitating the development of cyanobacteria as microbial cell factories.

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.

Microbial Diversity in the Midguts of Field and Lab-Reared Populations of the European Corn Borer Ostrinia nubilalis

Background Insects are associated with microorganisms that contribute to the digestion and processing of nutrients. The European Corn Borer (ECB) is a moth present world-wide, causing severe economical damage as a pest on corn and other crops. In the present work, we give a detailed view of the complexity of the microorganisms forming the ECB midgut microbiota with the objective of comparing the biodiversity of the midgut-associated microbiota and explore their potential as a source of genes and enzymes with biotechnological applications. Methodological/Principal Findings A high-throughput sequencing approach has been used to identify bacterial species, genes and metabolic pathways, particularly those involved in plant-matter degradation, in two different ECB populations (field-collected vs. lab-reared population with artificial diet). Analysis of the resulting sequences revealed the massive presence of Staphylococcus warneri and Weissella paramesenteroides in the lab-reared sample. This enabled us to reconstruct both genomes almost completely. Despite the apparently low diversity, 208 different genera were detected in the sample, although most of them at very low frequency. By contrast, the natural population exhibited an even higher taxonomic diversity along with a wider array of cellulolytic enzyme families. However, in spite of the differences in relative abundance of major taxonomic groups, not only did both metagenomes share a similar functional profile but also a similar distribution of non-redundant genes in different functional categories. Conclusions/Significance Our results reveal a highly diverse pool of bacterial species in both O. nubilalis populations, with major differences: The lab-reared sample is rich in gram-positive species (two of which have almost fully sequenced genomes) while the field sample harbors mainly gram-negative species and has a larger set of cellulolytic enzymes. We have found a clear relationship between the diet and the midgut microbiota, which reveals the selection pressure of food on the community of intestinal bacteria.

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.

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.

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.

Automation on the Generation of Genome-Scale Metabolic Models

Nowadays, the reconstruction of genome-scale metabolic models is a nonautomatized and interactive process based on decision making. This lengthy process usually requires a full year of one persons 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. 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, 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. 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.

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

Abstract 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.

Aequorin-expressing yeast emits light under electric control

In this study, we show the use of direct external electrical stimulation of a jellyfish luminescent calcium-activated protein, aequorin, expressed in a transgenic yeast strain. Yeast cultures were electrically stimulated through two electrodes coupled to a standard power generator. Even low (1.5 V) electric pulses triggered a rapid light peak and serial light pulses were obtained after electric pulses were applied periodically, suggesting that the system is re-enacted after a short refraction time. These results open up a new scenario, in the very interphase between synthetic biology and cybernetics, in which complex cellular behavior might be subjected to electrical control.

Yeast cultures with UCP1 uncoupling activity as a heating device

Uncoupling proteins (UCPs) are mitochondrial transporters that facilitate controlled dissipation of the proton gradient and thus regulate energetic efficiency. The heat generating capacity of UCP from brown adipose tissue was investigated in yeasts expressing the protein recombinantly under conditions in which the temperature of the growth medium was measured directly. A Liquid Culture Calorimeter (LCC) was built consisting of a thermally isolated culture flask able to keep yeast cultures warm without resorting to additional heating. The exact internal temperature of the cultures was monitored for 24h through a thermocouple connected to a data logger. Under these conditions, significant temperature increases (1 degrees C) in the media were recorded when yeast strains expressing endogenously active UCP1 mutants were grown. This is the first direct evidence, in a eukaryotic microbial model, of a temperature rise associated with uncoupling activity, and could be seen as the first step toward developing a biological heating device.