Characterization of an easy-to-use method for the routine analysis of the central metabolism using an affordable low-resolution GC-MS system: application to Arthrospira platensis.

We developed an easy-to-use method for the routine analysis of the central metabolism using an affordable low-resolution GC-MS system run in SIM mode. The profiling approach was optimized for the derivatization protocol of some 60 targeted metabolites. The performance of two silylation reagents (MSTFA and BSTFA) that allowed the comprehensive derivatization of 42 key intermediary metabolites of the 60 initially targeted (organic acids, phosphate derivatives, monosaccharides and amino acids) was measured. The experimental results unequivocally showed that the MSTFA reagent met mandatory criteria including ease of handling (a very simple one-step protocol was developed), comprehensiveness of derivatization (the 42 compounds covered the extended metabolic pathways of the central carbon metabolism, with a coverage percentage ranging from 17% for the worst to 90% for the best result), optimized response coefficient of the whole derivatives (median value greater than the others by one order of magnitude) and repeatability of the protocol (RSD value below 25% for the whole procedure). When tested in real conditions (cyanobacteria polar extract), the experimental results showed that the profiling methodology was adequately repeatable (RSD = 35%) to ensure quantification results comparable with much more sensitive analytical techniques (capillary electrophoresis/mass spectrometry and liquid chromatography/triple quadrupole mass spectrometry system), while needing only about twice the quantity of biomass. Graphical abstract Schematic overview of an easy-to-use profiling method for the routine analysis of the central metabolism using a low-resolution GC-MS system.

A systems-wide understanding of photosynthetic acclimation in algae and higher plants.

The ability of phototrophs to colonise different environments relies on robust protection against oxidative stress, a critical requirement for the successful evolutionary transition from water to land. Photosynthetic organisms have developed numerous strategies to adapt their photosynthetic apparatus to changing light conditions in order to optimise their photosynthetic yield, which is crucial for life on Earth to exist. Photosynthetic acclimation is an excellent example of the complexity of biological systems, where highly diverse processes, ranging from electron excitation over protein protonation to enzymatic processes coupling ion gradients with biosynthetic activity, interact on drastically different timescales from picoseconds to hours. Efficient functioning of the photosynthetic apparatus and its protection is paramount for efficient downstream processes, including metabolism and growth. Modern experimental techniques can be successfully integrated with theoretical and mathematical models to promote our understanding of underlying mechanisms and principles. This review aims to provide a retrospective analysis of multidisciplinary photosynthetic acclimation research carried out by members of the Marie Curie Initial Training Project, AccliPhot, placing the results in a wider context. The review also highlights the applicability of photosynthetic organisms for industry, particularly with regards to the cultivation of microalgae. It intends to demonstrate how theoretical concepts can successfully complement experimental studies broadening our knowledge of common principles in acclimation processes in photosynthetic organisms, as well as in the field of applied microalgal biotechnology.

PredAlgo: a new subcellular localization prediction tool dedicated to green algae.

The unicellular green alga Chlamydomonas reinhardtii is a prime model for deciphering processes occurring in the intracellular compartments of the photosynthetic cell. Organelle-specific proteomic studies have started to delineate its various subproteomes, but sequence-based prediction software is necessary to assign proteins subcellular localizations at whole genome scale. Unfortunately, existing tools are oriented toward land plants and tend to mispredict the localization of nuclear-encoded algal proteins, predicting many chloroplast proteins as mitochondrion targeted. We thus developed a new tool called PredAlgo that predicts intracellular localization of those proteins to one of three intracellular compartments in green algae: the mitochondrion, the chloroplast, and the secretory pathway. At its core, a neural network, trained using carefully curated sets of C. reinhardtii proteins, divides the N-terminal sequence into overlapping 19-residue windows and scores the probability that they belong to a cleavable targeting sequence for one of the aforementioned organelles. A targeting prediction is then deduced for the protein, and a likely cleavage site is predicted based on the shape of the scoring function along the N-terminal sequence. When assessed on an independent benchmarking set of C. reinhardtii sequences, PredAlgo showed a highly improved discrimination capacity between chloroplast- and mitochondrion-localized proteins. Its predictions matched well the results of chloroplast proteomics studies. When tested on other green algae, it gave good results with Chlorophyceae and Trebouxiophyceae but tended to underpredict mitochondrial proteins in Prasinophyceae. Approximately 18% of the nuclear-encoded C. reinhardtii proteome was predicted to be targeted to the chloroplast and 15% to the mitochondrion.

Photobioreactor design for isotopic non-stationary 13C-metabolic flux analysis (INST 13C-MFA) under photoautotrophic conditions.

Adaptive metabolic behavior of photoautotrophic microorganisms toward genetic and environmental perturbations can be interpreted in a quantitative depiction of carbon flow through a biochemical reaction network using isotopic non-stationary (13) C-metabolic flux analysis (INST (13) C-MFA). To evaluate (13) C-metabolic flux maps for Chlamydomonas reinhardtii, an original experimental framework was designed allowing rapid, reliable collection of high-quality isotopomer data against time. It involved (i) a short-time (13) C labeling injection device based on mixing control in a torus-shaped photobioreactor with plug-flow hydrodynamics allowing a sudden step-change in the (13) C proportion in the substrate feed and (ii) a rapid sampling procedure using an automatic fast filtration method coupled to a manual rapid liquid nitrogen quenching step. (13) C-substrate labeling enrichment was controlled through the total dissolved inorganic carbon concentration in the pulsed solution. First results were obtained from steady-state continuous culture measurements allowing the characterization of the kinetics of label incorporation into light-limited growing cells cultivated in a photobioreactor operating at the maximal biomass productivity for an incident photon flux density of 200 µmol m(-2) s(-1). (13)C label incorporation was measured for 21 intracellular metabolites using IC-MS/MS in 58 samples collected across a labeling experiment duration of 7 min. The fastest labeling rate was observed for 2/3-phosphoglycerate with an apparent isotopic stationary state reached after 300 s. The labeling rate was consistent with the optimized mixing time of about 4.9 s inside the reactor and the shortest reliable sampling period assessed at 5 s.

Network reduction in metabolic pathway analysis: elucidation of the key pathways involved in the photoautotrophic growth of the green alga Chlamydomonas reinhardtii.

Metabolic pathway analysis aims at discovering and analyzing meaningful routes and their interactions in metabolic networks. A major difficulty in applying this technique lies in the decomposition of metabolic flux distributions into elementary mode or extreme pathway activity patterns, which in general is not unique. We propose a network reduction approach based on the decomposition of a set of flux vectors representing adaptive microbial metabolic behavior in bioreactors into a minimal set of shared pathways. Several optimality criteria from the literature were compared in order to select the most appropriate objective function. We further analyze photoautotrophic metabolism of the green alga Chlamydomonas reinhardtii growing in a photobioreactor under maximal growth rate conditions. Key pathways involved in its adaptive metabolic response to changes in light influx are identified and discussed using an energetic approach.

A model-based method for investigating bioenergetic processes in autotrophically growing eukaryotic microalgae: application to the green algae Chlamydomonas reinhardtii.

A constraint-based modeling approach was developed to investigate the metabolic response of the eukaryotic microalgae Chlamydomonas reinhardtii under photoautotrophic conditions. The model explicitly includes thermodynamic and energetic constraints on the functioning metabolism. A mixed integer linear programming method was used to determine the optimal flux distributions with regard to this set of constraints. It enabled us, in particular, to highlight the existence of a light-driven respiration depending on the incident photon flux density in photobioreactors functioning in physical light limitation.

Design, operation, and modeling of a membrane photobioreactor to study the growth of the Cyanobacterium Arthrospira platensis in space conditions.

A membrane photobioreactor was designed, implemented and used to grow the cyanobacterium Arthrospira platensis PCC 8005 in batch mode. Growth was followed directly by monitoring optical density and indirectly by measuring pressure increase due to the oxygen produced and separated from the liquid phase by diffusion through a hydrophobic membrane, and pH increase due to carbon consumption. When the pressure attained an upper limit, valves opened automatically, and the oxygen in the gas chamber was flushed out with nitrogen. As expected, two growth phases were observed, a short exponential phase followed by a linear phase, indicating limitation by light transfer. Growth rate during the second phase was measured easily and accurately, and consistency of optical density, pressure and pH data values was checked using a model of the system. Pressure measurement was found best suited to monitoring and measuring growth rate in space in terms of accuracy, precision and reliability.

Identification of a metabolic network structure representative of Arthrospira (spirulina) platensis metabolism.

A comprehensive network structure for the autotrophic growth of Arthrospira platensis is proposed. The metabolic network was built up with 121 reactions and 134 metabolites including biomass synthesis, production of a growth-associated exopolysaccharide, and energy aspects. The model supports the existence of a metabolic shunt of PEP to pyruvate through PEP carboxylase, NAD(+)-dependent malate dehydrogenase and malic enzyme to convert NADH,H(+) into NADPH,H(+). A limit in Arthrospira growth metabolism due to NADH,H(+) balancing is evidenced, explaining why the maximal light-dependent mass yield of the growth-associated exopolysaccharide was 0.51 kg EPS kg(-1) biomass, consistent with experimental results.

Uptake of macrominerals and trace elements by the cyanobacterium Spirulina platensis (Arthrospira platensis PCC 8005) under photoautotrophic conditions: culture medium optimization.

Uptake rates of macrominerals and trace elements were characterized in batch and continuous cultures of Spirulina platensis under photoautotropic conditions. The values of yield coefficients were determined using inductively coupled plasma emission spectroscopy (ICP-ES). Further simplifications of culture medium proved possible, mainly in the trace element solutions; concentrations of some elements were lowered and trace elements B, Mo, V, Cr, Ni, Co, W, and Ti were removed.