Sugar-stimulated CO2 sequestration by the green microalga Chlorella vulgaris.

To convert waste CO2 from flue gases of power plants into value-added products, bio-mitigation technologies show promise. In this study, we cultivated a fast-growing species of green microalgae, Chlorella vulgaris, in different sizes of photobioreactors (PBRs) and developed a strategy using small doses of sugars for enhancing CO2 sequestration under light-emitting diode illumination. Glucose supplementation at low levels resulted in an increase of photoautotrophic growth-driven biomass generation as well as CO2 capture by 10% and its enhancement corresponded to an increase of supplied photon flux. The utilization of urea instead of nitrate as the sole nitrogen source increased photoautotrophic growth by 14%, but change of nitrogen source didn't compromise glucose-induced enhancement of photoautotrophic growth. The optimized biomass productivity achieved was 30.4% higher than the initial productivity of purely photoautotrophic culture. The major pigments in the obtained algal biomass were found comparable to its photoautotrophic counterpart and a high neutral lipids productivity of 516.6 mg/(L·day) was achieved after optimization. A techno-economic model was also developed, indicating that LED-based PBRs represent a feasible strategy for converting CO2 into value-added algal biomass.

Dataset on economic analysis of mass production of algae in LED-based photobioreactors.

The data presented in this article are related to the research article entitled "Sugar-stimulated CO2 sequestration by the green microalga Chlorella vulgaris" (Fu et al., 2019) [1]. The data describe a rational design and scale-up of LED-based photobioreactors for producing value-added algal biomass while removing waste CO2 from flu gases from power plants. The dataset were created from growth rate experiments for biomass production including direct biomass productivity data, PBR size and setup parameters, medium composition as well as indirect energy cost and overhead in Iceland. A complete economic analysis is formed through a cost breakdown as well as PBR scalability predictions.

Metabolic systems analysis of LPS induced endothelial dysfunction applied to sepsis patient stratification.

Endothelial dysfunction contributes to sepsis outcome. Metabolic phenotypes associated with endothelial dysfunction are not well characterised in part due to difficulties in assessing endothelial metabolism in situ. Here, we describe the construction of iEC2812, a genome scale metabolic reconstruction of endothelial cells and its application to describe metabolic changes that occur following endothelial dysfunction. Metabolic gene expression analysis of three endothelial subtypes using iEC2812 suggested their similar metabolism in culture. To mimic endothelial dysfunction, an in vitro sepsis endothelial cell culture model was established and the metabotypes associated with increased endothelial permeability and glycocalyx loss after inflammatory stimuli were quantitatively defined through metabolomics. These data and transcriptomic data were then used to parametrize iEC2812 and investigate the metabotypes of endothelial dysfunction. Glycan production and increased fatty acid metabolism accompany increased glycocalyx shedding and endothelial permeability after inflammatory stimulation. iEC2812 was then used to analyse sepsis patient plasma metabolome profiles and predict changes to endothelial derived biomarkers. These analyses revealed increased changes in glycan metabolism in sepsis non-survivors corresponding to metabolism of endothelial dysfunction in culture. The results show concordance between endothelial health and sepsis survival in particular between endothelial cell metabolism and the plasma metabolome in patients with sepsis.

Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks.

The temperature dependence of biological processes has been studied at the levels of individual biochemical reactions and organism physiology (e.g. basal metabolic rates) but has not been examined at the metabolic network level. Here, we used a systems biology approach to characterize the temperature dependence of the human red blood cell (RBC) metabolic network between 4 and 37 °C through absolutely quantified exo- and endometabolomics data. We used an Arrhenius-type model (Q10) to describe how the rate of a biochemical process changes with every 10 °C change in temperature. Multivariate statistical analysis of the metabolomics data revealed that the same metabolic network-level trends previously reported for RBCs at 4 °C were conserved but accelerated with increasing temperature. We calculated a median Q10 coefficient of 2.89 ± 1.03, within the expected range of 2-3 for biological processes, for 48 individual metabolite concentrations. We then integrated these metabolomics measurements into a cell-scale metabolic model to study pathway usage, calculating a median Q10 coefficient of 2.73 ± 0.75 for 35 reaction fluxes. The relative fluxes through glycolysis and nucleotide metabolism pathways were consistent across the studied temperature range despite the non-uniform distributions of Q10 coefficients of individual metabolites and reaction fluxes. Together, these results indicate that the rate of change of network-level responses to temperature differences in RBC metabolism is consistent between 4 and 37 °C. More broadly, we provide a baseline characterization of a biochemical network given no transcriptional or translational regulation that can be used to explore the temperature dependence of metabolism.

Mannose and fructose metabolism in red blood cells during cold storage in SAGM.

BACKGROUND: Alternate sugar metabolism during red blood cell (RBC) storage is not well understood. Here we report fructose and mannose metabolism in RBCs during cold storage in SAGM and the impact that these monosaccharides have on metabolic biomarkers of RBC storage lesion. STUDY DESIGN AND METHODS: RBCs were stored in SAGM containing uniformly labeled 13 C-fructose or 13 C-mannose at 9 or 18 mmol/L concentration for 25 days. RBCs and media were sampled at 14 time points during storage and analyzed using ultraperformance liquid chromatography-mass spectrometry. Blood banking quality assurance measurements were performed. RESULTS: Red blood cells incorporated fructose and mannose during cold storage in the presence of glucose. Mannose was metabolized in preference to glucose via glycolysis. Fructose lowered adenosine triphosphate (ATP) levels and contributed little to ATP maintenance when added to SAGM. Both monosaccharides form the advanced glycation end product glycerate. Mannose activates enzymes in the RBC that take part in glycan synthesis. CONCLUSIONS: Fructose or mannose addition to RBC SAGM concentrates may not offset the shift in metabolism of RBCs that occurs after 10 days of storage. Fructose and mannose metabolism at 4°C in SAGM reflects their metabolism at physiologic temperature. Glycerate excretion is a measure of protein deglycosylation activity in stored RBCs. No cytoprotective effect was observed upon the addition of either fructose or mannose to SAGM.

Metabolic fate of adenine in red blood cells during storage in SAGM solution.

BACKGROUND: Red blood cells (RBCs) are routinely stored and transfused worldwide. Recently, metabolomics have shown that RBCs experience a three-phase metabolic decay process during storage, resulting in the definition of three distinct metabolic phenotypes, occurring between Days 1 and 10, 11 and 17, and 18 and 46. Here we use metabolomics and stable isotope labeling analysis to study adenine metabolism in RBCs. STUDY DESIGN AND METHODS: A total of 6 units were prepared in SAGM or modified additive solutions (ASs) containing 15 N5 -adenine. Three of them were spiked with 15 N5 -adenine on Days 10, 14, and 17 during storage. Each unit was sampled 10 times spanning Day 1 to Day 32. At each time point metabolic profiling was performed. RESULTS: We increased adenine concentration in the AS and we pulsed the adenine concentration during storage and found that in both cases the RBCs' main metabolic pathways were not affected. Our data clearly show that RBCs cannot consume adenine after 18 days of storage, even if it is still present in the storage solution. However, increased levels of adenine influenced S-adenosylmethionine metabolism. CONCLUSION: In this work, we have studied in detail the metabolic fate of adenine during RBC storage in SAGM. Adenine is one of the main substrates used by RBCs, but the metabolic shift observed during storage is not caused by an absence of adenine later in storage. The rate of adenine consumption strongly correlated with duration of storage but not with the amount of adenine present in the AS.

Biomarkers defining the metabolic age of red blood cells during cold storage.

Metabolomic investigations of packed red blood cells (RBCs) stored under refrigerated conditions in saline adenine glucose mannitol (SAGM) additives have revealed the presence of 3 distinct metabolic phases, occurring on days 0-10, 10-18, and after day 18 of storage. Here we used receiving operating characteristics curve analysis to identify biomarkers that can differentiate between the 3 metabolic states. We first recruited 24 donors and analyzed 308 samples coming from RBC concentrates stored in SAGM and additive solution 3. We found that 8 extracellular compounds (lactic acid, nicotinamide, 5-oxoproline, xanthine, hypoxanthine, glucose, malic acid, and adenine) form the basis for an accurate classification/regression model and are able to differentiate among the metabolic phases. This model was then validated by analyzing an additional 49 samples obtained by preparing 7 new RBC concentrates in SAGM. Despite the technical variability associated with RBC processing strategies, verification of these markers was independently confirmed in 2 separate laboratories with different analytical setups and different sample sets. The 8 compounds proposed here highly correlate with the metabolic age of packed RBCs, and can be prospectively validated as biomarkers of the RBC metabolic lesion.

Understanding the Causes and Implications of Endothelial Metabolic Variation in Cardiovascular Disease through Genome-Scale Metabolic Modeling.

High-throughput biochemical profiling has led to a requirement for advanced data interpretation techniques capable of integrating the analysis of gene, protein, and metabolic profiles to shed light on genotype-phenotype relationships. Herein, we consider the current state of knowledge of endothelial cell (EC) metabolism and its connections to cardiovascular disease (CVD) and explore the use of genome-scale metabolic models (GEMs) for integrating metabolic and genomic data. GEMs combine gene expression and metabolic data acting as frameworks for their analysis and, ultimately, afford mechanistic understanding of how genetic variation impacts metabolism. We demonstrate how GEMs can be used to investigate CVD-related genetic variation, drug resistance mechanisms, and novel metabolic pathways in ECs. The application of GEMs in personalized medicine is also highlighted. Particularly, we focus on the potential of GEMs to identify metabolic biomarkers of endothelial dysfunction and to discover methods of stratifying treatments for CVDs based on individual genetic markers. Recent advances in systems biology methodology, and how these methodologies can be applied to understand EC metabolism in both health and disease, are thus highlighted.

Identified metabolic signature for assessing red blood cell unit quality is associated with endothelial damage markers and clinical outcomes.

BACKGROUND: There has been interest in determining whether older red blood cell (RBC) units have negative clinical effects. Numerous observational studies have shown that older RBC units are an independent factor for patient mortality. However, recently published randomized clinical trials have shown no difference of clinical outcome for patients receiving old or fresh RBCs. An overlooked but essential issue in assessing RBC unit quality and ultimately designing the necessary clinical trials is a metric for what constitutes an old or fresh RBC unit. STUDY DESIGN AND METHODS: Twenty RBC units were profiled using quantitative metabolomics over 42 days of storage in SAGM with 3- to 4-day time intervals. Metabolic pathway usage during storage was assessed using systems biology methods. The detected time intervals of the metabolic states were compared to clinical outcomes. RESULTS: Using multivariate statistics, we identified a nonlinear decay process exhibiting three distinct metabolic states (Days 0-10, 10-17, and 17-42). Hematologic variables traditionally measured in the transfusion setting (e.g., pH, hemolysis, RBC indices) did not distinguish these three states. Systemic changes in pathway usage occurred between the three states, with key pathways changing in both magnitude and direction. Finally, an association was found between the time periods of the metabolic states with the clinical outcomes of more than 280,000 patients in the country of Denmark transfused over the past 15 years and endothelial damage markers in healthy volunteers undergoing autologous transfusions. CONCLUSION: The state of RBC metabolism may be a better indicator of cellular quality than traditional hematologic variables.

The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models.

BACKGROUND: The complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach. RESULTS: A dynamic simulator, the Fully-integrated Immune Response Model (FIRM), was built in a stepwise fashion by integrating published subset models and adding novel features. The approach used to build the model includes the formulation of the network of interacting species and the subsequent introduction of rate laws to describe each biological process. The resulting model represents a multi-organ structure, comprised of the target organ where the immune response takes place, circulating blood, lymphoid T, and lymphoid B tissue. The cell types accounted for include macrophages, a few T-cell lineages (cytotoxic, regulatory, helper 1, and helper 2), and B-cell activation to plasma cells. Four different cytokines were accounted for: IFN-γ, IL-4, IL-10 and IL-12. In addition, generic inflammatory signals are used to represent the kinetics of IL-1, IL-2, and TGF-ß. Cell recruitment, differentiation, replication, apoptosis and migration are described as appropriate for the different cell types. The model is a hybrid structure containing information from several mammalian species. The structure of the network was built to be physiologically and biochemically consistent. Rate laws for all the cellular fate processes, growth factor production rates and half-lives, together with antibody production rates and half-lives, are provided. The results demonstrate how this framework can be used to integrate mathematical models of the immune response from several published sources and describe qualitative predictions of global immune system response arising from the integrated, hybrid model. In addition, we show how the model can be expanded to include novel biological findings. Case studies were carried out to simulate TB infection, tumor rejection, response to a blood borne pathogen and the consequences of accounting for regulatory T-cells. CONCLUSIONS: The final result of this work is a postulated and increasingly comprehensive representation of the mammalian immune system, based on physiological knowledge and susceptible to further experimental testing and validation. We believe that the integrated nature of FIRM has the potential to simulate a range of responses under a variety of conditions, from modeling of immune responses after tuberculosis (TB) infection to tumor formation in tissues. FIRM also has the flexibility to be expanded to include both complex and novel immunological response features as our knowledge of the immune system advances.