High-quality genome-scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities.

Genome-scale reconstructions of metabolism are computational species-specific knowledge bases able to compute systemic metabolic properties. We present a comprehensive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440 that greatly expands computable predictions of its metabolic states. The reconstruction represents a significant reactome expansion over available reconstructed bacterial metabolic networks. Specifically, iJN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock-out library and Bar-seq data; and (iv) allowed mapping of its network to 82 P. putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin. Thus, this study provides a thoroughly updated metabolic reconstruction and new computable phenotypes for P. putida, which can be leveraged as a first step toward understanding the pan metabolic capabilities of Pseudomonas.

A metabolic reconstruction of Lactobacillus reuteri JCM 1112 and analysis of its potential as a cell factory.

BACKGROUND: Lactobacillus reuteri is a heterofermentative Lactic Acid Bacterium (LAB) that is commonly used for food fermentations and probiotic purposes. Due to its robust properties, it is also increasingly considered for use as a cell factory. It produces several industrially important compounds such as 1,3-propanediol and reuterin natively, but for cell factory purposes, developing improved strategies for engineering and fermentation optimization is crucial. Genome-scale metabolic models can be highly beneficial in guiding rational metabolic engineering. Reconstructing a reliable and a quantitatively accurate metabolic model requires extensive manual curation and incorporation of experimental data. RESULTS: A genome-scale metabolic model of L. reuteri JCM 1112T was reconstructed and the resulting model, Lreuteri_530, was validated and tested with experimental data. Several knowledge gaps in the metabolism were identified and resolved during this process, including presence/absence of glycolytic genes. Flux distribution between the two glycolytic pathways, the phosphoketolase and Embden-Meyerhof-Parnas pathways, varies considerably between LAB species and strains. As these pathways result in different energy yields, it is important to include strain-specific utilization of these pathways in the model. We determined experimentally that the Embden-Meyerhof-Parnas pathway carried at most 7% of the total glycolytic flux. Predicted growth rates from Lreuteri_530 were in good agreement with experimentally determined values. To further validate the prediction accuracy of Lreuteri_530, the predicted effects of glycerol addition and adhE gene knock-out, which results in impaired ethanol production, were compared to in vivo data. Examination of both growth rates and uptake- and secretion rates of the main metabolites in central metabolism demonstrated that the model was able to accurately predict the experimentally observed effects. Lastly, the potential of L. reuteri as a cell factory was investigated, resulting in a number of general metabolic engineering strategies. CONCLUSION: We have constructed a manually curated genome-scale metabolic model of L. reuteri JCM 1112T that has been experimentally parameterized and validated and can accurately predict metabolic behavior of this important platform cell factory.

Systems analysis of metabolism in platelet concentrates during storage in platelet additive solution.

Platelets (PLTs) deteriorate over time when stored within blood banks through a biological process known as PLT storage lesion (PSL). Here, we describe the refinement of the biochemical model of PLT metabolism, iAT-PLT-636, and its application to describe and investigate changes in metabolism during PLT storage. Changes in extracellular acetate and citrate were measured in buffy coat and apheresis PLT units over 10 days of storage in the PLT additive solution T-Sol. Metabolic network analysis of these data was performed alongside our prior metabolomics data to describe the metabolism of fresh (days 1-3), intermediate (days 4-6), and expired (days 7-10) PLTs. Changes in metabolism were studied by comparing metabolic model flux predictions of iAT-PLT-636 between stages and between collection methods. Extracellular acetate and glucose contribute most to central carbon metabolism in PLTs. The anticoagulant citrate is metabolized in apheresis-stored PLTs and is converted into aconitate and, to a lesser degree, malate. The consumption of nutrients changes during storage and reflects altered PLT activation profiles following their collection. Irrespective of the collection method, a slowdown in oxidative phosphorylation takes place, consistent with mitochondrial dysfunction during PSL. Finally, the main contributors to intracellular ammonium and NADPH are highlighted. Future optimization of flux through these pathways provides opportunities to address intracellular pH changes and reactive oxygen species, which are both of importance to PSL. The metabolic models provide descriptions of PLT metabolism at steady state and represent a platform for future PLT metabolic research.

EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend.

Responses of Synechocystis sp. PCC 6803 to heterologous biosynthetic pathways.

BACKGROUND: There are an increasing number of studies regarding genetic manipulation of cyanobacteria to produce commercially interesting compounds. The majority of these works study the expression and optimization of a selected heterologous pathway, largely ignoring the wholeness and complexity of cellular metabolism. Regulation and response mechanisms are largely unknown, and even the metabolic pathways themselves are not fully elucidated. This poses a clear limitation in exploiting the rich biosynthetic potential of cyanobacteria. RESULTS: In this work, we focused on the production of two different compounds, the cyanogenic glucoside dhurrin and the diterpenoid 13R-manoyl oxide in Synechocystis PCC 6803. We used genome-scale metabolic modelling to study fluxes in individual reactions and pathways, and we determined the concentrations of key metabolites, such as amino acids, carotenoids, and chlorophylls. This allowed us to identify metabolic crosstalk between the native and the introduced metabolic pathways. Most results and simulations highlight the metabolic robustness of cyanobacteria, suggesting that the host organism tends to keep metabolic fluxes and metabolite concentrations steady, counteracting the effects of the heterologous pathway. However, the amino acid concentrations of the dhurrin-producing strain show an unexpected profile, where the perturbation levels were high in seemingly unrelated metabolites. CONCLUSIONS: There is a wealth of information that can be derived by combining targeted metabolite identification and computer modelling as a frame of understanding. Here we present an example of how strain engineering approaches can be coupled to 'traditional' metabolic engineering with systems biology, resulting in novel and more efficient manipulation strategies.

Multidimensional analytical approach based on UHPLC-UV-ion mobility-MS for the screening of natural pigments.

Here, we propose a novel strategy that combines a typical ultra high performance liquid chromatography (UHPLC), data-independent mass spectrometry (MS(E)) workflow with traveling wave ion mobility (TWIM) and UV detection, to improve the characterization of carotenoids and chlorophylls in complex biological matrices. UV detection selectively highlighted pigments absorbing at specific wavelengths, while TWIM coupled to MS was used to maximize the peak capacity. We applied this approach for the analysis of pigments in different microalgae samples, including Chlorella vulgaris, Dunaliella salina, and Phaeodactylum tricornutum. Using UHPLC-UV-MS(E) information (retention time, absorbance at 450 nm, and accurate masses of precursors and product ions), we tentatively identified 26 different pigments (carotenes, chlorophylls, and xanthophylls). By adding TWIM information (collision cross sections), we further resolved 5 isobaric pigments, not resolved by UHPLC-UV-MS(E) alone. The characterization of the molecular phenotypes allowed us to differentiate the microalgae species. Our results demonstrate that a combination of TWIM and UV detection with traditional analytical approaches increases the selectivity and specificity of analysis, providing a new tool to characterize pigments in biological samples. We anticipate that such an analytical approach will be extended to other lipidomics and metabolomics applications.

Detailing the optimality of photosynthesis in cyanobacteria through systems biology analysis.

Photosynthesis has recently gained considerable attention for its potential role in the development of renewable energy sources. Optimizing photosynthetic organisms for biomass or biofuel production will therefore require a systems understanding of photosynthetic processes. We reconstructed a high-quality genome-scale metabolic network for Synechocystis sp. PCC6803 that describes key photosynthetic processes in mechanistic detail. We performed an exhaustive in silico analysis of the reconstructed photosynthetic process under different light and inorganic carbon (Ci) conditions as well as under genetic perturbations. Our key results include the following. (i) We identified two main states of the photosynthetic apparatus: a Ci-limited state and a light-limited state. (ii) We discovered nine alternative electron flow pathways that assist the photosynthetic linear electron flow in optimizing the photosynthesis performance. (iii) A high degree of cooperativity between alternative pathways was found to be critical for optimal autotrophic metabolism. Although pathways with high photosynthetic yield exist for optimizing growth under suboptimal light conditions, pathways with low photosynthetic yield guarantee optimal growth under excessive light or Ci limitation. (iv) Photorespiration was found to be essential for the optimal photosynthetic process, clarifying its role in high-light acclimation. Finally, (v) an extremely high photosynthetic robustness drives the optimal autotrophic metabolism at the expense of metabolic versatility and robustness. The results and modeling approach presented here may promote a better understanding of the photosynthetic process. They can also guide bioengineering projects toward optimal biofuel production in photosynthetic organisms.

Effects of abiotic stressors on lutein production in the green microalga Dunaliella salina.

BACKGROUND: Recent years have witnessed a rising trend in exploring microalgae for valuable carotenoid products as the demand for lutein and many other carotenoids in global markets has increased significantly. In green microalgae lutein is a major carotenoid protecting cellular components from damage incurred by reactive oxygen species under stress conditions. In this study, we investigated the effects of abiotic stressors on lutein accumulation in a strain of the marine microalga D. salina which had been selected for growth under stress conditions of combined blue and red lights by adaptive laboratory evolution. RESULTS: Nitrate concentration, salinity and light quality were selected as three representative influencing factors and their impact on lutein production in batch cultures of D. salina was evaluated using response surface analysis. D. salina was found to be more tolerant to hyper-osmotic stress than to hypo-osmotic stress which caused serious cell damage and death in a high proportion of cells while hyper-osmotic stress increased the average cell size of D. salina only slightly. Two models were developed to explain how lutein productivity depends on the stress factors and for predicting the optimal conditions for lutein productivity. Among the three stress variables for lutein production, stronger interactions were found between nitrate concentration and salinity than between light quality and the other two. The predicted optimal conditions for lutein production were close to the original conditions used for adaptive evolution of D. salina. This suggests that the conditions imposed during adaptive evolution may have selected for the growth optima arrived at. CONCLUSIONS: This study shows that systematic evaluation of the relationship between abiotic environmental stresses and lutein biosynthesis can help to decipher the key parameters in obtaining high levels of lutein productivity in D. salina. This study may benefit future stress-driven adaptive laboratory evolution experiments and a strategy of applying stress in a step-wise manner can be suggested for a rational design of experiments.

Temperature Dependence of Platelet Metabolism.

Temperature plays a fundamental role in biology, influencing cellular function, chemical reaction rates, molecular structures, and interactions. While the temperature dependence of many biochemical reactions is well defined in vitro, the effect of temperature on metabolic function at the network level is poorly understood, and it remains an important challenge in optimizing the storage of cells and tissues at lower temperatures. Here, we used time-course metabolomic data and systems biology approaches to characterize the effects of storage temperature on human platelets (PLTs) in a platelet additive solution. We observed that changes to the metabolome with storage time do not simply scale with temperature but instead display complex temperature dependence, with only a small subset of metabolites following an Arrhenius-type relationship. Investigation of PLT energy metabolism through integration with computational modeling revealed that oxidative metabolism is more sensitive to temperature changes than glycolysis. The increased contribution of glycolysis to ATP turnover at lower temperatures indicates a stronger glycolytic phenotype with decreasing storage temperature. More broadly, these results demonstrate that the temperature dependence of the PLT metabolic network is not uniform, suggesting that efforts to improve the health of stored PLTs could be targeted at specific pathways.

Ensemble Learning Using Individual Neonatal Data for Seizure Detection.

OBJECTIVE: Sharing medical data between institutions is difficult in practice due to data protection laws and official procedures within institutions. Therefore, most existing algorithms are trained on relatively small electroencephalogram (EEG) data sets which is likely to be detrimental to prediction accuracy. In this work, we simulate a case when the data can not be shared by splitting the publicly available data set into disjoint sets representing data in individual institutions. METHODS AND PROCEDURES: We propose to train a (local) detector in each institution and aggregate their individual predictions into one final prediction. Four aggregation schemes are compared, namely, the majority vote, the mean, the weighted mean and the Dawid-Skene method. The method was validated on an independent data set using only a subset of EEG channels. RESULTS: The ensemble reaches accuracy comparable to a single detector trained on all the data when sufficient amount of data is available in each institution. CONCLUSION: The weighted mean aggregation scheme showed best performance, it was only marginally outperformed by the Dawid-Skene method when local detectors approach performance of a single detector trained on all available data. CLINICAL IMPACT: Ensemble learning allows training of reliable algorithms for neonatal EEG analysis without a need to share the potentially sensitive EEG data between institutions.

Argininosuccinate lyase is a metabolic vulnerability in breast development and cancer.

Epithelial-to-mesenchymal transition (EMT) is fundamental to both normal tissue development and cancer progression. We hypothesized that EMT plasticity defines a range of metabolic phenotypes and that individual breast epithelial metabolic phenotypes are likely to fall within this phenotypic landscape. To determine EMT metabolic phenotypes, the metabolism of EMT was described within genome-scale metabolic models (GSMMs) using either transcriptomic or proteomic data from the breast epithelial EMT cell culture model D492. The ability of the different data types to describe breast epithelial metabolism was assessed using constraint-based modeling which was subsequently verified using 13C isotope tracer analysis. The application of proteomic data to GSMMs provided relatively higher accuracy in flux predictions compared to the transcriptomic data. Furthermore, the proteomic GSMMs predicted altered cholesterol metabolism and increased dependency on argininosuccinate lyase (ASL) following EMT which were confirmed in vitro using drug assays and siRNA knockdown experiments. The successful verification of the proteomic GSMMs afforded iBreast2886, a breast GSMM that encompasses the metabolic plasticity of EMT as defined by the D492 EMT cell culture model. Analysis of breast tumor proteomic data using iBreast2886 identified vulnerabilities within arginine metabolism that allowed prognostic discrimination of breast cancer patients on a subtype-specific level. Taken together, we demonstrate that the metabolic reconstruction iBreast2886 formalizes the metabolism of breast epithelial cell development and can be utilized as a tool for the functional interpretation of high throughput clinical data.

Computationally efficient flux variability analysis.

BACKGROUND: Flux variability analysis is often used to determine robustness of metabolic models in various simulation conditions. However, its use has been somehow limited by the long computation time compared to other constraint-based modeling methods. RESULTS: We present an open source implementation of flux variability analysis called fastFVA. This efficient implementation makes large-scale flux variability analysis feasible and tractable allowing more complex biological questions regarding network flexibility and robustness to be addressed. CONCLUSIONS: Networks involving thousands of biochemical reactions can be analyzed within seconds, greatly expanding the utility of flux variability analysis in systems biology.

Engineering the carotenoid biosynthetic pathway in Rhodothermus marinus for lycopene production.

Rhodothermus marinus has the potential to be well suited for biorefineries, as an aerobic thermophile that produces thermostable enzymes and is able to utilize polysaccharides from different 2nd and 3rd generation biomass. The bacterium produces valuable chemicals such as carotenoids. However, the native carotenoids are not established for industrial production and R. marinus needs to be genetically modified to produce higher value carotenoids. Here we genetically modified the carotenoid biosynthetic gene cluster resulting in three different mutants, most importantly the lycopene producing mutant TK-3 (ΔtrpBΔpurAΔcruFcrtB::trpBcrtB T.thermophilus ). The genetic modifications and subsequent structural analysis of carotenoids helped clarify the carotenoid biosynthetic pathway in R. marinus. The nucleotide sequences encoding the enzymes phytoene synthase (CrtB) and the previously unidentified 1',2'-hydratase (CruF) were found fused together and encoded by a single gene in R. marinus. Deleting only the cruF part of the gene did not result in an active CrtB enzyme. However, by deleting the entire gene and inserting the crtB gene from Thermus thermophilus, a mutant strain was obtained, producing lycopene as the sole carotenoid. The lycopene produced by TK-3 was quantified as 0.49 â€‹g/kg CDW (cell dry weight).

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.

Characterization of carotenoids in Rhodothermus marinus.

Rhodothermus marinus, a marine aerobic thermophile, was first isolated from an intertidal hot spring in Iceland. In recent years, the R. marinus strain PRI 493 has been genetically modified, which opens up possibilities for targeted metabolic engineering of the species, such as of the carotenoid biosynthetic pathway. In this study, the carotenoids of the R. marinus type-strain DSM 4252T , strain DSM 4253, and strain PRI 493 were characterized. Bioreactor cultivations were used for pressurized liquid extraction and analyzed by ultra-high performance supercritical fluid chromatography with diode array and quadropole time-of-flight mass spectrometry detection (UHPSFC-DAD-QTOF/MS). Salinixanthin, a carotenoid originally found in Salinibacter ruber and previously detected in strain DSM 4253, was identified in all three R. marinus strains, both in the hydroxylated and nonhydroxylated form. Furthermore, an additional and structurally distinct carotenoid was detected in the three strains. MS/MS fragmentation implied that the mass difference between salinixanthin and the novel carotenoid structure corresponded to the absence of a 4-keto group on the ß-ionone ring. The study confirmed the lack of carotenoids for the strain SB-71 (ΔtrpBΔpurAcrtBI'::trpB) in which genes encoding two enzymes of the proposed pathway are partially deleted. Moreover, antioxidant capacity was detected in extracts of all the examined R. marinus strains and found to be 2-4 times lower for the knock-out strain SB-71. A gene cluster with 11 genes in two operons in the R. marinusDSM 4252T genome was identified and analyzed, in which several genes were matched with carotenoid biosynthetic pathway genes in other organisms.

Reliability of quantitative EEG features.

OBJECTIVE: To investigate the reliability of several well-known quantitative EEG (qEEG) features in the elderly in the resting, eyes closed condition and study the effects of epoch length and channel derivations on reliability. METHODS: Fifteen healthy adults, over 50 years of age, underwent 10 EEG recordings over a 2-month period. Various qEEG features derived from power spectral, coherence, entropy and complexity analysis of the EEG were computed. Reliability was quantified using an intraclass correlation coefficient. RESULTS: The highest reliability was obtained with the average montage, reliability increased with epoch length up to 40s, longer epochs gave only marginal improvement. The reliability of the qEEG features was highest for power spectral parameters, followed by regularity measures based on entropy and complexity, coherence being least reliable. CONCLUSIONS: Montage and epoch length had considerable effects on reliability. Several apparently unrelated regularity measures had similar stability. Reliability of coherence measures was strongly dependent on channel location and frequency bands. SIGNIFICANCE: The reliability of regularity measures has until now received limited attention. Low reliability of coherence measures in general may limit their usefulness in the clinical setting.

Metabolic re-wiring of isogenic breast epithelial cell lines following epithelial to mesenchymal transition.

Epithelial to mesenchymal transition (EMT) has implications in tumor progression and metastasis. Metabolic alterations have been described in cancer development but studies focused on the metabolic re-wiring that takes place during EMT are still limited. We performed metabolomics profiling of a breast epithelial cell line and its EMT derived mesenchymal phenotype to create genome-scale metabolic models descriptive of both cell lines. Glycolysis and OXPHOS were higher in the epithelial phenotype while amino acid anaplerosis and fatty acid oxidation fueled the mesenchymal phenotype. Through comparative bioinformatics analysis, PPAR-γ1, PPAR- γ2 and AP-1 were found to be the most influential transcription factors associated with metabolic re-wiring. In silico gene essentiality analysis predicts that the LAT1 neutral amino acid transporter is essential for mesenchymal cell survival. Our results define metabolic traits that distinguish an EMT derived mesenchymal cell line from its epithelial progenitor and may have implications in cancer progression and metastasis. Furthermore, the tools presented here can aid in identifying critical metabolic nodes that may serve as therapeutic targets aiming to prevent EMT and inhibit metastatic dissemination.

Cyanobacteria as photosynthetic biocatalysts: a systems biology perspective.

The increasing need to replace oil-based products and to address global climate change concerns has triggered considerable interest in photosynthetic microorganisms. Cyanobacteria, in particular, have great potential as biocatalysts for fuels and fine-chemicals. During the last few years the biotechnological applications of cyanobacteria have experienced an unprecedented increase and the use of these photosynthetic organisms for chemical production is becoming a tangible reality. However, the field is still immature and many concerns about the economic feasibility of the biotechnological potential of cyanobacteria remain. In this review we describe recent successes in biofuel and fine-chemical production using cyanobacteria. We discuss the role of the photosynthetic metabolism and highlight the need for systems-level metabolic optimization in order to achieve the true potential of cyanobacterial biocatalysts.

Kinetic analysis of gluconate phosphorylation by human gluconokinase using isothermal titration calorimetry.

Gluconate is a commonly encountered nutrient, which is degraded by the enzyme gluconokinase to generate 6-phosphogluconate. Here we used isothermal titration calorimetry to study the properties of this reaction. ΔH, KM and kcat are reported along with substrate binding data. We propose that the reaction follows a ternary complex mechanism, with ATP binding first. The reaction is inhibited by gluconate, as it binds to an Enzyme-ADP complex forming a dead-end complex. The study exemplifies that ITC can be used to determine mechanisms of enzyme catalyzed reactions, for which it is currently not commonly applied.