Prediction of intracellular metabolic states from extracellular metabolomic data

Physiologic relevance of the transpulmonary metabolome in connective tissue disease-associated pulmonary vascular disease

Pathologic implications of dysregulated pulmonary vascular metabolism to pulmonary arterial hypertension (PAH) are increasingly recognized, but their clinical applications have been limited. We hypothesized that metabolite quantification across the pulmonary vascular bed in connective tissue disease-associated (CTD-associated) PAH would identify transpulmonary gradients of pathobiologically relevant metabolites, in an exercise stage-specific manner. Sixty-three CTD patients with established or suspected PAH underwent exercise right heart catheterization. Using mass spectrometry-based metabolomics, metabolites were quantified in plasma samples simultaneously collected from the pulmonary and radial arteries at baseline and during resistance-free wheeling, peak exercise, and recovery. We identified uptake and excretion of metabolites across the pulmonary vascular bed, unique and distinct from single vascular site analysis. We demonstrated the physiological relevance of metabolites previously shown to promote disease in animal models and end-stage human lung tissues, including acylcarnitines, glycolytic intermediates, and tryptophan catabolites. Notably, pulmonary vascular metabolite handling was exercise stage specific. Transpulmonary metabolite gradients correlated with hemodynamic endpoints largely during free-wheeling. Glycolytic intermediates demonstrated physiologic significance at peak exercise, including net uptake of lactate in those with more advanced disease. Contribution of pulmonary vascular metabolism to CTD-PAH pathogenesis and therapeutic candidacy of metabolism modulation must be considered in the context of physiologic stress.

Monitoring cellular glycolysis pathway kinetics in the extracellular medium using label-free, Raman spectroscopy

This study explored the potential of Raman spectroscopy to holistically monitor the glycolysis pathway kinetics as a function of time through the extracellular medium. Initially, the collinearity of individual metabolites of interest- glucose and lactic acid as a function of their concentration was tested followed by the sensitivity analysis of the approach by elucidating the limits of detection (0.85 mM and 2.8 mM) and quantification (2.5 mM and 9.5 mM) for glucose and lactic acid respectively in the biological range. In the process several datamining approaches were also explored. Finally, the A549 cell culture was used for kinetic spectral acquisition of the extracellular medium mimicking the kinetic glycolysis assay as a function of time under different pathway modulations. The spectra were resolved and fitted with a kinetically constrained-model (A → B → C) using the multivariate curve resolution- alternating least squares tool for all the modulated conditions to elucidate the pathway kinetics and the rate of change. The rate of change of the resolved components for the stimulated condition (k1: 0.005 min-1, k2: 0.011 min-1) was approximately twice as that of the control (k1: 0,045 min-1; k2: 0.049 min-1) while the inhibited condition (k1: 0.025 min-1, k2: 0.017 min-1) was substantially slower. The technique is superior to the targeted current gold standard kinetic assay approach, in that it is holistic in nature and has potential applications in drug discovery, bioprocessing, disease diagnostics, etc. Furthermore, this approach overcomes the limitations of the omics/multiomics approaches, limited to a snapshot of cellular metabolism. This study serves as a guideline for future, more complex subcellular kinetic spectroscopy experiments.

β-cyclocitral induced rapid cell death of Microcystis aeruginosa

β-cyclocitral (BCC) is an odorous compound that can be produced by bloom-forming cyanobacteria, for example, Microcystis aeruginosa. BCC has been proposed to explain the rapid decline of cyanobacterial blooms in natural water bodies due to its lytic effects on cyanobacteria cells. However, few insights have been gained regarding the mechanisms of its lethality on cyanobacteria. In this study, M. aeruginosa was exposed to 0-300 mg/L BCC, and the physiological responses were comprehensively studied at the cellular, molecular, and transcriptomic levels. The result indicated that the lethal effect was concentration-dependent; 100 mg/L BCC only caused recoverable stress, while 150-300 mg/L BCC caused rapid rupture of cyanobacterial cells. Scanning electron microscope images suggested two typical morphological changes exposed to above 150 mg/LBCC: wrinkled/shrank with limited holes on the surface at 150 and 200 mg/L BCC exposure; no apparent shrinkage at the surface but with cell perforation at 250 and 300 mg/L BCC exposure. BCC can rapidly inhibit the photosynthetic activity of M. aeruginosa cells (40%∼100% decreases for 100-300 mg/L BCC) and significantly down-regulate photosynthetic system Ⅰ-related genes. Also, chlorophyll a (by 30%∼90%) and ATP (by ∼80%) contents severely decreased, suggesting overwhelming pressure on the energy metabolism in cells. Glutathione levels increased significantly, and stress response-related genes were upregulated, indicating the perturbation of intracellular redox homeostasis. Two cell death pathways were proposed to explain the lethal effect: apoptosis-like death as revealed by the upregulation of SOS response genes when exposed to 200 mg/L BCC and mazEF-mediated death as revealed by the upregulation of mazEF system genes when exposed to 300 mg/L BCC. Results of the current work not only provide insights into the potential role of BCC in inducing programmed cell death during bloom demise but also indicate the potential of using BCC for harmful algal control.

Thyroid cancer cell metabolism: A glance into cell culture system-based metabolomics approaches

Thyroid cancer is the most common malignancy of the endocrine system and the seventh most prevalent cancer in women worldwide. It is a complex and diverse disease characterized by heterogeneity, underscoring the importance of understanding the underlying metabolic alterations within tumor cells. Metabolomics technologies offer a powerful toolset to explore and identify endogenous and exogenous biochemical reaction products, providing crucial insights into the intricate metabolic pathways and processes within living cells. Metabolism plays a central role in cell function, making metabolomics a valuable reflection of a cell's phenotype. In the OMICs era, metabolomics analysis of cells brings numerous advantages over existing methods, propelling cell metabolomics as an emerging field with vast potential for investigating metabolic pathways and their perturbation in pathophysiological conditions. This review article aims to look into recent developments in applying metabolomics for characterizing and interpreting the cellular metabolome in thyroid cancer cell lines, exploring their unique metabolic characteristics. Understanding the metabolic alterations in tumor cells can lead to the identification of critical nodes in the metabolic network that could be targeted for therapeutic intervention.

Microfluidics enabled multi-omics triple-shot mass spectrometry for cell-based therapies

In recent years, cell-based therapies have transformed medical treatment. These therapies present a multitude of challenges associated with identifying the mechanism of action, developing accurate safety and potency assays, and achieving low-cost product manufacturing at scale. The complexity of the problem can be attributed to the intricate composition of the therapeutic products: living cells with complex biochemical compositions. Identifying and measuring critical quality attributes (CQAs) that impact therapy success is crucial for both the therapy development and its manufacturing. Unfortunately, current analytical methods and tools for identifying and measuring CQAs are limited in both scope and speed. This Perspective explores the potential for microfluidic-enabled mass spectrometry (MS) systems to comprehensively characterize CQAs for cell-based therapies, focusing on secretome, intracellular metabolome, and surfaceome biomarkers. Powerful microfluidic sampling and processing platforms have been recently presented for the secretome and intracellular metabolome, which could be implemented with MS for fast, locally sampled screening of the cell culture. However, surfaceome analysis remains limited by the lack of rapid isolation and enrichment methods. Developing innovative microfluidic approaches for surface marker analysis and integrating them with secretome and metabolome measurements using a common analytical platform hold the promise of enhancing our understanding of CQAs across all "omes," potentially revolutionizing cell-based therapy development and manufacturing for improved efficacy and patient accessibility.

The media composition as a crucial element in high-throughput metabolic network reconstruction

In recent years, metagenome-assembled genomes (MAGs) have provided glimpses into the intra- and interspecies genetic diversity and interactions that form the bases of complex microbial communities. High-throughput reconstruction of genome-scale metabolic networks (GEMs) from MAGs is a promising avenue to disentangle the myriad trophic interactions stabilizing these communities. However, high-throughput reconstruction of GEMs relies on accurate gap filling of metabolic pathways using automated algorithms. Here, we systematically explore how the composition of the media (specification of the available nutrients and metabolites) during gap filling influences the resulting GEMs concerning predicted auxotrophies for fully sequenced model organisms and environmental isolates. We expand this analysis by using 106 MAGs from the same species with differing quality. We find that although the completeness of MAGs influences the fraction of gap-filled reactions, the composition of the media plays the dominant role in the accurate prediction of auxotrophies that form the basis of myriad community interactions. We propose that constraining the media composition for gap filling through both experimental approaches and computational approaches will increase the reliability of high-throughput reconstruction of genome-scale metabolic models from MAGs and paves the way for culture independent prediction of trophic interactions in complex microbial communities.

Sample Preparation in Microbial Metabolomics: Advances and Challenges

Microbial metabolomics has gained significant interest as it reflects the physiological state of microorganisms. Due to the great variability of biological organisms, in terms of physicochemical characteristics and variable range of concentration of metabolites, the choice of sample preparation methods is a crucial step in the metabolomics workflow and will reflect on the quality and reliability of the results generated. The procedures applied to the preparation of microbial samples will vary according to the type of microorganism studied, the metabolomics approach (untargeted or targeted), and the analytical platform of choice. This chapter aims to provide an overview of the sample preparation workflow for microbial metabolomics, highlighting the pre-analytical factors associated with cultivation, harvesting, metabolic quenching, and extraction. Discussions focus on obtaining intracellular and extracellular metabolites. Finally, we introduced advanced sample preparation methods based on automated systems.

Multi-OMICs landscape of SARS-CoV-2-induced host responses in human lung epithelial cells

COVID-19 pandemic continues to remain a global health concern owing to the emergence of newer variants. Several multi-Omics studies have produced extensive evidence on host-pathogen interactions and potential therapeutic targets. Nonetheless, an increased understanding of host signaling networks regulated by post-translational modifications and their ensuing effect on the cellular dynamics is critical to expanding the current knowledge on SARS-CoV-2 infections. Through an unbiased transcriptomics, proteomics, acetylomics, phosphoproteomics, and exometabolome analysis of a lung-derived human cell line, we show that SARS-CoV-2 Norway/Trondheim-S15 strain induces time-dependent alterations in the induction of type I IFN response, activation of DNA damage response, dysregulated Hippo signaling, among others. We identified interplay of phosphorylation and acetylation dynamics on host proteins and its effect on the altered release of metabolites, especially organic acids and ketone bodies. Together, our findings serve as a resource of potential targets that can aid in designing novel host-directed therapeutic strategies.

Metabolic systems analysis identifies a novel mechanism contributing to shock in patients with endotheliopathy of trauma (EoT) involving thromboxane A2 and LTC4

Purpose

Endotheliopathy of trauma (EoT), as defined by circulating levels of syndecan-1 ≥ 40 ng/mL, has been reported to be associated with significantly increased transfusion requirements and a doubled 30-day mortality. Increased shedding of the glycocalyx points toward the endothelial cell membrane composition as important for the clinical outcome being the rationale for this study.

Results

The plasma metabolome of 95 severely injured trauma patients was investigated by mass spectrometry, and patients with EoT vs. non-EoT were compared by partial least square-discriminant analysis, identifying succinic acid as the top metabolite to differentiate EoT and non-EoT patients (VIP score = 3). EoT and non-EoT patients' metabolic flux profile was inferred by integrating the corresponding plasma metabolome data into a genome-scale metabolic network reconstruction analysis and performing a functional study of the metabolic capabilities of each group. Model predictions showed a decrease in cholesterol metabolism secondary to impaired mevalonate synthesis in EoT compared to non-EoT patients. Intracellular task analysis indicated decreased synthesis of thromboxanA2 and leukotrienes, as well as a lower carnitine palmitoyltransferase I activity in EoT compared to non-EoT patients. Sensitivity analysis also showed a significantly high dependence of eicosanoid-associated metabolic tasks on alpha-linolenic acid as unique to EoT patients.

Conclusions

Model-driven analysis of the endothelial cells' metabolism identified potential novel targets as impaired thromboxane A2 and leukotriene synthesis in EoT patients when compared to non-EoT patients. Reduced thromboxane A2 and leukotriene availability in the microvasculature impairs vasoconstriction ability and may thus contribute to shock in EoT patients. These findings are supported by extensive scientific literature; however, further investigations are required on these findings.

Intracellular and extracellular untargeted metabolomics reveal the effect of acute uranium exposure in HK-2 cells

Uranium exposure poses a serious threat to the health of occupational populations and the public. Although metabolomics is a promising research approach to study the toxicological mechanisms of uranium exposure, in vitro studies using human cells are scarce. Applying cultured cell metabolomics, we exhaustively analyzed the intracellular and extracellular differential metabolites upon uranium exposure and characterized the possible biological effects of uranium exposure on human kidney cells. Uranium exposure significantly induced disturbance in the amino acid biosynthesis and linoleic acid metabolism of the cells. Cells exposed to uranium produce excessive amounts of arachidonic acid, which has the potential to cause oxidative stress and damage cells. The results provide new evidence for an oxidative stress mechanism of uranium-induced renal cell injury. Cell metabolomics has proven to be a useful diagnostic tool to study the molecular mechanisms of uranium poisoning.

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

An integrated in vivo and in silico analysis of the metabolism disrupting effects of CPI-613 on embryo-larval zebrafish (Danio rerio)

CPI-613 is a mitochondrial metabolism disrupter that inhibits tricarboxylic acid (TCA) cycle activity. The consequences of TCA cycle disruption on various metabolic pathways and overall organismal physiology are not fully known. The present study integrates in vivo experimental data with an in silico stoichiometric metabolism model of zebrafish to study the metabolic pathways perturbed under CPI-613 exposure. Embryo-larval life stages of zebrafish (Danio rerio) were exposed to 1 μM CPI-613 for 20 days. Whole-organism respirometry measurements showed an initial suppression of O₂ consumption at Day 5 of exposure, followed by recovery comparable to the solvent control (0.01% DMSO) by Day 20. Comparison of whole-transcriptome RNA-sequencing at Day 5 vs. 20 of exposure showed functional categories related to O₂ binding and transport, antioxidant activity, FAD binding, and hemoglobin complexes, to be commonly represented. Metabolic enzyme gene expression changes and O₂ consumption rate was used to parametrize two in silico stoichiometric metabolic models representative of Day 5 or 20 of exposure. Computational simulations predicted impaired ATP synthesis, α-ketoglutarate dehydrogenase (KGDH) activity, and fatty acid β-oxidation at Day 5 vs. 20 of exposure. These results show that the targeted disruption of KGDH may also impact oxidative phosphorylation (ATP synthesis) and fatty acid metabolism (β-oxidation), in turn influencing cellular bioenergetics and the observed reduction in whole-organism O₂ consumption rate. The results of this study provide an integrated in vivo and in silico framework to study the impacts of metabolic disruption on organismal physiology.

Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer

BACKGROUND

Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge.

METHODS

Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively.

RESULTS

Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways.

CONCLUSIONS

Here, we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.

Optimization of a modeling platform to predict oncogenes from genome-scale metabolic networks of non-small-cell lung cancers

Cancer cell dysregulations result in the abnormal regulation of cellular metabolic pathways. By simulating this metabolic reprogramming using constraint-based modeling approaches, oncogenes can be predicted, and this knowledge can be used in prognosis and treatment. We introduced a trilevel optimization problem describing metabolic reprogramming for inferring oncogenes. First, this study used RNA-Seq expression data of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples and their healthy counterparts to reconstruct tissue-specific genome-scale metabolic models and subsequently build the flux distribution pattern that provided a measure for the oncogene inference optimization problem for determining tumorigenesis. The platform detected 45 genes for LUAD and 84 genes for LUSC that lead to tumorigenesis. A high level of differentially expressed genes was not an essential factor for determining tumorigenesis. The platform indicated that pyruvate kinase (PKM), a well-known oncogene with a low level of differential gene expression in LUAD and LUSC, had the highest fitness among the predicted oncogenes based on computation. By contrast, pyruvate kinase L/R (PKLR), an isozyme of PKM, had a high level of differential gene expression in both cancers. Phosphatidylserine synthase 1 (PTDSS1), an oncogene in LUAD, was inferred to have a low level of differential gene expression, and overexpression could significantly reduce survival probability. According to the factor analysis, PTDSS1 characteristics were close to those of the template, but they were unobvious in LUSC. Angiotensin-converting enzyme 2 (ACE2) has recently garnered widespread interest as the SARS-CoV-2 virus receptor. Moreover, we determined that ACE2 is an oncogene of LUSC but not of LUAD. The platform developed in this study can identify oncogenes with low levels of differential expression and be used to identify potential therapeutic targets for cancer treatment.

Analyzing Metabolic States of Adipogenic and Osteogenic Differentiation in Human Mesenchymal Stem Cells via Genome Scale Metabolic Model Reconstruction

Since their initial discovery in 1976, mesenchymal stem cells (MSCs) have been gathering interest as a possible tool to further the development and enhancement of various therapeutics within regenerative medicine. However, our current understanding of both metabolic function and existing differences within the varying cell lineages (e.g., cells in either osteogenesis or adipogenesis) is severely lacking making it more difficult to fully realize the therapeutic potential of MSCs. Here, we reconstruct the MSC metabolic network to understand the activity of various metabolic pathways and compare their usage under different conditions and use these models to perform experimental design. We present three new genome-scale metabolic models (GEMs) each representing a different MSC lineage (proliferation, osteogenesis, and adipogenesis) that are biologically feasible and have distinctive cell lineage characteristics that can be used to explore metabolic function and increase our understanding of these phenotypes. We present the most distinctive differences between these lineages when it comes to enriched metabolic subsystems and propose a possible osteogenic enhancer. Taken together, we hope these mechanistic models will aid in the understanding and therapeutic potential of MSCs.

Unraveling the Extracellular Metabolism of Immortalized Hippocampal Neurons Under Normal Growth Conditions

Metabolomic profiling of cell lines has shown many potential applications and advantages compared to animal models and human subjects, and an accurate cellular metabolite analysis is critical to understanding both the intracellular and extracellular environments in cell culture. This study provides a fast protocol to investigate in vitro metabolites of immortalized hippocampal neurons HN9.10e with minimal perturbation of the cell system using a targeted approach. HN9.10e neurons represent a reliable model of one of the most vulnerable regions of the central nervous system. Here, the assessment of their extracellular metabolic profile was performed by studying the cell culture medium before and after cell growth under standard conditions. The targeted analysis was performed by a direct, easy, high-throughput reversed-phase liquid chromatography with diode array detector (RP-HPLC-DAD) method and by headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) for the study of volatile organic compounds (VOCs). The analysis of six different batches of cells has allowed to investigate the metabolic reproducibility of neuronal cells and to describe the metabolic "starting" conditions that are mandatory for a well-grounded interpretation of the results of any following cellular treatment. An accurate study of the metabolic profile of the HN9.10e cell line has never been performed before, and it could represent a quality parameter before any other targeting assay or further exploration.

Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.

Comparative genomics and metabolomics analysis of Riemerella anatipestifer strain CH-1 and CH-2

Riemerella anatipestifer is a major pathogenic microorganism in poultry causing serositis with significant mortality. Serotype 1 and 2 were most pathogenic, prevalent, and liable over the world. In this study, the intracellular metabolites in R. anatipestifer strains RA-CH-1 (serotype 1) and RA-CH-2 (serotype 2) were identified by gas chromatography-mass spectrometer (GC–MS). The metabolic profiles were performed using hierarchical clustering and partial least squares discriminant analysis (PLS-DA). The results of hierarchical cluster analysis showed that the amounts of the detected metabolites were more abundant in RA-CH-2. RA-CH-1 and RA-CH-2 were separated by the PLS-DA model. 24 potential biomarkers participated in nine metabolisms were contributed predominantly to the separation. Based on the complete genome sequence database and metabolite data, the first large-scale metabolic models of iJL463 (RA-CH-1) and iDZ470 (RA-CH-2) were reconstructed. In addition, we explained the change of purine metabolism combined with the transcriptome and metabolomics data. The study showed that it is possible to detect and differentiate between these two organisms based on their intracellular metabolites using GC–MS. The present research fills a gap in the metabolomics characteristics of R. anatipestifer.

Identifying the effect of vancomycin on health care-associated methicillin-resistant Staphylococcus aureus strains using bacteriological and physiological media

BACKGROUND

The evolving antibiotic-resistant behavior of health care-associated methicillin-resistant Staphylococcus aureus (HA-MRSA) USA100 strains are of major concern. They are resistant to a broad class of antibiotics such as macrolides, aminoglycosides, fluoroquinolones, and many more.

FINDINGS

The selection of appropriate antibiotic susceptibility examination media is very important. Thus, we use bacteriological (cation-adjusted Mueller-Hinton broth) as well as physiological (R10LB) media to determine the effect of vancomycin on USA100 strains. The study includes the profiling behavior of HA-MRSA USA100 D592 and D712 strains in the presence of vancomycin through various high-throughput assays. The US100 D592 and D712 strains were characterized at sub-inhibitory concentrations through growth curves, RNA sequencing, bacterial cytological profiling, and exo-metabolomics high throughput experiments.

CONCLUSIONS

The study reveals the vancomycin resistance behavior of HA-MRSA USA100 strains in dual media conditions using wide-ranging experiments.

Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures

Biological organisms are constantly exposed to an immense repertoire of molecules that cover environmental or food-derived molecules and drugs, triggering a continuous flow of stimuli-dependent adaptations. The diversity of these chemicals as well as their concentrations contribute to the multiplicity of induced effects, including activation, stimulation, or inhibition of physiological processes and toxicity. Metabolism, as the foremost phenotype and manifestation of life, has proven to be immensely sensitive and highly adaptive to chemical stimuli. Therefore, studying the effect of endo- or xenobiotics over cellular metabolism delivers valuable knowledge to apprehend potential cellular activity of individual molecules and evaluate their acute or chronic benefits and toxicity. The development of modern metabolomics technologies such as mass spectrometry or nuclear magnetic resonance spectroscopy now offers unprecedented solutions for the rapid and efficient determination of metabolic profiles of cells and more complex biological systems. Combined with the availability of well-established cell culture techniques, these analytical methods appear perfectly suited to determine the biological activity and estimate the positive and negative effects of chemicals in a variety of cell types and models, even at hardly detectable concentrations. Metabolic phenotypes can be estimated from studying intracellular metabolites at homeostasis in vivo, while in vitro cell cultures provide additional access to metabolites exchanged with growth media. This article discusses analytical solutions available for metabolic phenotyping of cell culture metabolism as well as the general metabolomics workflow suitable for testing the biological activity of molecular compounds. We emphasize how metabolic profiling of cell supernatants and intracellular extracts can deliver valuable and complementary insights for evaluating the effects of xenobiotics on cellular metabolism. We note that the concepts and methods discussed primarily for xenobiotics exposure are widely applicable to drug testing in general, including endobiotics that cover active metabolites, nutrients, peptides and proteins, cytokines, hormones, vitamins, etc.

Suppression of water-bloom cyanobacterium Microcystis aeruginosa by algaecide hydrogen peroxide maximized through programmed cell death

The global expansion and intensification of toxic cyanobacterial blooms require effective algaecides. Algaecides should be selective, effective, fast-acting, and ideally suppress cyanotoxin production. In this study, whether both maximum growth suppression and minimal toxin production can be simultaneously achieved was tested with a selective algaecide H₂O₂, through its ability to induce apoptosis-like programmed cell death (AL PCD) in a common bloom species Microcystis aeruginosa. Under doses of 1-15 mg L^-1, non-monotonic dose-response suppression of H₂O₂ on M. aeruginosa were observed, where maximal cell death and minimal microcystin production both occurred at a moderate dose of 10 mg L^-1 H₂O₂. Maximal cell death was indeed achieved through AL PCD, as revealed by integrated biochemical, structural, physiological and transcriptional evidence; transcriptional profile suggested AL PCD was mediated by mazEF and lexA systems. Higher H₂O₂ doses directly led to necrosis in M. aeruginosa, while lower doses only caused recoverable stress. The integrated data showed the choice between the two modes of cell death is determined by the intracellular energy state under stress. A model was proposed for suppressing M. aeruginosa with AL PCD or necrosis. H₂O₂ was demonstrated to simultaneously maximize the suppression of both growth and microcystin production through triggering AL PCD.

Metabolomic and Lipidomic Profiling of Bone Marrow Plasma Differentiates Patients with Monoclonal Gammopathy of Undetermined Significance from Multiple Myeloma

Oncogenic drivers of progression of monoclonal gammopathy of undetermined significance (MGUS) to multiple myeloma (MM) such as c-MYC have downstream effects on intracellular metabolic pathways of clonal plasma cells (PCs). Thus, extracellular environments such as the bone marrow (BM) plasma likely have unique metabolite profiles that differ from patients with MGUS compared to MM. This study utilized an untargeted metabolite and targeted complex lipid profiling of BM plasma to identify significant differences in the relative metabolite levels between patients with MGUS and MM from an exploratory cohort. This was followed by verification of some of the metabolite differences of interest by targeted quantification of the metabolites using isotopic internal standards in the exploratory cohort as well as an independent validation cohort. Significant differences were noted in the amino acid profiles such as decreased branch chain amino acids (BCAAs) and increased catabolism of tryptophan to the active kynurenine metabolite 3-hydroxy-kynurenine between patients with MGUS and MM. A decrease in the total levels of complex lipids such as phosphatidylethanolamines (PE), lactosylceramides (LCER) and phosphatidylinositols (PI) were also detected in the BM plasma samples from MM compared to MGUS patients. Thus, metabolite and complex lipid profiling of the BM plasma identifies differences in levels of metabolites and lipids between patients with MGUS and MM. This may provide insight into the possible differences of the intracellular metabolic pathways of their clonal PCs.

Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN

Altered metabolism is associated with many human diseases. Human genome-scale metabolic models (GEMs) were reconstructed within systems biology to study the biochemistry occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation. We present here redHUMAN, a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology using the recently established methods redGEM and lumpGEM. The reductions include the thermodynamic properties of compounds and reactions guaranteeing the consistency of predictions with the bioenergetics of the cell. We introduce a method (redGEMX) to incorporate the pathways used by cells to adapt to the medium. We provide the thermodynamic curation of the human GEMs Recon2 and Recon3D and we apply the redHUMAN workflow to derive leukemia-specific reduced models. The reduced models are powerful platforms for studying metabolic differences between phenotypes, such as diseased and healthy cells.

Yeast-Yeast Interactions: Mechanisms, Methodologies and Impact on Composition

During the winemaking process, alcoholic fermentation is carried out by a consortium of yeasts in which interactions occurs. The consequences of these interactions on the wine matrix have been widely described for several years with the aim of controlling the winemaking process as well as possible. In this review, we highlight the wide diversity of methodologies used to study these interactions, and their underlying mechanisms and consequences on the final wine composition and characteristics. The wide variety of matrix parameters, yeast couples, and culture conditions have led to contradictions between the results of the different studies considered. More recent aspects of modifications in the composition of the matrix are addressed through different approaches that have not been synthesized recently. Non-volatile and volatile metabolomics, as well as sensory analysis approaches are developed in this paper. The description of the matrix composition modification does not appear sufficient to explain interaction mechanisms, making it vital to take an integrated approach to draw definite conclusions on them.

Current Status and Future Prospects of Genome-Scale Metabolic Modeling to Optimize the Use of Mesenchymal Stem Cells in Regenerative Medicine

Mesenchymal stem cells are a promising source for externally grown tissue replacements and patient-specific immunomodulatory treatments. This promise has not yet been fulfilled in part due to production scaling issues and the need to maintain the correct phenotype after re-implantation. One aspect of extracorporeal growth that may be manipulated to optimize cell growth and differentiation is metabolism. The metabolism of MSCs changes during and in response to differentiation and immunomodulatory changes. MSC metabolism may be linked to functional differences but how this occurs and influences MSC function remains unclear. Understanding how MSC metabolism relates to cell function is however important as metabolite availability and environmental circumstances in the body may affect the success of implantation. Genome-scale constraint based metabolic modeling can be used as a tool to fill gaps in knowledge of MSC metabolism, acting as a framework to integrate and understand various data types (e.g., genomic, transcriptomic and metabolomic). These approaches have long been used to optimize the growth and productivity of bacterial production systems and are being increasingly used to provide insights into human health research. Production of tissue for implantation using MSCs requires both optimized production of cell mass and the understanding of the patient and phenotype specific metabolic situation. This review considers the current knowledge of MSC metabolism and how it may be optimized along with the current and future uses of genome scale constraint based metabolic modeling to further this aim.

Using Genome-Scale Metabolic Networks for Analysis, Visualization, and Integration of Targeted Metabolomics Data

Interpretation of metabolomics data in the context of biological pathways is important to gain knowledge about underlying metabolic processes. In this chapter we present methods to analyze genome-scale models (GSMs) and metabolomics data together. This includes reading and mining of GSMs using the SBTab format to retrieve information on genes, reactions, and metabolites. Furthermore, the chapter showcases the generation of metabolic pathway maps using the Escher tool, which can be used for data visualization. Lastly, approaches to constrain flux balance analysis (FBA) by metabolomics data are presented.

Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling

Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.

Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine

In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and in silico clinical trials.

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.

Use of genome-scale models to get new insights into the marine actinomycete genus Salinispora

BACKGROUND

There is little published regarding metabolism of Salinispora species. In continuation with efforts performed towards this goal, this study is focused on new insights into the metabolism of the three-identified species of Salinispora using constraints-based modeling. At present, only one manually curated genome-scale metabolic model (GSM) for Salinispora tropica strain CNB-440T has been built despite the role of Salinispora strains in drug discovery.

RESULTS

Here, we updated, and expanded the scope of the model of Salinispora tropica CNB-440T, and GSMs were constructed for two sequenced type strains covering the three-identified species. We also constructed a Salinispora core model that contains the genes shared by 93 sequenced strains and a few non-conserved genes associated with essential reactions. The models predicted no auxotrophies for essential amino acids, which was corroborated experimentally using a defined minimal medium (DMM). Experimental observations suggest possible sulfur accumulation. The Core metabolic content shows that the biosynthesis of specialised metabolites is the less conserved subsystem. Sets of reactions were analyzed to explore the differences between the reconstructions. Unique reactions associated to each GSM were mainly due to genome sequence data except for the ST-CNB440 reconstruction. In this case, additional reactions were added from experimental evidence. This reveals that by reaction content the ST-CNB440 model is different from the other species models. The differences identified in reaction content between models gave rise to different functional predictions of essential nutrient usage by each species in DMM. Furthermore, models were used to evaluate in silico single gene knockouts under DMM and complex medium. Cluster analysis of these results shows that ST-CNB440, and SP-CNR114 models are more similar when considering predicted essential genes.

CONCLUSIONS

Models were built for each of the three currently identified Salinispora species, and a core model representing the conserved metabolic capabilities of Salinispora was constructed. Models will allow in silico metabolism studies of Salinispora strains, and help researchers to guide and increase the production of specialised metabolites. Also, models can be used as templates to build GSMs models of closely related organisms with high biotechnology potential.

Juice Index: an integrated Sauvignon blanc grape and wine metabolomics database shows mainly seasonal differences

INTRODUCTION

Although Sauvignon Blanc (SB) grapes are cultivated widely throughout New Zealand, wines from the Marlborough region are most famous for their typical varietal combination of tropical and vegetal aromas. These wines differ in composition from season to season as well as among locations within the region, which makes the continual production of good quality wines challenging. Here, we developed a unique database of New Zealand SB grape juices and wines to develop tools to help winemakers to make blending decisions and assist in the development of new wine styles.

METHODS

About 400 juices were collected from different regions in New Zealand over three harvest seasons (2011-2013), which were then fermented under controlled conditions using a commercial yeast strain Saccharomyces cerevisiae EC1118. Comprehensive metabolite profiling of these juices and wines by gas chromatography-mass spectrometry (GC-MS) was combined with their detailed oenological parameters and associated meteorological data.

RESULTS

These combined metabolomics data clearly demonstrate that seasonal variation is more prominent than regional difference in both SB grape juices and wines, despite almost universal use of vineyard irrigation to mitigate seasonal rainfall and evapotranspiration differences, Additionally, we identified a group of juice metabolites that play central roles behind these variations, which may represent chemical signatures for juice and wine quality assessment.

CONCLUSION

This database is the first of its kind in the world to be available for the wider scientific community and offers potential as a predictive tool for wine quality and innovation when combined with mathematical modelling.

Integrating -omics data into genome-scale metabolic network models: principles and challenges

At genome scale, it is not yet possible to devise detailed kinetic models for metabolism because data on the in vivo biochemistry are too sparse. Predictive large-scale models for metabolism most commonly use the constraint-based framework, in which network structures constrain possible metabolic phenotypes at steady state. However, these models commonly leave many possibilities open, making them less predictive than desired. With increasingly available -omics data, it is appealing to increase the predictive power of constraint-based models (CBMs) through data integration. Many corresponding methods have been developed, but data integration is still a challenge and existing methods perform less well than expected. Here, we review main approaches for the integration of different types of -omics data into CBMs focussing on the methods' assumptions and limitations. We argue that key assumptions - often derived from single-enzyme kinetics - do not generally apply in the context of networks, thereby explaining current limitations. Emerging methods bridging CBMs and biochemical kinetics may allow for -omics data integration in a common framework to provide more accurate predictions.

An Untargeted Metabolomics Approach to Investigate the Metabolic Modulations of HepG2 Cells Exposed to Low Doses of Bisphenol A and 17β-Estradiol

The model xeno-estrogen bisphenol A (BPA) has been extensively studied over the past two decades, contributing to major advances in the field of endocrine disrupting chemicals research. Besides its well documented adverse effects on reproduction and development observed in rodents, latest studies strongly suggest that BPA disrupts several endogenous metabolic pathways, with suspected steatogenic and obesogenic effects. BPA's adverse effects on reproduction are attributed to its ability to activate estrogen receptors (ERs), but its effects on metabolism and its mechanism(s) of action at low doses are so far only marginally understood. Metabolomics based approaches are increasingly used in toxicology to investigate the biological changes induced by model toxicants and chemical mixtures, to identify markers of toxicity and biological effects. In this study, we used proton nuclear magnetic resonance (1H-NMR) based untargeted metabolite profiling, followed by multivariate statistics and computational analysis of metabolic networks to examine the metabolic modulation induced in human hepatic cells (HepG2) by an exposure to low and very low doses of BPA (10-6M, 10-9M, and 10-12M), vs. the female reference hormone 17β-estradiol (E2, 10-9M, 10-12M, and 10-15M). Metabolomic analysis combined to metabolic network reconstruction highlighted different mechanisms at lower doses of exposure. At the highest dose, our results evidence that BPA shares with E2 the capability to modulate several major metabolic routes that ensure cellular functions and detoxification processes, although the effects of the model xeno-estrogen and of the natural hormone can still be distinguished.

Integrating Extracellular Flux Measurements and Genome-Scale Modeling Reveals Differences between Brown and White Adipocytes

White adipocytes are specialized for energy storage, whereas brown adipocytes are specialized for energy expenditure. Explicating this difference can help identify therapeutic targets for obesity. A common tool to assess metabolic differences between such cells is the Seahorse Extracellular Flux (XF) Analyzer, which measures oxygen consumption and media acidification in the presence of different substrates and perturbagens. Here, we integrate the Analyzer's metabolic profile from human white and brown adipocytes with a genome-scale metabolic model to predict flux differences across the metabolic map. Predictions matched experimental data for the metabolite 4-aminobutyrate, the protein ABAT, and the fluxes for glucose, glutamine, and palmitate. We also uncovered a difference in how adipocytes dispose of nitrogenous waste, with brown adipocytes secreting less ammonia and more urea than white adipocytes. Thus, the method and software we developed allow for broader metabolic phenotyping and provide a distinct approach to uncovering metabolic differences.

Prediction of acute GVHD and relapse by metabolic biomarkers after allogeneic hematopoietic stem cell transplantation

BACKGROUND

There are very few studies investigating metabolic biomarkers to predict acute graft-versus-host disease (aGVHD) after allogeneic hematopoietic stem cell transplantation (HSCT). Metabolic models can provide a framework for analyzing the information-rich omics data sets in this setting.

METHODS

Four hundred and fifty-six samples from one hundred and fourteen consecutive patients who underwent HSCT from January 2012 to May 2014 were collected for this study. The changes in serum metabolite levels were investigated using a gas chromatography-mass spectrometry-based metabolomics approach and underwent statistical analysis.

RESULTS

Significant metabolic changes were observed on day 7. The stearic acid/palmitic acid (SA/PA) ratio was effective in the diagnosis of grade II-IV aGVHD. Multivariate analysis showed that patients with high SA/PA ratios on day 7 after HSCT were less likely to develop II-IV aGVHD than patients with low SA/PA ratios (odds ratio [OR] = 0.06, 95% CI 0.02-0.18, P < 0.001). After the adjustment for clinical characteristics, the SA/PA ratio had no significant effect on overall survival (hazard ratio [HR] = 1.95, 95% CI 0.92-4.14, P = 0.08), and patients in the high SA/PA ratio group were significantly more likely to relapse than those in the low ratio group (HR = 2.26, 95% CI 1.04-4.91, P = 0.04).

CONCLUSION

Our findings suggest that the SA/PA ratio on day 7 after HSCT is an excellent biomarker to predict both aGVHD and relapse. The serum SA/PA ratio measured on day 7 after transplantation may improve risk stratification for aGVHD and relapse after allogeneic stem cell transplantation.

FUNDING

National Natural Science Foundation of China (81470346, 81773361), Priority Academic Program Development of Jiangsu Higher Education Institutions, Jiangsu Natural Science Foundation (BK20161204), Innovation Capability Development Project of Jiangsu Province (BM2015004), Jiangsu Medical Junior Talent Person award (QNRC2016707), and NIH (AI129582 and NS106170).

Constraint-based modeling in microbial food biotechnology

Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. Constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype–phenotype relationships. This is essential for the rational improvement of industrial strains, which can further be facilitated through various model-guided strain design approaches. CBM of microbial communities offers a valuable tool for the rational design of defined food cultures, where it can catalyze hypothesis generation and provide unintuitive rationales for the development of enhanced community phenotypes and, consequently, novel or improved food products. In the industrial-scale production of microorganisms for food cultures, CBM may enable a knowledge-driven bioprocess optimization by rationally identifying strategies for growth and stability improvement. Through these applications, we believe that CBM can become a powerful tool for guiding the areas of strain development, culture development and process optimization in the production of food cultures. Nevertheless, in order to make the correct choice of the modeling framework for a particular application and to interpret model predictions in a biologically meaningful manner, one should be aware of the current limitations of CBM.

Metabolite secretion in microorganisms: the theory of metabolic overflow put to the test

INTRODUCTION

Microbial cells secrete many metabolites during growth, including important intermediates of the central carbon metabolism. This has not been taken into account by researchers when modeling microbial metabolism for metabolic engineering and systems biology studies.

MATERIALS AND METHODS

The uptake of metabolites by microorganisms is well studied, but our knowledge of how and why they secrete different intracellular compounds is poor. The secretion of metabolites by microbial cells has traditionally been regarded as a consequence of intracellular metabolic overflow.

CONCLUSIONS

Here, we provide evidence based on time-series metabolomics data that microbial cells eliminate some metabolites in response to environmental cues, independent of metabolic overflow. Moreover, we review the different mechanisms of metabolite secretion and explore how this knowledge can benefit metabolic modeling and engineering.

Monitoring tyrosine kinase inhibitor therapeutic responses with a panel of metabolic biomarkers in chronic myeloid leukemia patients

The aim of this study is to investigate the potential biomarkers associated with chronic myeloid leukemia (CML), reveal the metabolite changes related to the continuous phases of tyrosine kinase inhibitors (TKIs), and find the potential biomarkers associated with treatment effects. Fifty‐two patients with CML and 26 matched healthy people were enrolled as the discovery set. Another 194 randomly selected CML patients treated with TKI were chosen as the external validation set. Plasma samples from the patients and controls were profiled using the gas chromatography‐mass spectrometry‐based metabonomic approach. Multivariate and univariate statistical analyses were combined to select the differential metabolic features. The gas chromatography‐mass spectrometry‐based metabolomics showed a clear clustering and separation of metabolic patterns from healthy controls and pre‐ and post‐TKI treatment CML patients in the discovery set. We identified 9 metabolites that differentiated CML patients from healthy controls, including lactic acid, isoleucine, glycerol, glycine, myristic acid, d‐sorbitol, d‐galactose, d‐glucose, and myo‐inositol. Among the 9 markers, glycerol and myristic acid had the most significant association with TKI treatment effects in both discovery and external validation sets. In the receiver operating characteristic analysis, the combination of glycerol and myristic acid showed a better discrimination performance compared to a single biomarker. The results indicated that metabolic profiling has the potential for diagnosis of CML and the panel of biomarkers including myristic acid and glycerol could be useful in monitoring TKI therapeutic responses.

Experimental design and reporting standards for metabolomics studies of mammalian cell lines

Metabolomics is an analytical technique that investigates the small biochemical molecules present within a biological sample isolated from a plant, animal, or cultured cells. It can be an extremely powerful tool in elucidating the specific metabolic changes within a biological system in response to an environmental challenge such as disease, infection, drugs, or toxins. A historically difficult step in the metabolomics pipeline is in data interpretation to a meaningful biological context, for such high-variability biological samples and in untargeted metabolomics studies that are hypothesis-generating by design. One way to achieve stronger biological context of metabolomic data is via the use of cultured cell models, particularly for mammalian biological systems. The benefits of in vitro metabolomics include a much greater control of external variables and no ethical concerns. The current concerns are with inconsistencies in experimental procedures and level of reporting standards between different studies. This review discusses some of these discrepancies between recent studies, such as metabolite extraction and data normalisation. The aim of this review is to highlight the importance of a standardised experimental approach to any cultured cell metabolomics study and suggests an example procedure fully inclusive of information that should be disclosed in regard to the cell type/s used and their culture conditions. Metabolomics of cultured cells has the potential to uncover previously unknown information about cell biology, functions and response mechanisms, and so the accurate biological interpretation of the data produced and its ability to be compared to other studies should be considered vitally important.

Extracellular Microbial Metabolomics: The State of the Art

Microorganisms produce and secrete many primary and secondary metabolites to the surrounding environment during their growth. Therefore, extracellular metabolites provide important information about the changes in microbial metabolism due to different environmental cues. The determination of these metabolites is also comparatively easier than the extraction and analysis of intracellular metabolites as there is no need for cell rupture. Many analytical methods are already available and have been used for the analysis of extracellular metabolites from microorganisms over the last two decades. Here, we review the applications and benefits of extracellular metabolite analysis. We also discuss different sample preparation protocols available in the literature for both types (e.g., metabolites in solution and in gas) of extracellular microbial metabolites. Lastly, we evaluate the authenticity of using extracellular metabolomics data in the metabolic modelling of different industrially important microorganisms.

A systems approach reveals distinct metabolic strategies among the NCI-60 cancer cell lines

The metabolic phenotype of cancer cells is reflected by the metabolites they consume and by the byproducts they release. Here, we use quantitative, extracellular metabolomic data of the NCI-60 panel and a novel computational method to generate 120 condition-specific cancer cell line metabolic models. These condition-specific cancer models used distinct metabolic strategies to generate energy and cofactors. The analysis of the models' capability to deal with environmental perturbations revealed three oxotypes, differing in the range of allowable oxygen uptake rates. Interestingly, models based on metabolomic profiles of melanoma cells were distinguished from other models through their low oxygen uptake rates, which were associated with a glycolytic phenotype. A subset of the melanoma cell models required reductive carboxylation. The analysis of protein and RNA expression levels from the Human Protein Atlas showed that IDH2, which was an essential gene in the melanoma models, but not IDH1 protein, was detected in normal skin cell types and melanoma. Moreover, the von Hippel-Lindau tumor suppressor (VHL) protein, whose loss is associated with non-hypoxic HIF-stabilization, reductive carboxylation, and promotion of glycolysis, was uniformly absent in melanoma. Thus, the experimental data supported the predicted role of IDH2 and the absence of VHL protein supported the glycolytic and low oxygen phenotype predicted for melanoma. Taken together, our approach of integrating extracellular metabolomic data with metabolic modeling and the combination of different network interrogation methods allowed insights into the metabolism of cells.

Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells

Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites (p < 0.05) in RBC metabolism using only measurements of these five biomarkers. The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. The ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.

While deep-coverage omics data sets are allowing for more complete characterization of biological systems, there has been a concerted effort to identify a subset of measurements that are representative of qualitative network-level behavior. For some systems—like the human red blood cell (RBC)—such biomarkers have already been identified. Using the concentration profiles of these biomarkers as input to a statistical model, we predict quantitative concentration profiles of other metabolites in the RBC network. These results demonstrate that if good biomarkers are available for a biological system, it is possible to use these measurements to gain insight into the quantitative state of the rest of the network.

Change of the metabolomic profile during short-term mononuclear cell storage

BACKGROUND AND OBJECTIVES

Short-term storage of leukapheresis products used for immunotherapeutic mononuclear cell (MNC) products is a frequent event. The analysis of time-related metabolic patterns enables the characterization of storage-related effects in MNCs and the hypothesis-based optimization of the MNC medium.

MATERIALS AND METHODS

The MNC products from seven leukapheresis procedures were stored within a closed bag system for 48 h. Concentrations of amino acids, biogenic amines, phospho- and sphingolipids and hexoses in the medium were measured by targeted metabolomics. The viability of MNC subpopulations was assayed by Annexin V (AnV) and JC-1 staining.

RESULTS

Glucose depletion and a significant change of the acylcarnitine profile are early events within the first 24 h of storage. In contrast, for most amino acids, the maximum increase was observed at 48 h of storage as mirrored by an increase in the amino acid levels by a mean factor of 1·2 (1·3, 2·0) after 6 h (24 h, 48 h, respectively). This was except for the concentrations of glutamine and lysine, which did not change significantly. The taurine concentration showed a twofold increase within the first 24 h and remained constant thereafter. The steepest increase in AnV⁺ and 7-AAD⁺ CD4⁺ T cells was found between 24 and 48 h.

CONCLUSION

The time-course of apoptosis and metabolic patterns in the MNC products demonstrate that 24 h of storage is a decisive time-point, as afterwards key metabolic pathways showed nonlinear detrimental changes. Optimization of storage by supplementation of specific substrates demands therefore an early intervention.

MetaboTools: A Comprehensive Toolbox for Analysis of Genome-Scale Metabolic Models

Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Previous work, by us and others, revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. With the MetaboTools, we make our methods available to the broader scientific community. The MetaboTools consist of a protocol, a toolbox, and tutorials of two use cases. The protocol describes, in a step-wise manner, the workflow of data integration, and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, the MetaboTools constitute a comprehensive guide to the intra-model analysis of extracellular metabolomic data from microbial, plant, or human cells. This computational modeling resource offers a broad set of computational analysis tools for a wide biomedical and non-biomedical research community.