LASSO regression shows histidine and sphingosine 1 phosphate are linked to both sepsis mortality and endothelial damage.

Sepsis is a major cause of death worldwide, with a mortality rate that has remained stubbornly high. The current gold standard of risk stratifying sepsis patients provides limited mechanistic insight for therapeutic targeting. An improved ability to predict sepsis mortality and to understand the risk factors would allow better treatment targeting. Sepsis causes metabolic dysregulation in patients; therefore, metabolomics offers a promising tool to study sepsis. It is also known that that in sepsis endothelial cells affecting their function regarding blood clotting and vascular permeability. We integrated metabolomics data from patients admitted to an intensive care unit for sepsis, with commonly collected clinical features of their cases and two measures of endothelial function relevant to blood vessel function, platelet endothelial cell adhesion molecule and soluble thrombomodulin concentrations in plasma. We used least absolute shrinkage and selection operator penalized regression, and pathway enrichment analysis to identify features most able to predict 30-day survival. The features important to sepsis survival include carnitines, and amino acids. Endothelial proteins in plasma also predict 30-day mortality and the levels of these proteins also correlate with a somewhat overlapping set of metabolites. Overall metabolic dysregulation, particularly in endothelial cells, may be a contributory factor to sepsis response. By exploring sepsis metabolomics data in conjunction with clinical features and endothelial proteins we have gained a better understanding of sepsis risk factors.

MYC overrides HIF-1α to regulate proliferating primary cell metabolism in hypoxia.

Hypoxia requires metabolic adaptations to sustain energetically demanding cellular activities. While the metabolic consequences of hypoxia have been studied extensively in cancer cell models, comparatively little is known about how primary cell metabolism responds to hypoxia. Thus, we developed metabolic flux models for human lung fibroblast and pulmonary artery smooth muscle cells proliferating in hypoxia. Unexpectedly, we found that hypoxia decreased glycolysis despite activation of hypoxia-inducible factor 1α (HIF-1α) and increased glycolytic enzyme expression. While HIF-1α activation in normoxia by prolyl hydroxylase (PHD) inhibition did increase glycolysis, hypoxia blocked this effect. Multi-omic profiling revealed distinct molecular responses to hypoxia and PHD inhibition, and suggested a critical role for MYC in modulating HIF-1α responses to hypoxia. Consistent with this hypothesis, MYC knockdown in hypoxia increased glycolysis and MYC over-expression in normoxia decreased glycolysis stimulated by PHD inhibition. These data suggest that MYC signaling in hypoxia uncouples an increase in HIF-dependent glycolytic gene transcription from glycolytic flux.

Metabolic Response in Endothelial Cells to Catecholamine Stimulation Associated with Increased Vascular Permeability.

Disruption to endothelial cell homeostasis results in an extensive variety of human pathologies that are particularly relevant to major trauma. Circulating catecholamines, such as adrenaline and noradrenaline, activate endothelial adrenergic receptors triggering a potent response in endothelial function. The regulation of the endothelial cell metabolism is distinct and profoundly important to endothelium homeostasis. However, a precise catalogue of the metabolic alterations caused by sustained high catecholamine levels that results in endothelial dysfunction is still underexplored. Here, we uncover a set of up to 46 metabolites that exhibit a dose-response relationship to adrenaline-noradrenaline equimolar treatment. The identified metabolites align with the glutathione-ascorbate cycle and the nitric oxide biosynthesis pathway. Certain key metabolites, such as arginine and reduced glutathione, displayed a differential response to treatment in early (4 h) compared to late (24 h) stages of sustained stimulation, indicative of homeostatic metabolic feedback loops. Furthermore, we quantified an increase in the glucose consumption and aerobic respiration in endothelial cells upon catecholamine stimulation. Our results indicate that oxidative stress and nitric oxide metabolic pathways are downstream consequences of endothelial cell stimulation with sustained high levels of catecholamines. A precise understanding of the metabolic response in endothelial cells to pathological levels of catecholamines will facilitate the identification of more efficient clinical interventions in trauma patients.

Metabolic and Transcriptional Changes across Osteogenic Differentiation of Mesenchymal Stromal Cells.

Mesenchymal stromal cells (MSCs) are multipotent post-natal stem cells with applications in tissue engineering and regenerative medicine. MSCs can differentiate into osteoblasts, chondrocytes, or adipocytes, with functional differences in cells during osteogenesis accompanied by metabolic changes. The temporal dynamics of these metabolic shifts have not yet been fully characterized and are suspected to be important for therapeutic applications such as osteogenesis optimization. Here, our goal was to characterize the metabolic shifts that occur during osteogenesis. We profiled five key extracellular metabolites longitudinally (glucose, lactate, glutamine, glutamate, and ammonia) from MSCs from four donors to classify osteogenic differentiation into three metabolic stages, defined by changes in the uptake and secretion rates of the metabolites in cell culture media. We used a combination of untargeted metabolomic analysis, targeted analysis of 13C-glucose labelled intracellular data, and RNA-sequencing data to reconstruct a gene regulatory network and further characterize cellular metabolism. The metabolic stages identified in this proof-of-concept study provide a framework for more detailed investigations aimed at identifying biomarkers of osteogenic differentiation and small molecule interventions to optimize MSC differentiation for clinical applications.

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.

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.

Comparative Metabolic Network Flux Analysis to Identify Differences in Cellular Metabolism.

Metabolic network flux analysis uses genome-scale metabolic reconstructions to integrate transcriptomics, proteomics, and/or metabolomics data to allow for comprehensive interpretation of genotype to metabolic phenotype relationships. The compilation of many Constraint-based model analysis methods into one MATLAB package, the COBRAtoolbox, has opened the possibility of using these methods to the many biologists with some knowledge of the commonly used statistical program, MATLAB. Here we outline the steps required to take a published genome-scale metabolic reconstruction and interrogate its consistency and biological feasibility. Subsequently, we demonstrate how mRNA expression data and metabolomics data, relating to one or more cell types or biological contexts, can be applied to constrain and generate metabolic models descriptive of metabolic flux phenotypes. Finally, we describe the comparison of the resulting models and model outputs with the aim of identifying metabolic biomarkers and changes in cellular metabolism.

Metabolic Systems Analysis of Shock-Induced Endotheliopathy (SHINE) in Trauma: A New Research Paradigm.

OBJECTIVE: Investigate the endothelial cell phenotype (s) that causes Shock-Induced Endotheliopathy in trauma. BACKGROUND: We have studied more than 2750 trauma patients and identified that patients with high circulating syndecan-1 (endothelial glycocalyx damage marker) in plasma have an increased mortality rate compared with patients with lower levels. Notably, we found that patients suffering from the same trauma severity could develop significantly different degrees of endothelial dysfunction as measured by syndecan-1. METHODS: Prospective observational study of 20 trauma patients admitted to a Level 1 Trauma Centre and 20 healthy controls. Admission plasma syndecan-1 level and mass spectrometry were measured and analyzed by computational network analysis of our genome-scale metabolic model of the microvascular endothelial cell function. RESULTS: Trauma patients had a significantly different endothelial metabolic profile compared with controls. Among the patients, 4 phenotypes were identified. Three phenotypes were independent of syndecan-1 levels. We developed genome-scale metabolic models representative of the observed phenotypes. Within these phenotypes, we observed differences in the cell fluxes from glucose and palmitate to produce Acetyl-CoA, and secretion of heparan sulfate proteoglycan (component of syndecan-1). CONCLUSIONS: We confirm that trauma patients have a significantly different metabolic profile compared with controls. A minimum of 4 shock-induced endotheliopathy phenotypes were identified, which were independent of syndecan-1level (except 1 phenotype) verifying that the endothelial response to trauma is heterogeneous and most likely driven by a genetic component. Moreover, we introduced a new research tool in trauma by using metabolic systems biology, laying the foundation for personalized medicine.

Three Faces of Mercaptopurine Cytotoxicity In Vitro: Methylation, Nucleotide Homeostasis, and Deoxythioguanosine in DNA.

Mercaptopurine (MP) is a cytotoxic thiopurine important for the treatment of cancer and autoimmune diseases. MP and other thiopurine drugs undergo extensive intracellular metabolism, but the mechanisms of action are poorly characterized. In particular, it is unknown how different metabolites contribute to cytotoxicity and incorporation of thiopurine bases into DNA. The aim of this study was to ask whether cytotoxicity results from the incorporation of thioguanosine nucleotides into DNA, an alternative thiopurine metabolite, or a combination of factors. Therefore, we measured the cytotoxicity, metabolism, and incorporation of thioguanosine into DNA in response to MP or MP metabolites. Thiopurine metabolites varied in cytotoxicity, with methyl-thioinosine-mono-phosphate and thioguanosine-tri-phosphate the most toxic, and the methyl-thioguanosine nucleotides the least. We show, using liquid chromatography-tandem mass spectrometry, how different metabolites may perturb biochemical pathways, particularly disrupting guanosine nucleotide homeostasis, that may contribute to the mechanism of action of thiopurines. Although there was no correlation between metabolite cytotoxicity and the levels of 6-methylthioinosine-mono-phosphate or thioguanosine incorporation into DNA as individual factors, a combined analysis suggested that these factors together had a major influence on cytotoxicity. This study emphasizes the importance of enzymes of nucleotide homeostasis, methylation, and demethylation in thiopurine effects. These results will facilitate the development of dynamic biochemical models of thiopurine biochemistry that will improve our understanding of mechanisms of action in relevant target tissues.

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

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

Azathioprine with Allopurinol: Lower Deoxythioguanosine in DNA and Transcriptome Changes Indicate Mechanistic Differences to Azathioprine Alone.

BACKGROUND: Use of azathioprine (AZA) for inflammatory bowel disease is limited by side effects or poor efficacy. Combining low-dose azathioprine with allopurinol (LDAA) bypasses side effects, improves efficacy, and may be appropriate as first-line therapy. We test the hypothesis that standard-dose azathioprine (AZA) and LDAA treatments work by similar mechanisms, using incorporation of the metabolite deoxythioguanosine into patient DNA, white-blood cell counts, and transcriptome analysis as biological markers of drug effect. METHODS: DNA was extracted from peripheral whole-blood from patients with IBD treated with AZA or LDAA, and analyzed for DNA-incorporated deoxythioguanosine. Measurement of red-blood cell thiopurine metabolites was part of usual clinical practice, and pre- and on-treatment (12 wk) blood samples were used for transcriptome analysis. RESULTS: There were no differences in reduction of white-cell counts between the 2 treatment groups, but patients on LDAA had lower DNA-incorporated deoxythioguanosine than those on AZA; for both groups, incorporated deoxythioguanosine was lower in patients on thiopurines for 24 weeks or more (maintenance of remission) compared to patients treated for less than 24 weeks (achievement of remission). Patients on LDAA had higher levels of red-blood cell thioguanine nucleotides than those on AZA, but there was no correlation between these or their methylated metabolites, and incorporated deoxythioguanosine. Transcriptome analysis suggested down-regulation of immune responses consistent with effective immunosuppression in patients receiving LDAA, with evidence for different mechanisms of action between the 2 therapies. CONCLUSIONS: LDAA is biologically effective despite lower deoxythioguanosine incorporation into DNA, and has different mechanisms of action compared to standard-dose azathioprine.

Liquid chromatography-mass spectrometry for measuring deoxythioguanosine in DNA from thiopurine-treated patients.

Adverse reactions and non-response are common in patients treated with thiopurine drugs. Current monitoring of drug metabolite levels for guiding treatment are limited to analysis of thioguanine nucleotides (TGNs) in erythrocytes after chemical derivatisation. Erythrocytes are not the target tissue and TGN levels show poor correlations with clinical response. We have developed a sensitive assay to quantify deoxythioguanosine (dTG) without derivatisation in the DNA of nucleated blood cells. Using liquid chromatography and detection by tandem mass spectrometry, an intra- and inter-assay variability below 7.8% and 17.0% respectively were achieved. The assay had a detection limit of 0.0003125ng (1.1 femtomoles) dTG and was quantified in DNA samples relative to endogenous deoxyadenosine (dA) in a small group of 20 patients with inflammatory bowel disease, all of whom had been established on azathioprine (AZA) therapy for more than 25 weeks. These patients had dTG levels of 20-1360mol dTG/10(6)mol dA; three patients who had not started therapy had no detectable dTG. This method, comparable to previous methods in sensitivity, enables the direct detection of a cytotoxic thiopurine metabolite without derivatisation in an easily obtainable, stable sample and will facilitate a better understanding of the mechanisms of action of these inexpensive yet effective drugs.

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

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