Blood donor exposome and impact of common drugs on red blood cell metabolism.

Computational models based on recent maps of the RBC proteome suggest that mature erythrocytes may harbor targets for common drugs. This prediction is relevant to RBC storage in the blood bank, in which the impact of small molecule drugs or other xenometabolites deriving from dietary, iatrogenic, or environmental exposures ("exposome") may alter erythrocyte energy and redox metabolism and, in so doing, affect red cell storage quality and posttransfusion efficacy. To test this prediction, here we provide a comprehensive characterization of the blood donor exposome, including the detection of common prescription and over-the-counter drugs in blood units donated by 250 healthy volunteers in the Recipient Epidemiology and Donor Evaluation Study III Red Blood Cell-Omics (REDS-III RBC-Omics) Study. Based on high-throughput drug screenings of 1366 FDA-approved drugs, we report that approximately 65% of the tested drugs had an impact on erythrocyte metabolism. Machine learning models built using metabolites as predictors were able to accurately predict drugs for several drug classes/targets (bisphosphonates, anticholinergics, calcium channel blockers, adrenergics, proton pump inhibitors, antimetabolites, selective serotonin reuptake inhibitors, and mTOR), suggesting that these drugs have a direct, conserved, and substantial impact on erythrocyte metabolism. As a proof of principle, here we show that the antacid ranitidine - though rarely detected in the blood donor population - has a strong effect on RBC markers of storage quality in vitro. We thus show that supplementation of blood units stored in bags with ranitidine could - through mechanisms involving sphingosine 1-phosphate-dependent modulation of erythrocyte glycolysis and/or direct binding to hemoglobin - improve erythrocyte metabolism and storage quality.

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.

Development and evaluation of a transfusion medicine genome wide genotyping array.

BACKGROUND: Many aspects of transfusion medicine are affected by genetics. Current single-nucleotide polymorphism (SNP) arrays are limited in the number of targets that can be interrogated and cannot detect all variation of interest. We designed a transfusion medicine array (TM-Array) for study of both common and rare transfusion-relevant variations in genetically diverse donor and recipient populations. STUDY DESIGN AND METHODS: The array was designed by conducting extensive bioinformatics mining and consulting experts to identify genes and genetic variation related to a wide range of transfusion medicine clinical relevant and research-related topics. Copy number polymorphisms were added in the alpha globin, beta globin, and Rh gene clusters. RESULTS: The final array contains approximately 879,000 SNP and copy number polymorphism markers. Over 99% of SNPs were called reliably. Technical replication showed the array to be robust and reproducible, with an error rate less than 0.03%. The array also had a very low Mendelian error rate (average parent-child trio accuracy of 0.9997). Blood group results were in concordance with serology testing results, and the array accurately identifies rare variants (minor allele frequency of 0.5%). The array achieved high genome-wide imputation coverage for African-American (97.5%), Hispanic (96.1%), East Asian (94.6%), and white (96.1%) genomes at a minor allele frequency of 5%. CONCLUSIONS: A custom array for transfusion medicine research has been designed and evaluated. It gives wide coverage and accurate identification of rare SNPs in diverse populations. The TM-Array will be useful for future genetic studies in the diverse fields of transfusion medicine research.

Systems biology as an emerging paradigm in transfusion medicine.

Blood transfusions are an important part of modern medicine, delivering approximately 85 million blood units to patients annually. Recently, the field of transfusion medicine has started to benefit from the "omic" data revolution and corresponding systems biology analytics. The red blood cell is the simplest human cell, making it an accessible starting point for the application of systems biology approaches.In this review, we discuss how the use of systems biology has led to significant contributions in transfusion medicine, including the identification of three distinct metabolic states that define the baseline decay process of red blood cells during storage. We then describe how a series of perturbations to the standard storage conditions characterized the underlying metabolic phenotypes. Finally, we show how the analysis of high-dimensional data led to the identification of predictive biomarkers.The transfusion medicine community is in the early stages of a paradigm shift, moving away from the measurement of a handful of chosen variables to embracing systems biology and a cell-scale point of view.

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

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

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

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

Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics.

The increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed "unsteady-state flux balance analysis" (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBA predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through 13C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.

Systems biology analysis of drivers underlying hallmarks of cancer cell metabolism.

Malignant transformation is often accompanied by significant metabolic changes. To identify drivers underlying these changes, we calculated metabolic flux states for the NCI60 cell line collection and correlated the variance between metabolic states of these lines with their other properties. The analysis revealed a remarkably consistent structure underlying high flux metabolism. The three primary uptake pathways, glucose, glutamine and serine, are each characterized by three features: (1) metabolite uptake sufficient for the stoichiometric requirement to sustain observed growth, (2) overflow metabolism, which scales with excess nutrient uptake over the basal growth requirement, and (3) redox production, which also scales with nutrient uptake but greatly exceeds the requirement for growth. We discovered that resistance to chemotherapeutic drugs in these lines broadly correlates with the amount of glucose uptake. These results support an interpretation of the Warburg effect and glutamine addiction as features of a growth state that provides resistance to metabolic stress through excess redox and energy production. Furthermore, overflow metabolism observed may indicate that mitochondrial catabolic capacity is a key constraint setting an upper limit on the rate of cofactor production possible. These results provide a greater context within which the metabolic alterations in cancer can be understood.

Citrate metabolism in red blood cells stored in additive solution-3.

BACKGROUND: Red blood cells (RBCs) are thought to have a relatively simple metabolic network compared to other human cell types. Recent proteomics reports challenge the notion that RBCs are mere hemoglobin carriers with limited metabolic activity. Expanding our understanding of RBC metabolism has key implications in many biomedical areas, including transfusion medicine. STUDY DESIGN AND METHODS: In-gel digestion coupled with mass spectrometric analysis proteomics approaches were combined with state-of-the-art tracing experiments by incubating leukofiltered RBCs in additive solution-3 for up to 42 days under blood bank conditions, in presence of 13 C1,2,3 -glucose, 2,2,4,4-d-citrate, and 13 C,15 N-glutamine. RESULTS: Results indicate that the pentose phosphate pathway/glycolysis ratio increases during storage in additive solution-3. While the majority of supernatant glucose is consumed to fuel glycolysis, incorporation of glucose-derived pentose phosphate moieties was observed in nucleoside monophosphates. Incubation with deuterated citrate indicated that citrate uptake and metabolism contribute to explain the origin of up to approximately 20% to 30% lactate that could not be explained by glucose oxidation and 2,3-diphosphoglycerate consumption alone. Incubation with 13 C,15 N-glutamine showed that glutaminolysis fuels transamination reactions and accumulation of millimolar levels of 5-oxoproline, while de novo glutathione synthesis was not significantly active during refrigerated storage. CONCLUSION: Quantitative tracing metabolic experiments revealed that mature RBCs can metabolize other substrates than glucose, such as citrate, an observation relevant to transfusion medicine (i.e., formulation of novel additives), and other research endeavors where metabolic modulation of RBCs opens potential avenues for therapeutic interventions, such as in sickle cell disease.

A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism.

Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production. The models accurately predict growth phenotypes and known auxotrophies in CHO cells. With the models, we quantify the protein synthesis capacity of CHO cells and demonstrate that common bioprocess treatments, such as histone deacetylase inhibitors, inefficiently increase product yield. However, our simulations show that the metabolic resources in CHO are more than three times more efficiently utilized for growth or recombinant protein synthesis following targeted efforts to engineer the CHO secretory pathway. This model will further accelerate CHO cell engineering and help optimize bioprocesses.

Multi-omic data integration enables discovery of hidden biological regularities.

Rapid growth in size and complexity of biological data sets has led to the 'Big Data to Knowledge' challenge. We develop advanced data integration methods for multi-level analysis of genomic, transcriptomic, ribosomal profiling, proteomic and fluxomic data. First, we show that pairwise integration of primary omics data reveals regularities that tie cellular processes together in Escherichia coli: the number of protein molecules made per mRNA transcript and the number of ribosomes required per translated protein molecule. Second, we show that genome-scale models, based on genomic and bibliomic data, enable quantitative synchronization of disparate data types. Integrating omics data with models enabled the discovery of two novel regularities: condition invariant in vivo turnover rates of enzymes and the correlation of protein structural motifs and translational pausing. These regularities can be formally represented in a computable format allowing for coherent interpretation and prediction of fitness and selection that underlies cellular physiology.

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

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

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

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

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism.

Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.

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

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

Pharmacogenomic and clinical data link non-pharmacokinetic metabolic dysregulation to drug side effect pathogenesis.

Drug side effects cause a significant clinical and economic burden. However, mechanisms of drug action underlying side effect pathogenesis remain largely unknown. Here, we integrate pharmacogenomic and clinical data with a human metabolic network and find that non-pharmacokinetic metabolic pathways dysregulated by drugs are linked to the development of side effects. We show such dysregulated metabolic pathways contain genes with sequence variants affecting side effect incidence, play established roles in pathophysiology, have significantly altered activity in corresponding diseases, are susceptible to metabolic inhibitors and are effective targets for therapeutic nutrient supplementation. Our results indicate that metabolic dysregulation represents a common mechanism underlying side effect pathogenesis that is distinct from the role of metabolism in drug clearance. We suggest that elucidating the relationships between the cellular response to drugs, genetic variation of patients and cell metabolism may help managing side effects by personalizing drug prescriptions and nutritional intervention strategies.

Personalized Whole-Cell Kinetic Models of Metabolism for Discovery in Genomics and Pharmacodynamics.

Understanding individual variation is fundamental to personalized medicine. Yet interpreting complex phenotype data, such as multi-compartment metabolomic profiles, in the context of genotype data for an individual is complicated by interactions within and between cells and remains an unresolved challenge. Here, we constructed multi-omic, data-driven, personalized whole-cell kinetic models of erythrocyte metabolism for 24 healthy individuals based on fasting-state plasma and erythrocyte metabolomics and whole-genome genotyping. We show that personalized kinetic rate constants, rather than metabolite levels, better represent the genotype. Additionally, changes in erythrocyte dynamics between individuals occur on timescales of circulation, suggesting detected differences play a role in physiology. Finally, we use the models to identify individuals at risk for a drug side effect (ribavirin-induced anemia) and how genetic variation (inosine triphosphatase deficiency) may protect against this side effect. This study demonstrates the feasibility of personalized kinetic models, and we anticipate their use will accelerate discoveries in characterizing individual metabolic variation.

A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks.

Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH), succinate dehydrogenase (SDH), and fumarate hydratase (FH) that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes), expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers.