A guide to 13C metabolic flux analysis for the cancer biologist. Exp Mol Med. 2018;50:1-13. [PMID: 29657327 PMCID: PMC5938039 DOI: 10.1038/s12276-018-0060-y]

Regulatory mechanisms of one-carbon metabolism enzymes

One-carbon metabolism is a central metabolic pathway critical for the biosynthesis of several amino acids, methyl group donors, and nucleotides. The pathway mostly relies on the transfer of a carbon unit from the amino acid serine, through the cofactor folate (in its several forms), and to the ultimate carbon acceptors that include nucleotides and methyl groups used for methylation of proteins, RNA, and DNA. Nucleotides are required for DNA replication, DNA repair, gene expression, and protein translation, through ribosomal RNA. Therefore, the one-carbon metabolism pathway is essential for cell growth and function in all cells, but is specifically important for rapidly proliferating cells. The regulation of one-carbon metabolism is a critical aspect of the normal and pathological function of the pathway, such as in cancer, where hijacking these regulatory mechanisms feeds an increased need for nucleotides. One-carbon metabolism is regulated at several levels: via gene expression, posttranslational modification, subcellular compartmentalization, allosteric inhibition, and feedback regulation. In this review, we aim to inform the readers of relevant one-carbon metabolism regulation mechanisms and to bring forward the need to further study this aspect of one-carbon metabolism. The review aims to integrate two major aspects of cancer metabolism-signaling downstream of nutrient sensing and one-carbon metabolism, because while each of these is critical for the proliferation of cancerous cells, their integration is critical for comprehensive understating of cellular metabolism in transformed cells and can lead to clinically relevant insights.

A reversible state of hypometabolism in a human cellular model of sporadic Parkinson's disease

Sporadic Parkinson's Disease (sPD) is a progressive neurodegenerative disorder caused by multiple genetic and environmental factors. Mitochondrial dysfunction is one contributing factor, but its role at different stages of disease progression is not fully understood. Here, we showed that neural precursor cells and dopaminergic neurons derived from induced pluripotent stem cells (hiPSCs) from sPD patients exhibited a hypometabolism. Further analysis based on transcriptomics, proteomics, and metabolomics identified the citric acid cycle, specifically the α-ketoglutarate dehydrogenase complex (OGDHC), as bottleneck in sPD metabolism. A follow-up study of the patients approximately 10 years after initial biopsy demonstrated a correlation between OGDHC activity in our cellular model and the disease progression. In addition, the alterations in cellular metabolism observed in our cellular model were restored by interfering with the enhanced SHH signal transduction in sPD. Thus, inhibiting overactive SHH signaling may have potential as neuroprotective therapy during early stages of sPD.

Metabolic flux reprogramming in Mycobacterium tuberculosis-infected human macrophages

Metabolic fluxes are at the heart of metabolism and growth in any living system. During tuberculosis (TB) infection, the pathogenic Mycobacterium tuberculosis (Mtb) adapts its nutritional behaviour and metabolic fluxes to survive in human macrophages and cause infection. The infected host cells also undergo metabolic changes. However, our knowledge of the infected host metabolism and identification of the reprogrammed metabolic flux nodes remains limited. In this study, we applied systems-based 13C-metabolic flux analysis (MFA) to measure intracellular carbon metabolic fluxes in Mtb-infected human THP-1 macrophages. We provide a flux map for infected macrophages that quantified significantly increased fluxes through glycolytic fluxes towards pyruvate synthesis and reduced pentose phosphate pathway fluxes when compared to uninfected macrophages. The tri carboxylic acid (TCA) cycle fluxes were relatively low, and amino acid fluxes were reprogrammed upon Mtb infection. The knowledge of host metabolic flux profiles derived from our work expands on how the host cell adapts its carbon metabolism in response to Mtb infection and highlights important nodes that may provide targets for developing new therapeutics to improve TB treatment.

A Markov constraint to uniquely identify elementary flux mode weights in unimolecular metabolic networks

Elementary flux modes (EFMs) are minimal, steady state pathways characterizing a flux network. Fundamentally, all steady state fluxes in a network are decomposable into a linear combination of EFMs. While there is typically no unique set of EFM weights that reconstructs these fluxes, several optimization-based methods have been proposed to constrain the solution space by enforcing some notion of parsimony. However, it has long been recognized that optimization-based approaches may fail to uniquely identify EFM weights and return different feasible solutions across objective functions and solvers. Here we show that, for flux networks only involving single molecule transformations, these problems can be avoided by imposing a Markovian constraint on EFM weights. Our Markovian constraint guarantees a unique solution to the flux decomposition problem, and that solution is arguably more biophysically plausible than other solutions. We describe an algorithm for computing Markovian EFM weights via steady state analysis of a certain discrete-time Markov chain, based on the flux network, which we call the cycle-history Markov chain. We demonstrate our method with a differential analysis of EFM activity in a lipid metabolic network comparing healthy and Alzheimer's disease patients. Our method is the first to uniquely decompose steady state fluxes into EFM weights for any unimolecular metabolic network.

Quantification and isotope abundance determination of 13C labeled intracellular sugar metabolites with hydrophilic interaction liquid chromatography

Metabolic flux analysis (MFA) using stable isotope labeled tracers is a powerful tool to estimate fluxes through metabolic pathways. It finds applications in studying metabolic changes in diseases, regulation of cellular energetics, and novel strategies for metabolic engineering. Accurate and precise quantification of the concentration of metabolites and their labeling states is critical for correct MFA results. Utilizing an ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) system, an analytical method for simultaneously quantifying the concentration of sugar metabolites and their mass isotopologue distribution (MID) was developed. The method performs with good linearity and coefficient of determination (R2) > 0.99, while the detection limit ranged from 0.1 to 50 mg L-1. Seven sugar metabolites were detected in a labeled Brevibacterium flavum sample using the method. The detected quantities ranged from 6.15 to 3704.21 mg L-1, and 13C abundance was between 12.77% and 66.67% in the fermentation fluid and 16.28% and 91.93% in the bacterial body. Overall, the method is efficient, accurate, and suitable for analysis of labeled sugar metabolites in 13C MFA studies.

Integrative teaching of metabolic modeling and flux analysis with interactive python modules

The modeling of rates of biochemical reactions-fluxes-in metabolic networks is widely used for both basic biological research and biotechnological applications. A number of different modeling methods have been developed to estimate and predict fluxes, including kinetic and constraint-based (Metabolic Flux Analysis and flux balance analysis) approaches. Although different resources exist for teaching these methods individually, to-date no resources have been developed to teach these approaches in an integrative way that equips learners with an understanding of each modeling paradigm, how they relate to one another, and the information that can be gleaned from each. We have developed a series of modeling simulations in Python to teach kinetic modeling, metabolic control analysis, 13C-metabolic flux analysis, and flux balance analysis. These simulations are presented in a series of interactive notebooks with guided lesson plans and associated lecture notes. Learners assimilate key principles using models of simple metabolic networks by running simulations, generating and using data, and making and validating predictions about the effects of modifying model parameters. We used these simulations as the hands-on computer laboratory component of a four-day metabolic modeling workshop and participant survey results showed improvements in learners' self-assessed competence and confidence in understanding and applying metabolic modeling techniques after having attended the workshop. The resources provided can be incorporated in their entirety or individually into courses and workshops on bioengineering and metabolic modeling at the undergraduate, graduate, or postgraduate level.

Changes in Plasma Pyruvate and TCA Cycle Metabolites upon Increased Hepatic Fatty Acid Oxidation and Ketogenesis in Male Wistar Rats

Altered hepatic mitochondrial fatty acid β-oxidation and associated tricarboxylic acid (TCA) cycle activity contributes to lifestyle-related diseases, and circulating biomarkers reflecting these changes could have disease prognostic value. This study aimed to determine hepatic and systemic changes in TCA-cycle-related metabolites upon the selective pharmacologic enhancement of mitochondrial fatty acid β-oxidation in the liver, and to elucidate the mechanisms and potential markers of hepatic mitochondrial activity. Male Wistar rats were treated with 3-thia fatty acids (e.g., tetradecylthioacetic acid (TTA)), which target mitochondrial biogenesis, mitochondrial fatty acid β-oxidation, and ketogenesis predominantly in the liver. Hepatic and plasma concentrations of TCA cycle intermediates and anaplerotic substrates (LC-MS/MS), plasma ketones (colorimetric assay), and acylcarnitines (HPLC-MS/MS), along with associated TCA-cycle-related gene expression (qPCR) and enzyme activities, were determined. TTA-induced hepatic fatty acid β-oxidation resulted in an increased ratio of plasma ketone bodies/nonesterified fatty acid (NEFA), lower plasma malonyl-CoA levels, and a higher ratio of plasma acetylcarnitine/palmitoylcarnitine (C2/C16). These changes were associated with decreased hepatic and increased plasma pyruvate concentrations, and increased plasma concentrations of succinate, malate, and 2-hydroxyglutarate. Expression of several genes encoding TCA cycle enzymes and the malate-oxoglutarate carrier (Slc25a11), glutamate dehydrogenase (Gdh), and malic enzyme (Mdh1 and Mdh2) were significantly increased. In conclusion, the induction of hepatic mitochondrial fatty acid β-oxidation by 3-thia fatty acids lowered hepatic pyruvate while increasing plasma pyruvate, as well as succinate, malate, and 2-hydroxyglutarate.

CO2 electroreduction favors carbon isotope 12C over 13C and facilitates isotope separation

We discovered that CO2 electroreduction strongly favors the conversion of the dominant isotope of carbon (12C) and discriminates against the less abundant, stable carbon 13C isotope. Both absorption of CO2 in the alkaline electrolyte and CO2 electrochemical reduction favor the lighter isotopologue. As a result, the stream of unreacted CO2 leaving the electrolyzer has an increased 13C content, and the depletion of 13C in the product is several times greater than that of photosynthesis. Using a natural abundance feed, we demonstrate enriching of the 13C fraction to ∼1.3% (i.e., +18%) in a single-pass reactor and propose a scalable and economically attractive process to yield isotopes of a commercial purity. Our finding opens pathways to both cheaper and less energy-intensive production of stable isotopes (13C, 15N) essential to the healthcare and chemistry research, and to an economically viable, disruptive application of electrolysis technologies developed in the context of sustainability transition.

Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model

It is increasingly apparent that cancer cells, in addition to remodelling their metabolism to survive and proliferate, adapt and manipulate the metabolism of other cells. This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising in-vitro mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. Although this is increasingly recognised, and work towards addressing the issue is becoming routinary much remains poorly understood. For instance, knowledge regarding the biochemical mechanisms through which cancer cells manipulate non-cancerous cell metabolism, and the subsequent impact on their survival and proliferation remains limited. Additionally, the variations in these processes across different cancer types and progression stages, and their implications for therapy, also remain largely unexplored. This study employs an interdisciplinary approach that leverages the predictive power of mathematical modelling to enrich experimental findings. We develop a functional multicellular in-silico model that facilitates the qualitative and quantitative analysis of the metabolic network spawned by an in-vitro co-culture model of bone marrow mesenchymal stem- and myeloma cell lines. To procure this model, we devised a bespoke human genome constraint-based reconstruction workflow that combines aspects from the legacy mCADRE & Metabotools algorithms, the novel redHuman algorithm, along with 13C-metabolic flux analysis. Our workflow transforms the latest human metabolic network matrix (Recon3D) into two cell-specific models coupled with a metabolic network spanning a shared growth medium. When cross-validating our in-silico model against the in-vitro model, we found that the in-silico model successfully reproduces vital metabolic behaviours of its in-vitro counterpart; results include cell growth predictions, respiration rates, as well as support for observations which suggest cross-shuttling of redox-active metabolites between cells.

The Fe-S cluster assembly protein IscU2 increases α-ketoglutarate catabolism and DNA 5mC to promote tumor growth

IscU2 is a scaffold protein that is critical for the assembly of iron-sulfur (Fe-S) clusters and the functions of Fe-S-containing mitochondrial proteins. However, the role of IscU2 in tumor development remains unclear. Here, we demonstrated that IscU2 expression is much higher in human pancreatic ductal adenocarcinoma (PDAC) tissues than in adjacent normal pancreatic tissues. In PDAC cells, activated KRAS enhances the c-Myc-mediated IscU2 transcription. The upregulated IscU2 stabilizes Fe-S cluster and regulates the activity of tricarboxylic acid (TCA) cycle enzymes α-ketoglutarate (α-KG) dehydrogenase and aconitase 2, which promote α-KG catabolism through oxidative and reductive TCA cycling, respectively. In addition to promoting mitochondrial functions, activated KRAS-induced and IscU2-dependent acceleration of α-KG catabolism results in reduced α-KG levels in the cytosol and nucleus, leading to an increase in DNA 5mC due to Tet methylcytosine dioxygenase 3 (TET3) inhibition and subsequent expression of genes including DNA polymerase alpha 1 catalytic subunit for PDAC cell proliferation and tumor growth in mice. These findings underscore a critical role of IscU2 in KRAS-promoted α-KG catabolism, 5mC-dependent gene expression, and PDAC growth and highlight the instrumental and integrated regulation of mitochondrial functions and gene expression by IscU2 in PDAC cells.

Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives

BACKGROUND

Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge.

AIM

This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway.

KEY SCIENTIFIC CONCEPTS

Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.

Lactate-dependent transcriptional regulation controls mammalian eye morphogenesis

Mammalian retinal metabolism favors aerobic glycolysis. However, the role of glycolytic metabolism in retinal morphogenesis remains unknown. We report that aerobic glycolysis is necessary for the early stages of retinal development. Taking advantage of an unbiased approach that combines the use of eye organoids and single-cell RNA sequencing, we identify specific glucose transporters and glycolytic genes in retinal progenitors. Next, we determine that the optic vesicle territory of mouse embryos displays elevated levels of glycolytic activity. At the functional level, we show that removal of Glucose transporter 1 and Lactate dehydrogenase A gene activity from developing retinal progenitors arrests eye morphogenesis. Surprisingly, we uncover that lactate-mediated upregulation of key eye-field transcription factors is controlled by the epigenetic modification of histone H3 acetylation through histone deacetylase activity. Our results identify an unexpected bioenergetic independent role of lactate as a signaling molecule necessary for mammalian eye morphogenesis.

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 reprogramming and altered cell envelope characteristics in a pentose phosphate pathway mutant increases MRSA resistance to β-lactam antibiotics

Central metabolic pathways control virulence and antibiotic resistance, and constitute potential targets for antibacterial drugs. In Staphylococcus aureus the role of the pentose phosphate pathway (PPP) remains largely unexplored. Mutation of the 6-phosphogluconolactonase gene pgl, which encodes the only non-essential enzyme in the oxidative phase of the PPP, significantly increased MRSA resistance to β-lactam antibiotics, particularly in chemically defined media with physiologically-relevant concentrations of glucose, and reduced oxacillin (OX)-induced lysis. Expression of the methicillin-resistance penicillin binding protein 2a and peptidoglycan architecture were unaffected. Carbon tracing and metabolomics revealed extensive metabolic reprogramming in the pgl mutant including increased flux to glycolysis, the TCA cycle, and several cell envelope precursors, which was consistent with increased β-lactam resistance. Morphologically, pgl mutant cells were smaller than wild-type with a thicker cell wall and ruffled surface when grown in OX. The pgl mutation reduced resistance to Congo Red, sulfamethoxazole and oxidative stress, and increased resistance to targocil, fosfomycin and vancomycin. Levels of lipoteichoic acids (LTAs) were significantly reduced in pgl, which may limit cell lysis, while the surface charge of pgl cells was significantly more positive. A vraG mutation in pgl reversed the increased OX resistance phenotype, and partially restored wild-type surface charge, but not LTA levels. Mutations in vraF or graRS from the VraFG/GraRS complex that regulates DltABCD-mediated d-alanylation of teichoic acids (which in turn controls β-lactam resistance and surface charge), also restored wild-type OX susceptibility. Collectively these data show that reduced levels of LTAs and OX-induced lysis combined with a VraFG/GraRS-dependent increase in cell surface positive charge are accompanied by significantly increased OX resistance in an MRSA pgl mutant.

Unlocking the secrets of the microbiome: exploring the dynamic microbial interplay with humans through metabolomics and their manipulation for synthetic biology applications

Metabolomics is a powerful research discovery tool with the potential to measure hundreds to low thousands of metabolites. In this review, we discuss the application of GC-MS and LC-MS in discovery-based metabolomics research, we define metabolomics workflows and we highlight considerations that need to be addressed in order to generate robust and reproducible data. We stress that metabolomics is now routinely applied across the biological sciences to study microbiomes from relatively simple microbial systems to their complex interactions within consortia in the host and the environment and highlight this in a range of biological species and mammalian systems including humans. However, challenges do still exist that need to be overcome to maximise the potential for metabolomics to help us understanding biological systems. To demonstrate the potential of the approach we discuss the application of metabolomics in two broad research areas: (1) synthetic biology to increase the production of high-value fine chemicals and reduction in secondary by-products and (2) gut microbial interaction with the human host. While burgeoning in importance, the latter is still in its infancy and will benefit from the development of tools to detangle host-gut-microbial interactions and their impact on human health and diseases.

Real-Time Volatile Metabolomics Analysis of Dendritic Cells

Dendritic cells (DCs) actively sample and present antigen to cells of the adaptive immune system and are thus vital for successful immune control and memory formation. Immune cell metabolism and function are tightly interlinked, and a better understanding of this interaction offers potential to develop immunomodulatory strategies. However, current approaches for assessing the immune cell metabolome are often limited by end-point measurements, may involve laborious sample preparation, and may lack unbiased, temporal resolution of the metabolome. In this study, we present a novel setup coupled to a secondary electrospray ionization-high resolution mass spectrometric (SESI-HRMS) platform allowing headspace analysis of immature and activated DCs in real-time with minimal sample preparation and intervention, with high technical reproducibility and potential for automation. Distinct metabolic signatures of DCs treated with different supernatants (SNs) of bacterial cultures were detected during real-time analyses over 6 h compared to their respective controls (SN only). Furthermore, the technique allowed for the detection of ¹³C-incorporation into volatile metabolites, opening the possibility for real-time tracing of metabolic pathways in DCs. Moreover, differences in the metabolic profile of naı̈ve and activated DCs were discovered, and pathway-enrichment analysis revealed three significantly altered pathways, including the TCA cycle, α-linolenic acid metabolism, and valine, leucine, and isoleucine degradation.

Cellular redox homeostasis maintained by malic enzyme 2 is essential for MYC-driven T cell lymphomagenesis

T cell lymphomas (TCLs) are a group of rare and heterogeneous tumors. Although proto-oncogene MYC has an important role in driving T cell lymphomagenesis, whether MYC carries out this function remains poorly understood. Here, we show that malic enzyme 2 (ME2), one of the NADPH-producing enzymes associated with glutamine metabolism, is essential for MYC-driven T cell lymphomagenesis. We establish a CD4-Cre; Myc flox/+transgenic mouse mode, and approximately 90% of these mice develop TCL. Interestingly, knockout of Me2 in Myc transgenic mice almost completely suppresses T cell lymphomagenesis. Mechanistically, by transcriptionally up-regulating ME2, MYC maintains redox homeostasis, thereby increasing its tumorigenicity. Reciprocally, ME2 promotes MYC translation by stimulating mTORC1 activity through adjusting glutamine metabolism. Treatment with rapamycin, an inhibitor of mTORC1, blocks the development of TCL both in vitro and in vivo. Therefore, our findings identify an important role for ME2 in MYC-driven T cell lymphomagenesis and reveal that MYC-ME2 circuit may be an effective target for TCL therapy.

Systematic comparison of local approaches for isotopically nonstationary metabolic flux analysis

Quantification of reaction fluxes of metabolic networks can help us understand how the integration of different metabolic pathways determine cellular functions. Yet, intracellular fluxes cannot be measured directly but are estimated with metabolic flux analysis (MFA) that relies on the patterns of isotope labeling of metabolites in the network. For metabolic systems, typical for plants, where all potentially labeled atoms effectively have only one source atom pool, only isotopically nonstationary MFA can provide information about intracellular fluxes. There are several global approaches that implement MFA for an entire metabolic network and estimate, at once, a steady-state flux distribution for all reactions with identifiable fluxes in the network. In contrast, local approaches deal with estimation of fluxes for a subset of reactions, with smaller data demand for flux estimation. Here we present a systematic comparative review and benchmarking of the existing local approaches for isotopically nonstationary MFA. The comparison is conducted with respect to the required data and underlying computational problems solved on a synthetic network example. Furthermore, we benchmark the performance of these approaches in estimating fluxes for a subset of reactions using data obtained from the simulation of nitrogen fluxes in the Arabidopsis thaliana core metabolism. The findings pinpoint practical aspects that need to be considered when applying local approaches for flux estimation in large-scale plant metabolic networks.

Optimization of energy production and central carbon metabolism in a non-respiring eukaryote

Most eukaryotes respire oxygen, using it to generate biomass and energy. However, a few organisms have lost the capacity to respire. Understanding how they manage biomass and energy production may illuminate the critical points at which respiration feeds into central carbon metabolism and explain possible routes to its optimization. Here, we use two related fission yeasts, Schizosaccharomyces pombe and Schizosaccharomyces japonicus, as a comparative model system. We show that although S. japonicus does not respire oxygen, unlike S. pombe, it is capable of efficient NADH oxidation, amino acid synthesis, and ATP generation. We probe possible optimization strategies through the use of stable isotope tracing metabolomics, mass isotopologue distribution analysis, genetics, and physiological experiments. S. japonicus appears to have optimized cytosolic NADH oxidation via glycerol-3-phosphate synthesis. It runs a fully bifurcated TCA pathway, sustaining amino acid production. Finally, we propose that it has optimized glycolysis to maintain high ATP/ADP ratio, in part by using the pentose phosphate pathway as a glycolytic shunt, reducing allosteric inhibition of glycolysis and supporting biomass generation. By comparing two related organisms with vastly different metabolic strategies, our work highlights the versatility and plasticity of central carbon metabolism in eukaryotes, illuminating critical adaptations supporting the preferential use of glycolysis over oxidative phosphorylation.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

LKB1-Dependent Regulation of TPI1 Creates a Divergent Metabolic Liability between Human and Mouse Lung Adenocarcinoma

KRAS is the most frequently mutated oncogene in human lung adenocarcinomas (hLUAD), and activating mutations frequently co-occur with loss-of-function mutations in TP53 or STK11/LKB1. However, mutation of all three genes is rarely observed in hLUAD, even though engineered comutation is highly aggressive in mouse lung adenocarcinoma (mLUAD). Here, we provide a mechanistic explanation for this difference by uncovering an evolutionary divergence in the regulation of triosephosphate isomerase (TPI1). In hLUAD, TPI1 activity is regulated via phosphorylation at Ser21 by the salt inducible kinases (SIK) in an LKB1-dependent manner, modulating flux between the completion of glycolysis and production of glycerol lipids. In mice, Ser21 of TPI1 is a Cys residue that can be oxidized to alter TPI1 activity without a need for SIKs or LKB1. Our findings suggest this metabolic flexibility is critical in rapidly growing cells with KRAS and TP53 mutations, explaining why the loss of LKB1 creates a liability in these tumors.

SIGNIFICANCE

Utilizing phosphoproteomics and metabolomics in genetically engineered human cell lines and genetically engineered mouse models (GEMM), we uncover an evolutionary divergence in metabolic regulation within a clinically relevant genotype of human LUAD with therapeutic implications. Our data provide a cautionary example of the limits of GEMMs as tools to study human diseases such as cancers. This article is highlighted in the In This Issue feature, p. 799.

Model Validation and Selection in Metabolic Flux Analysis and Flux Balance Analysis

13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both of these methods use metabolic reaction network models of metabolism operating at steady state, so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. A number of approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to decide on and/or discriminate between alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ2-test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how the adoption of robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology in particular.

FAMetA: a mass isotopologue-based tool for the comprehensive analysis of fatty acid metabolism

The use of stable isotope tracers and mass spectrometry (MS) is the gold standard method for the analysis of fatty acid (FA) metabolism. Yet, current state-of-the-art tools provide limited and difficult-to-interpret information about FA biosynthetic routes. Here we present FAMetA, an R package and a web-based application (www.fameta.es) that uses 13C mass isotopologue profiles to estimate FA import, de novo lipogenesis, elongation and desaturation in a user-friendly platform. The FAMetA workflow covers the required functionalities needed for MS data analyses. To illustrate its utility, different in vitro and in vivo experimental settings are used in which FA metabolism is modified. Thanks to the comprehensive characterization of FA biosynthesis and the easy-to-interpret graphical representations compared to previous tools, FAMetA discloses unnoticed insights into how cells reprogram their FA metabolism and, when combined with FASN, SCD1 and FADS2 inhibitors, it enables the identification of new FAs by the metabolic reconstruction of their synthesis route.

13C-Metabolic flux analysis of 3T3-L1 adipocytes illuminates its core metabolism under hypoxia

Hypoxia has been identified as a major factor in the pathogenesis of adipose tissue inflammation, which is a hallmark of obesity and obesity-linked type 2 diabetes mellitus. In this study, we have investigated the impact of hypoxia (1% oxygen) on the physiology and metabolism of 3T3-L1 adipocytes, a widely used cell culture model of adipose. Specifically, we applied parallel labeling experiments, isotopomer spectral analysis, and ¹³C-metabolic flux analysis to quantify the impact of hypoxia on adipogenesis, de novo lipogenesis and metabolic flux reprogramming in adipocytes. We found that 3T3-L1 cells can successfully differentiate into lipid-accumulating adipocytes under hypoxia, although the production of lipids was reduced by about 40%. Quantitative flux analysis demonstrated that short-term (1 day) and long-term (7 days) exposure to hypoxia resulted in similar reprogramming of cellular metabolism. Overall, we found that hypoxia: 1) reduced redox and energy generation by more than 2-fold and altered the patterns of metabolic pathway contributions to production and consumption of energy and redox cofactors; 2) redirected glucose metabolism from pentose phosphate pathway and citric acid cycle to lactate production; 3) rewired glutamine metabolism, from net glutamine production to net glutamine catabolism; 4) suppressed branched chain amino acid consumption; and 5) reduced biosynthesis of odd-chain fatty acids and mono-unsaturated fatty acids, while synthesis of saturated even-chain fatty acids was not affected. Together, these results highlight the profound impact of extracellular microenvironment on adipocyte metabolic activity and function.

Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale

The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by ¹³C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.

Progress in understanding and improving oil content and quality in seeds

The world's population is projected to increase by two billion by 2050, resulting in food and energy insecurity. Oilseed crops have been identified as key to address these challenges: they produce and store lipids in the seeds as triacylglycerols that can serve as a source of food/feed, renewable fuels, and other industrially-relevant chemicals. Therefore, improving seed oil content and composition has generated immense interest. Research efforts aiming to unravel the regulatory pathways involved in fatty acid synthesis and to identify targets for metabolic engineering have made tremendous progress. This review provides a summary of the current knowledge of oil metabolism and discusses how photochemical activity and unconventional pathways can contribute to high carbon conversion efficiency in seeds. It also highlights the importance of 13C-metabolic flux analysis as a tool to gain insights on the pathways that regulate oil biosynthesis in seeds. Finally, a list of key genes and regulators that have been recently targeted to enhance seed oil production are reviewed and additional possible targets in the metabolic pathways are proposed to achieve desirable oil content and quality.

1,2-13C2-Glucose Tracing Approach to Assess Metabolic Alterations of Human Monocytes under Neuroinflammatory Conditions

Neuroinflammation is one of the common features in most neurological diseases including multiple sclerosis (MScl) and neurodegenerative diseases such as Alzheimer's disease (AD). It is associated with local brain inflammation, microglial activation, and infiltration of peripheral immune cells into cerebrospinal fluid (CSF) and the central nervous system (CNS). It has been shown that the diversity of phenotypic changes in monocytes in CSF relates to neuroinflammation. It remains to be investigated whether these phenotypic changes are associated with functional or metabolic alteration, which may give a hint to their function or changes in cell states, e.g., cell activation. In this article, we investigate whether major metabolic pathways of blood monocytes alter after exposure to CSF of healthy individuals or patients with AD or MScl. Our findings show a significant alteration of the metabolism of monocytes treated with CSF from patients and healthy donors, including higher production of citric acid and glutamine, suggesting a more active glycolysis and tricarboxylic acid (TCA) cycle and reduced production of glycine and serine. These alterations suggest metabolic reprogramming of monocytes, possibly related to the change of compartment (from blood to CSF) and/or disease-related. Moreover, the levels of serine differ between AD and MScl, suggesting different phenotypic alterations between diseases.

NMR-Based Metabolomics to Evaluate Individual Response to Treatments

The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using ¹H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the ¹H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.

Isotopic Tracer for Absolute Quantification of Metabolites of the Pentose Phosphate Pathway in Bacteria

The pentose phosphate pathway (PPP) plays a key role in many metabolic functions, including the generation of NADPH, biosynthesis of nucleotides, and carbon homeostasis. In particular, the intermediates of PPP have been found to be significantly perturbed in bacterial metabolomic studies. Nonetheless, detailed analysis to gain mechanistic information of PPP metabolism remains limited as most studies are unable to report on the absolute levels of the metabolites. Absolute quantification of metabolites is a prerequisite to study the details of fluxes and its regulations. Isotope tracer or labeling studies are conducted in vivo and in vitro and have significantly improved the analysis and understanding of PPP. Due to the laborious procedure and limitations in the in vivo method, an in vitro approach known as Group Specific Internal Standard Technology (GSIST) has been successfully developed to measure the absolute levels of central carbon metabolism, including PPP. The technique adopts derivatization of an experimental sample and a corresponding internal standard with isotope-coded reagents to provide better precision for accurate identification and absolute quantification. In this review, we highlight bacterial studies that employed isotopic tracers as the tagging agents used for the absolute quantification analysis of PPP metabolites.

Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease

Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.

Cardio-Onco-Metabolism - Metabolic vulnerabilities in cancer and the heart

Cancer and cardiovascular diseases (CVDs) are the leading cause of death worldwide. Metabolic remodeling is a hallmark of both cancer and the failing heart. Tumors reprogram metabolism to optimize nutrient utilization and meet increased demands for energy provision, biosynthetic pathways, and proliferation. Shared risk factors for cancer and CVDs suggest intersecting mechanisms for disease pathogenesis and progression. In this review, we aim to highlight the role of metabolic remodeling in cancer and its potential to impair cardiac function. Understanding these mechanisms will help us develop biomarkers, better therapies, and identify patients at risk of developing heart disease after surviving cancer.

Tracing metabolic flux in vivo: basic model structures of tracer methodology

Molecules in living organisms are in a constant state of turnover at varying rates, i.e., synthesis, breakdown, oxidation, and/or conversion to different compounds. Despite the dynamic nature of biomolecules, metabolic research has focused heavily on static, snapshot information such as the abundances of mRNA, protein, and metabolites and/or (in)activation of molecular signaling, often leading to erroneous conclusions regarding metabolic status. Over the past century, stable, non-radioactive isotope tracers have been widely used to provide critical information on the dynamics of specific biomolecules (metabolites and polymers including lipids, proteins, and DNA), in studies in vitro in cells as well as in vivo in both animals and humans. In this review, we discuss (1) the historical background of the use of stable isotope tracer methodology in metabolic research; (2) the importance of obtaining kinetic information for a better understanding of metabolism; and (3) the basic principles and model structures of stable isotope tracer methodology using ¹³C-, ¹⁵N-, or ²H-labeled tracers.

Tagging biomolecules with stable isotopes of specific atoms can reveal details of the molecular inter-conversions of metabolism. The masses of the tracer isotopes used are greater than those of the more common atomic forms. This allows their movement through different metabolic pathways to be detected using mass spectrometry and modeling. Il-Young Kim at Gachon University School of Medicine in South Korea and colleagues focus their review on the use of stable, non-radioactive isotope tracers, especially, of carbon, nitrogen, and hydrogen, to study metabolism in live humans and other animals. They cover the basic model structures of tracer methodology that serve as the fundamental basis for various tracer methods available and the most recent applications. Their procedure is especially useful for monitoring the rates of metabolic inter-conversions, which can reveal aspects of health and disease.

Metabolic flux analysis: a comprehensive review on sample preparation, analytical techniques, data analysis, computational modelling, and main application areas

Metabolic flux analysis (MFA) quantitatively describes cellular fluxes to understand metabolic phenotypes and functional behaviour after environmental and/or genetic perturbations. In the last decade, the application of stable isotopes became extremely important to determine and integrate in vivo measurements of metabolic reactions in systems biology. 13C-MFA is one of the most informative methods used to study central metabolism of biological systems. This review aims to outline the current experimental procedure adopted in 13C-MFA, starting from the preparation of cell cultures and labelled tracers to the quenching and extraction of metabolites and their subsequent analysis performed with very powerful software. Here, the limitations and advantages of nuclear magnetic resonance spectroscopy and mass spectrometry techniques used in carbon labelled experiments are elucidated by reviewing the most recent published papers. Furthermore, we summarise the most successful approaches used for computational modelling in flux analysis and the main application areas with a particular focus in metabolic engineering.

Polystyrene micro and nano-particles induce metabolic rewiring in normal human colon cells: A risk factor for human health

Polystyrene is a thermoplastic polymer widely used in commercial products. Like all plastics, polystyrene can be degraded into microplastic and nanoplastic particles and ingested via food chain contamination. Although the ecological impact due to plastic contamination is well known, there are no studies indicating a carcinogenic potential of polystyrene microplastics (MPs) and nanoplastics (NPs). Here, we evaluated the effects of the MPs and NPs on normal human intestinal CCD-18Co cells. Our results show that internalization of NPs and MPs induces metabolic changes under both acute and chronic exposure by inducing oxidative stress, increasing glycolysis via lactate to sustain energy metabolism and glutamine metabolism to sustain anabolic processes. We also show that this decoupling of nutrients mirrors the effect of the potent carcinogenic agent azoxymethane and HCT15 colon cancer cells, carrying out the typical strategy of cancer cells to optimize nutrients utilization and allowing metabolic adaptation to environmental stress conditions. Taken together our data provide new evidence that chronic NPs and MPs exposure could act as cancer risk factor for human health.

Cellular Bioenergetics: Experimental Evidence for Alcohol-induced Adaptations

At-risk alcohol use is associated with multisystemic effects and end-organ injury, and significantly contributes to global health burden. Several alcohol-mediated mechanisms have been identified, with bioenergetic maladaptation gaining credence as an underlying pathophysiological mechanism contributing to cellular injury. This evidence-based review focuses on the current knowledge of alcohol-induced bioenergetic adaptations in metabolically active tissues: liver, cardiac and skeletal muscle, pancreas, and brain. Alcohol metabolism itself significantly interferes with bioenergetic pathways in tissues, particularly the liver. Alcohol decreases states of respiration in the electron transport chain, and activity and expression of respiratory complexes, with a net effect to decrease ATP content. In addition, alcohol dysregulates major metabolic pathways, including glycolysis, the tricarboxylic acid cycle, and fatty acid oxidation. These bioenergetic alterations are influenced by alcohol-mediated changes in mitochondrial morphology, biogenesis, and dynamics. The review highlights similarities and differences in bioenergetic adaptations according to tissue type, pattern of (acute vs. chronic) alcohol use, and energy substrate availability. The compromised bioenergetics synergizes with other critical pathophysiological mechanisms, including increased oxidative stress and accelerates cellular dysfunction, promoting senescence, programmed cell death, and end-organ injury.

A practical guide for the analysis, standardization and interpretation of oxygen consumption measurements

Measurement of oxygen consumption is a powerful and uniquely informative experimental technique. It can help identify mitochondrial mechanisms of action following pharmacologic and genetic interventions, and characterize energy metabolism in physiology and disease. The conceptual and practical benefits of respirometry have made it a frontline technique to understand how mitochondrial function can interface with-and in some cases control-cell physiology. Nonetheless, an appreciation of the complexity and challenges involved with such measurements is required to avoid common experimental and analytical pitfalls. Here we provide a practical guide to oxygen consumption measurements covering the selection of experimental models and instrumentation, as well as recommendations for the collection, interpretation and normalization of data. These guidelines are provided with the intention of aiding experimental design and enhancing the overall reputability, transparency and reliability of oxygen consumption measurements.

Synergistic Effect of β-Lapachone and Aminooxyacetic Acid on Central Metabolism in Breast Cancer

The compound β-lapachone, a naturally derived naphthoquinone, has been utilized as a potent medicinal nutrient to improve health. Over the last twelve years, numerous reports have demonstrated distinct associations of β-lapachone and NAD(P)H: quinone oxidoreductase 1 (NQO1) protein in the amelioration of various diseases. Comprehensive research of NQO1 bioactivity has clearly confirmed the tumoricidal effects of β-lapachone action through NAD+-keresis, in which severe DNA damage from reactive oxygen species (ROS) production triggers a poly-ADP-ribose polymerase-I (PARP1) hyperactivation cascade, culminating in NAD+/ATP depletion. Here, we report a novel combination strategy with aminooxyacetic acid (AOA), an aspartate aminotransferase inhibitor that blocks the malate-aspartate shuttle (MAS) and synergistically enhances the efficacy of β-lapachone metabolic perturbation in NQO1+ breast cancer. We evaluated metabolic turnover in MDA-MB-231 NQO1+, MDA-MB-231 NQO1-, MDA-MB-468, and T47D cancer cells by measuring the isotopic labeling of metabolites from a [U-13C]glucose tracer. We show that β-lapachone treatment significantly hampers lactate secretion by ~85% in NQO1+ cells. Our data demonstrate that combinatorial treatment decreases citrate, glutamate, and succinate enrichment by ~14%, ~50%, and ~65%, respectively. Differences in citrate, glutamate, and succinate fractional enrichments indicate synergistic effects on central metabolism based on the coefficient of drug interaction. Metabolic modeling suggests that increased glutamine anaplerosis is protective in the case of MAS inhibition.

Integrating a dynamic central metabolism model of cancer cells with a hybrid 3D multiscale model for vascular hepatocellular carcinoma growth

We develop here a novel modelling approach with the aim of closing the conceptual gap between tumour-level metabolic processes and the metabolic processes occurring in individual cancer cells. In particular, the metabolism in hepatocellular carcinoma derived cell lines (HEPG2 cells) has been well characterized but implementations of multiscale models integrating this known metabolism have not been previously reported. We therefore extend a previously published multiscale model of vascular tumour growth, and integrate it with an experimentally verified network of central metabolism in HEPG2 cells. This resultant combined model links spatially heterogeneous vascular tumour growth with known metabolic networks within tumour cells and accounts for blood flow, angiogenesis, vascular remodelling and nutrient/growth factor transport within a growing tumour, as well as the movement of, and interactions between normal and cancer cells. Model simulations report for the first time, predictions of spatially resolved time courses of core metabolites in HEPG2 cells. These simulations can be performed at a sufficient scale to incorporate clinically relevant features of different tumour systems using reasonable computational resources. Our results predict larger than expected temporal and spatial heterogeneity in the intracellular concentrations of glucose, oxygen, lactate pyruvate, f16bp and Acetyl-CoA. The integrated multiscale model developed here provides an ideal quantitative framework in which to study the relationship between dosage, timing, and scheduling of anti-neoplastic agents and the physiological effects of tumour metabolism at the cellular level. Such models, therefore, have the potential to inform treatment decisions when drug response is dependent on the metabolic state of individual cancer cells.

AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications

Metabolomics describes a high-throughput approach for measuring a repertoire of metabolites and small molecules in biological samples. One utility of untargeted metabolomics, unbiased global analysis of the metabolome, is to detect key metabolites as contributors to, or readouts of, human health and disease. In this perspective, we discuss how artificial intelligence (AI) and machine learning (ML) have promoted major advances in untargeted metabolomics workflows and facilitated pivotal findings in the areas of disease screening and diagnosis. We contextualize applications of AI and ML to the emerging field of high-resolution mass spectrometry (HRMS) exposomics, which unbiasedly detects endogenous metabolites and exogenous chemicals in human tissue to characterize exposure linked with disease outcomes. We discuss the state of the science and suggest potential opportunities for using AI and ML to improve data quality, rigor, detection, and chemical identification in untargeted metabolomics and exposomics studies.

Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer

The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.

Isotope tracing in health and disease

Biochemical characterization of metabolism provides molecular insights for understanding biology in health and disease. Over the past decades, metabolic perturbations have been implicated in cancer, neurodegeneration, and diabetes, among others. Isotope tracing is a technique that allows tracking of labeled atoms within metabolites through biochemical reactions. This technique has become an integral component of the contemporary metabolic research. Isotope tracing measures substrate contribution to downstream metabolites and indicates its utilization in cellular metabolic networks. In addition, isotopic labeling data are necessary for quantitative metabolic flux analysis. Here, we review recent work utilizing metabolic tracing to study health and disease, and highlight its application to interrogate subcellular, intercellular, and in vivo metabolism. We further discuss the current challenges and opportunities to expand the utility of isotope tracing to new research areas.

Linear programming based gene expression model (LPM-GEM) predicts the carbon source for Bacillus subtilis

BACKGROUND

Elucidating cellular metabolism led to many breakthroughs in biotechnology, synthetic biology, and health sciences. To date, deriving metabolic fluxes by ¹³C tracer experiments is the most prominent approach for studying metabolic fluxes quantitatively, often with high accuracy and precision. However, the technique has a high demand for experimental resources. Alternatively, flux balance analysis (FBA) has been employed to estimate metabolic fluxes without labeling experiments. It is less informative but can benefit from the low costs and low experimental efforts and gain flux estimates in experimentally difficult conditions. Methods to integrate relevant experimental data have been emerged to improve FBA flux estimations. Data from transcription profiling is often selected since it is easy to generate at the genome scale, typically embedded by a discretization of differential and non-differential expressed genes coding for the respective enzymes.

RESULT

We established the novel method Linear Programming based Gene Expression Model (LPM-GEM). LPM-GEM linearly embeds gene expression into FBA constraints. We implemented three strategies to reduce thermodynamically infeasible loops, which is a necessary prerequisite for such an omics-based model building. As a case study, we built a model of B. subtilis grown in eight different carbon sources. We obtained good flux predictions based on the respective transcription profiles when validating with ¹³C tracer based metabolic flux data of the same conditions. We could well predict the specific carbon sources. When testing the model on another, unseen dataset that was not used during training, good prediction performance was also observed. Furthermore, LPM-GEM outperformed a well-established model building methods.

CONCLUSION

Employing LPM-GEM integrates gene expression data efficiently. The method supports gene expression-based FBA models and can be applied as an alternative to estimate metabolic fluxes when tracer experiments are inappropriate.

Cell energy metabolism and bone formation

Energy metabolism plays an important role in cell and tissue ability to effectively function, maintain homeostasis, and perform repair. Yet, the role of energy metabolism in skeletal tissues in general and in bone, in particular, remains understudied. We, here, review the aspects of cell energy metabolism relevant to bone tissue, such as: i) availability of substrates and oxygen; ii) metabolism regulatory mechanisms most active in bone tissue, e.g. HIF and BMP; iii) crosstalk of cell bioenergetics with other cell functions, e.g. proliferation and differentiation; iv) role of glycolysis and mitochondrial oxidative phosphorylation in osteogenic lineage; and v) most significant changes in bone energy metabolism observed in aging and other pathologies. In addition, we review available methods to study energy metabolism on a subcellular, cellular, tissue, and live animal levels.

An efficient LC-MS method for isomer separation and detection of sugars, phosphorylated sugars, and organic acids

Assessing central carbon metabolism in plants can be challenging due to the dynamic range in pool sizes, with low levels of important phosphorylated sugars relative to more abundant sugars and organic acids. Here, we report a sensitive liquid chromatography-mass spectrometry method for analysing central metabolites on a hybrid column, where both anion-exchange and hydrophilic interaction chromatography (HILIC) ligands are embedded in the stationary phase. The liquid chromatography method was developed for enhanced selectivity of 27 central metabolites in a single run with sensitivity at femtomole levels observed for most phosphorylated sugars. The method resolved phosphorylated hexose, pentose, and triose isomers that are otherwise challenging. Compared with a standard HILIC approach, these metabolites had improved peak areas using our approach due to ion enhancement or low ion suppression in the biological sample matrix. The approach was applied to investigate metabolism in high lipid-producing tobacco leaves that exhibited increased levels of acetyl-CoA, a precursor for oil biosynthesis. The application of the method to isotopologue detection and quantification was considered through evaluating 13C-labeled seeds from Camelina sativa. The method provides a means to analyse intermediates more comprehensively in central metabolism of plant tissues.

CDK12 promotes tumorigenesis but induces vulnerability to therapies inhibiting folate one-carbon metabolism in breast cancer

Cyclin-dependent kinase 12 (CDK12) overexpression is implicated in breast cancer, but whether it has a primary or only a cooperative tumorigenic role is unclear. Here, we show that transgenic CDK12 overexpression in the mouse mammary gland per se is sufficient to drive the emergence of multiple and multifocal tumors, while, in cooperation with known oncogenes, it promotes earlier tumor onset and metastasis. Integrative transcriptomic, metabolomic and functional data reveal that hyperactivation of the serine-glycine-one-carbon network is a metabolic hallmark inherent to CDK12-induced tumorigenesis. Consistently, in retrospective patient cohort studies and in patient-derived xenografts, CDK12-overexpressing breast tumors show positive response to methotrexate-based chemotherapy targeting CDK12-induced metabolic alterations, while being intrinsically refractory to other types of chemotherapy. In a retrospective analysis of hormone receptor-negative and lymph node-positive breast cancer patients randomized in an adjuvant phase III trial to 1-year low-dose metronomic methotrexate-based chemotherapy or no maintenance chemotherapy, a high CDK12 status predicts a dramatic reduction in distant metastasis rate in the chemotherapy-treated vs. not-treated arm. Thus, by coupling tumor progression with metabolic reprogramming, CDK12 creates an actionable vulnerability for breast cancer therapy and might represent a suitable companion biomarker for targeted antimetabolite therapies in human breast cancers.

Finding biomarkers for targeted therapy is a promising approach to treat cancer. Here, the authors show that in breast cancer preclinical models and patients, CDK12 promotes tumourigenesis but induces selective vulnerability to therapies that target folate one-carbon metabolism.

A Flexible Tool to Correct Superimposed Mass Isotopologue Distributions in GC-APCI-MS Flux Experiments

The investigation of metabolic fluxes and metabolite distributions within cells by means of tracer molecules is a valuable tool to unravel the complexity of biological systems. Technological advances in mass spectrometry (MS) technology such as atmospheric pressure chemical ionization (APCI) coupled with high resolution (HR), not only allows for highly sensitive analyses but also broadens the usefulness of tracer-based experiments, as interesting signals can be annotated de novo when not yet present in a compound library. However, several effects in the APCI ion source, i.e., fragmentation and rearrangement, lead to superimposed mass isotopologue distributions (MID) within the mass spectra, which need to be corrected during data evaluation as they will impair enrichment calculation otherwise. Here, we present and evaluate a novel software tool to automatically perform such corrections. We discuss the different effects, explain the implemented algorithm, and show its application on several experimental datasets. This adjustable tool is available as an R package from CRAN.

Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors

Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data with the use of a mathematical model of the metabolic network. A critical step in MFA is model selection: choosing what compartments, metabolites, and reactions to include in the metabolic network model. Model selection is often done informally during the modelling process, based on the same data that is used for model fitting (estimation data). This can lead to either overly complex models (overfitting) or too simple ones (underfitting), in both cases resulting in poor flux estimates. Here, we propose a method for model selection based on independent validation data. We demonstrate in simulation studies that this method consistently chooses the correct model in a way that is independent on errors in measurement uncertainty. This independence is beneficial, since estimating the true magnitude of these errors can be difficult. In contrast, commonly used model selection methods based on the χ²-test choose different model structures depending on the believed measurement uncertainty; this can lead to errors in flux estimates, especially when the magnitude of the error is substantially off. We present a new approach for quantification of prediction uncertainty of mass isotopomer distributions in other labelling experiments, to check for problems with too much or too little novelty in the validation data. Finally, in an isotope tracing study on human mammary epithelial cells, the validation-based model selection method identified pyruvate carboxylase as a key model component. Our results argue that validation-based model selection should be an integral part of MFA model development.

Measuring metabolic reaction fluxes in living cells is difficult, yet important. The gold standard is to label extracellular metabolites with ¹³C, to use mass spectrometry to find out where the ¹³C-atoms ends up, and finally use mathematical modelling to calculate how quickly each reaction must have flowed, for the ¹³C-atoms to end up like that. This measurement thus relies on usage of the right mathematical model, which must be selected among various candidate models. In this manuscript, we present a new way to do this model selection step, utilizing validation data. Using an adopted approach to calculate the uncertainty of model predictions, we identify new validation experiments, which are neither too similar, nor too dissimilar, compared to the previous training data. The model candidate that is best at predicting this new validation data is the one chosen. Tests on simulated data where the true model is known, shows that the validation-based method is robust when the magnitude of the error in the measurement uncertainty is unknown, something that conventional methods are not. This improvement is important since true uncertainties can be difficult to estimate for these data. Finally, we demonstrate how the new method can be used on real data, to identify fluxes and important reactions.