Understanding metabolism with flux analysis: From theory to application

Merging Metabolic Modeling and Imaging for Screening Therapeutic Targets in Colorectal Cancer

Cancer-associated fibroblasts (CAFs) play a key role in metabolic reprogramming and are well-established contributors to drug resistance in colorectal cancer (CRC). To exploit this metabolic crosstalk, we integrated a systems biology approach that identified key metabolic targets in a data-driven method and validated them experimentally. This process involved high-throughput computational screening to investigate the effects of enzyme perturbations predicted by a computational model of CRC metabolism to understand system-wide effects efficiently. Our results highlighted hexokinase (HK) as one of the crucial targets, which subsequently became our focus for experimental validation using patient-derived tumor organoids (PDTOs). Through metabolic imaging and viability assays, we found that PDTOs cultured in CAF conditioned media exhibited increased sensitivity to HK inhibition. Our approach emphasizes the critical role of integrating computational and experimental techniques in exploring and exploiting CRC-CAF crosstalk.

Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism

This paper presents Maud, a command-line application that implements Bayesian statistical inference for kinetic models of biochemical metabolic reaction networks. Maud takes into account quantitative information from omics experiments and background knowledge as well as structural information about kinetic mechanisms, regulatory interactions, and enzyme knockouts. Our paper reviews the existing options in this area, presents a case study illustrating how Maud can be used to analyze a metabolic network, and explains the biological, statistical, and computational design decisions underpinning Maud.

The intersection of host in vivo metabolism and immune responses to infection with kinetoplastid and apicomplexan parasites

SUMMARYProtozoan parasite infection dramatically alters host metabolism, driven by immunological demand and parasite manipulation strategies. Immunometabolic checkpoints are often exploited by kinetoplastid and protozoan parasites to establish chronic infection, which can significantly impair host metabolic homeostasis. The recent growth of tools to analyze metabolism is expanding our understanding of these questions. Here, we review and contrast host metabolic alterations that occur in vivo during infection with Leishmania, trypanosomes, Toxoplasma, Plasmodium, and Cryptosporidium. Although genetically divergent, there are commonalities among these pathogens in terms of metabolic needs, induction of the type I immune responses required for clearance, and the potential for sustained host metabolic dysbiosis. Comparing these pathogens provides an opportunity to explore how transmission strategy, nutritional demand, and host cell and tissue tropism drive similarities and unique aspects in host response and infection outcome and to design new strategies to treat disease.

Quantification of metabolic activity from isotope tracing data using automated methodology

Isotope tracing is a widely used technique to study metabolic activities by introducing heavy labeled nutrients into living cells and organisms. However, interpreting isotope tracing data is often heuristic, and application of automated methods using artificial intelligence is limited due to the paucity of evaluative knowledge. Our study developed a new pipeline that efficiently predicts metabolic activity in expansive metabolic networks and systematically quantifies flux uncertainty of traditional computational methods. We further developed an algorithm adept at significantly reducing this uncertainty, enabling robust evaluations of metabolic activity with limited data. Using this technology, we discovered highly reprogrammed mitochondria-cytosol exchange cycles in tumor tissue of patients, and observed similar metabolic patterns influenced by nutritional conditions in cancer cells. Thus, our refined methodology provides robust automated quantification of metabolism allowing for new insight into metabolic network activity.

GRAF1 integrates PINK1-Parkin signaling and actin dynamics to mediate cardiac mitochondrial homeostasis

The serine/threonine kinase, PINK1, and the E3 ubiquitin ligase, Parkin, are known to facilitate LC3-dependent autophagosomal encasement and lysosomal clearance of dysfunctional mitochondria, and defects in this process contribute to a variety of cardiometabolic and neurological diseases. Although recent evidence indicates that dynamic actin remodeling plays an important role in PINK1/Parkin-mediated mitochondrial autophagy (mitophagy), the underlying signaling mechanisms remain unknown. Here, we identify the RhoGAP GRAF1 (Arhgap26) as a PINK1 substrate that regulates mitophagy. GRAF1 promotes the release of damaged mitochondria from F-actin anchors, regulates mitochondrial-associated Arp2/3-mediated actin remodeling and facilitates Parkin-LC3 interactions to enhance mitochondria capture by autophagosomes. Graf1 phosphorylation on PINK1-dependent sites is dysregulated in human heart failure, and cardiomyocyte-restricted Graf1 depletion in mice blunts mitochondrial clearance and attenuates compensatory metabolic adaptations to stress. Overall, we identify GRAF1 as an enzyme that coordinates cytoskeletal and metabolic remodeling to promote cardioprotection.

Metabolic flux phenotyping of secondary metabolism in cyanobacteria

Cyanobacteria generate energy from photosynthesis and produce various secondary metabolites with diverse commercial and pharmaceutical applications. Unique metabolic and regulatory pathways in cyanobacteria present new challenges for researchers to enhance their product yields, titers, and rates. Therefore, further advancements are critically needed to establish cyanobacteria as a preferred bioproduction platform. Metabolic flux analysis (MFA) quantitatively determines the intracellular flows of carbon within complex biochemical networks, which elucidate the control of metabolic pathways by transcriptional, translational, and allosteric regulatory mechanisms. The emerging field of systems metabolic engineering (SME) involves the use of MFA and other omics technologies to guide the rational development of microbial production strains. This review highlights the potential of MFA and SME to optimize the production of cyanobacterial secondary metabolites and discusses the technical challenges that lie ahead.

Isotopic Tracing of Nucleotide Sugar Metabolism in Human Pluripotent Stem Cells

Metabolism not only produces energy necessary for the cell but is also a key regulator of several cellular functions, including pluripotency and self-renewal. Nucleotide sugars (NSs) are activated sugars that link glucose metabolism with cellular functions via protein N-glycosylation and O-GlcNAcylation. Thus, understanding how different metabolic pathways converge in the synthesis of NSs is critical to explore new opportunities for metabolic interference and modulation of stem cell functions. Tracer-based metabolomics is suited for this challenge, however chemically-defined, customizable media for stem cell culture in which nutrients can be replaced with isotopically labeled analogs are scarcely available. Here, we established a customizable flux-conditioned E8 (FC-E8) medium that enables stem cell culture with stable isotopes for metabolic tracing, and a dedicated liquid chromatography mass-spectrometry (LC-MS/MS) method targeting metabolic pathways converging in NS biosynthesis. By 13C6-glucose feeding, we successfully traced the time-course of carbon incorporation into NSs directly via glucose, and indirectly via other pathways, such as glycolysis and pentose phosphate pathways, in induced pluripotent stem cells (hiPSCs) and embryonic stem cells. Then, we applied these tools to investigate the NS biosynthesis in hiPSC lines from a patient affected by deficiency of phosphoglucomutase 1 (PGM1), an enzyme regulating the synthesis of the two most abundant NSs, UDP-glucose and UDP-galactose.

Interpreting metabolic complexity via isotope-assisted metabolic flux analysis

Isotope-assisted metabolic flux analysis (iMFA) is a powerful method to mathematically determine the metabolic fluxome from experimental isotope labeling data and a metabolic network model. While iMFA was originally developed for industrial biotechnological applications, it is increasingly used to analyze eukaryotic cell metabolism in physiological and pathological states. In this review, we explain how iMFA estimates the intracellular fluxome, including data and network model (inputs), the optimization-based data fitting (process), and the flux map (output). We then describe how iMFA enables analysis of metabolic complexities and discovery of metabolic pathways. Our goal is to expand the use of iMFA in metabolism research, which is essential to maximizing the impact of metabolic experiments and continuing to advance iMFA and biocomputational techniques.

Global determination of reaction rates and lipid turnover kinetics in Mus musculus

Metabolism is fundamental to life, but measuring metabolic reaction rates remains challenging. Here, we applied C13 fluxomics to monitor the metabolism of dietary glucose carbon in 12 tissues, 9 brain compartments, and over 1,000 metabolite isotopologues over a 4-day period. The rates of 85 reactions surrounding central carbon metabolism are determined with elementary metabolite unit (EMU) modeling. Lactate oxidation, not glycolysis, occurs at a comparable pace with the tricarboxylic acid cycle (TCA), supporting lactate as the primary fuel. We expand the EMU framework to track and quantify metabolite flows across tissues. Specifically, multi-organ EMU simulation of uridine metabolism shows that tissue-blood exchange, not synthesis, controls nucleotide homeostasis. In contrast, isotopologue fingerprinting and kinetic analyses reveal the brown adipose tissue (BAT) having the highest palmitate synthesis activity but no apparent contribution to circulation, suggesting a tissue-autonomous synthesis-to-burn mechanism. Together, this study demonstrates the utility of dietary fluxomics for kinetic mapping in vivo and provides a rich resource for elucidating inter-organ metabolic cross talk.

Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review

The use of nanomaterials in medicine depends largely on nanotoxicological evaluation in order to ensure safe application on living organisms. Artificial intelligence (AI) and machine learning (MI) can be used to analyze and interpret large amounts of data in the field of toxicology, such as data from toxicological databases and high-content image-based screening data. Physiologically based pharmacokinetic (PBPK) models and nano-quantitative structure-activity relationship (QSAR) models can be used to predict the behavior and toxic effects of nanomaterials, respectively. PBPK and Nano-QSAR are prominent ML tool for harmful event analysis that is used to understand the mechanisms by which chemical compounds can cause toxic effects, while toxicogenomics is the study of the genetic basis of toxic responses in living organisms. Despite the potential of these methods, there are still many challenges and uncertainties that need to be addressed in the field. In this review, we provide an overview of artificial intelligence (AI) and machine learning (ML) techniques in nanomedicine and nanotoxicology to better understand the potential toxic effects of these materials at the nanoscale.

Emerging metabolomic tools to study cancer metastasis

Metastasis is responsible for 90% of deaths in patients with cancer. Understanding the role of metabolism during metastasis has been limited by the development of robust and sensitive technologies that capture metabolic processes in metastasizing cancer cells. We discuss the current technologies available to study (i) metabolism in primary and metastatic cancer cells and (ii) metabolic interactions between cancer cells and the tumor microenvironment (TME) at different stages of the metastatic cascade. We identify advantages and disadvantages of each method and discuss how these tools and technologies will further improve our understanding of metastasis. Studies investigating the complex metabolic rewiring of different cells using state-of-the-art metabolomic technologies have the potential to reveal novel biological processes and therapeutic interventions for human cancers.

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.

Targeting lactate dehydrogenase B-dependent mitochondrial metabolism affects tumor initiating cells and inhibits tumorigenesis of non-small cell lung cancer by inducing mtDNA damage

Once considered a waste product of anaerobic cellular metabolism, lactate has been identified as a critical regulator of tumorigenesis, maintenance, and progression. The putative primary function of lactate dehydrogenase B (LDHB) is to catalyze the conversion of lactate to pyruvate; however, its role in regulating metabolism during tumorigenesis is largely unknown. To determine whether LDHB plays a pivotal role in tumorigenesis, we performed 2D and 3D in vitro experiments, utilized a conventional xenograft tumor model, and developed a novel genetically engineered mouse model (GEMM) of non-small cell lung cancer (NSCLC), in which we combined an LDHB deletion allele with an inducible model of lung adenocarcinoma driven by the concomitant loss of p53 (also known as Trp53) and expression of oncogenic KRAS (G12D) (KP). Here, we show that epithelial-like, tumor-initiating NSCLC cells feature oxidative phosphorylation (OXPHOS) phenotype that is regulated by LDHB-mediated lactate metabolism. We show that silencing of LDHB induces persistent mitochondrial DNA damage, decreases mitochondrial respiratory complex activity and OXPHOS, resulting in reduced levels of mitochondria-dependent metabolites, e.g., TCA intermediates, amino acids, and nucleotides. Inhibition of LDHB dramatically reduced the survival of tumor-initiating cells and sphere formation in vitro, which can be partially restored by nucleotide supplementation. In addition, LDHB silencing reduced tumor initiation and growth of xenograft tumors. Furthermore, we report for the first time that homozygous deletion of LDHB significantly reduced lung tumorigenesis upon the concomitant loss of Tp53 and expression of oncogenic KRAS without considerably affecting the animal's health status, thereby identifying LDHB as a potential target for NSCLC therapy. In conclusion, our study shows for the first time that LDHB is essential for the maintenance of mitochondrial metabolism, especially nucleotide metabolism, demonstrating that LDHB is crucial for the survival and proliferation of NSCLC tumor-initiating cells and tumorigenesis.

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.

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.

Biosynthesis and biotechnological interventions for commercial production of microalgal pigments: A review

Microalgae are photosynthetic eukaryotes that serve as microbial cell factories for the production of useful biochemicals, including pigments. These pigments are eco-friendly alternatives to synthetic dyes and reduce environmental and health risks. They also exhibit excellent anti-oxidative properties, making them a useful commodity in the nutrition and pharmaceutical industries. Light-harvesting pigments such as chlorophylls and phycobilins, and photoprotective carotenoids are some of the most common microalgal pigments. The increasing demand for these pigments in industrial applications has prompted a need to improve their metabolic yield in microalgal cells. So far, expensive cultivation methods and sensitivity to microbial contamination remain the main obstacles to the large-scale production of these pigments. This review highlights current issues and future prospects related to the production of microalgal pigments. The review also emphasizes the use of engineering approaches such as genetic engineering, and optimization of media components and physical parameters to increase their commercial-scale production.

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.

Quantitative metabolic fluxes regulated by trans-omic networks

Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.

Elucidating tumor-stromal metabolic crosstalk in colorectal cancer through integration of constraint-based models and LC-MS metabolomics

Colorectal cancer (CRC) is a major cause of morbidity and mortality in the United States. Tumor-stromal metabolic crosstalk in the tumor microenvironment promotes CRC development and progression, but exactly how stromal cells, in particular cancer-associated fibroblasts (CAFs), affect the metabolism of tumor cells remains unknown. Here we take a data-driven approach to investigate the metabolic interactions between CRC cells and CAFs, integrating constraint-based modeling and metabolomic profiling. Using metabolomics data, we perform unsteady-state parsimonious flux balance analysis to infer flux distributions for central carbon metabolism in CRC cells treated with or without CAF-conditioned media. We find that CAFs reprogram CRC metabolism through stimulation of glycolysis, the oxidative arm of the pentose phosphate pathway (PPP), and glutaminolysis, as well as inhibition of the tricarboxylic acid cycle. To identify potential therapeutic targets, we simulate enzyme knockouts and find that CAF-treated CRC cells are especially sensitive to inhibitions of hexokinase and glucose-6-phosphate, the rate limiting steps of glycolysis and oxidative PPP. Our work gives mechanistic insights into the metabolic interactions between CRC cells and CAFs and provides a framework for testing hypotheses towards CRC-targeted therapies.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Advances in flux balance analysis by integrating machine learning and mechanism-based models

The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.

Multilayer Genetic Circuits for Dynamic Regulation of Metabolic Pathways

The dynamic regulation of metabolic pathways is based on changes in external signals and endogenous changes in gene expression levels and has extensive applications in the field of synthetic biology and metabolic engineering. However, achieving dynamic control is not trivial, and dynamic control is difficult to obtain using simple, single-level, control strategies because they are often affected by native regulatory networks. Therefore, synthetic biologists usually apply the concept of logic gates to build more complex and multilayer genetic circuits that can process various signals and direct the metabolic flux toward the synthesis of the molecules of interest. In this review, we first summarize the applications of dynamic regulatory systems and genetic circuits and then discuss how to design multilayer genetic circuits to achieve the optimal control of metabolic fluxes in living cells.

Simultaneous Quantification of the Concentration and Carbon Isotopologue Distribution of Polar Metabolites in a Single Analysis by Gas Chromatography and Mass Spectrometry

¹³C-isotope tracing is a frequently employed approach to study metabolic pathway activity. When combined with the subsequent quantification of absolute metabolite concentrations, this enables detailed characterization of the metabolome in biological specimens and facilitates computational time-resolved flux quantification. Classically, a ¹³C-isotopically labeled sample is required to quantify ¹³C-isotope enrichments and a second unlabeled sample for the quantification of metabolite concentrations. The rationale for a second unlabeled sample is that the current methods for metabolite quantification rely mostly on isotope dilution mass spectrometry (IDMS) and thus isotopically labeled internal standards are added to the unlabeled sample. This excludes the absolute quantification of metabolite concentrations in ¹³C-isotopically labeled samples. To address this issue, we have developed and validated a new strategy using an unlabeled internal standard to simultaneously quantify metabolite concentrations and ¹³C-isotope enrichments in a single ¹³C-labeled sample based on gas chromatography-mass spectrometry (GC/MS). The method was optimized for amino acids and citric acid cycle intermediates and was shown to have high analytical precision and accuracy. Metabolite concentrations could be quantified in small tissue samples (≥20 mg). Also, we applied the method on ¹³C-isotopically labeled mammalian cells treated with and without a metabolic inhibitor. We proved that we can quantify absolute metabolite concentrations and ¹³C-isotope enrichments in a single ¹³C-isotopically labeled sample.

13C isotope-based metabolic flux analysis revealing cellular landscape of glucose metabolism in human liver cells exposed to perfluorooctanoic acid

Perfluorooctanoic acid (PFOA) is well known to break glucose homeostasis. However, the effects of PFOA on glucose metabolism are difficult to be evaluated because related metabolites may be synthesized from other nutritional substrates. Here, the relative contribution of glucose to metabolites (e.g., pyruvate and citrate) in the PFOA-treated human liver cells (HepG2) was determined using the ¹³C isotope-based metabolic flux analysis (MFA), i.e., pathway activities. The relative percentage of [U-¹³C₆] glucose-derived pyruvate in cells exposed to PFOA was not significantly different from that in the controls, indicating that the metabolic pattern of glycolysis was not substantially changed by PFOA. The pathway activity of [U-¹³C₆] glucose-driven tricarboxylic acid (TCA) cycle was dramatically inhibited by PFOA. Consequently, mitochondrial respiratory function was phenotypically impaired by PFOA, as observed from the decreasing basal oxygen consumption rate (OCR), ATP-linked OCR and spare respiratory capacity. This study suggests that PFOA may cause the abnormal glucose metabolism via altering the metabolic pattern of TCA cycle instead of glycolysis. The MFA is strongly recommended as a promising and robust tool to address the toxicity mechanisms of contaminants associated with glucose metabolism.

Challenges in Studying Stem Cell Metabolism

The expanding field of stem cell metabolism has been supported by technical advances in metabolite profiling and novel functional analyses. While use of these methodologies has been fruitful, many challenges are posed by the intricacies of culturing stem cells in vitro, along with the distinctive scarcity of adult tissue stem cells and the complexities of their niches in vivo. This review provides an examination of the methodologies used to characterize stem cell metabolism, highlighting their utility while placing a sharper focus on their limitations and hurdles the field needs to overcome for the optimal study of stem cell metabolic networks.

Metabolomics, metabolic flux analysis and cancer pharmacology

Metabolic reprogramming is a hallmark of cancer and increasing evidence suggests that reprogrammed cell metabolism supports tumor initiation, progression, metastasis and drug resistance. Understanding metabolic dysregulation may provide therapeutic targets and facilitate drug research and development for cancer therapy. Metabolomics enables the high-throughput characterization of a large scale of small molecule metabolites in cells, tissues and biofluids, while metabolic flux analysis (MFA) tracks dynamic metabolic activities using stable isotope tracer methods. Recent advances in metabolomics and MFA technologies make them powerful tools for metabolic profiling and characterizing metabolic activities in health and disease, especially in cancer research. In this review, we introduce recent advances in metabolomics and MFA analytical technologies, and provide the first comprehensive summary of the most commonly used isotope tracing methods. In addition, we highlight how metabolomics and MFA are applied in cancer pharmacology studies particularly for discovering targetable metabolic vulnerabilities, understanding the mechanisms of drug action and drug resistance, exploring potential strategies with dietary intervention, identifying cancer biomarkers, as well as enabling precision treatment with pharmacometabolomics.

Microbial physiological engineering increases the efficiency of microbial cell factories

Microbial cell factories provide vital platforms for the production of chemicals. Advanced biotechnological toolboxes have been developed to enhance their efficiency. However, these tools have limitations in improving physiological functions, and therefore boosting the efficiency (e.g. titer, rate, and yield) of microbial cell factories remains a challenge. In this review, we propose a strategy of microbial physiological engineering (MPE) to improve the efficiency of microbial cell factories. This strategy integrates tools from synthetic and systems biology to characterize and regulate physiological functions during chemical synthesis. MPE strategies mainly focus on the efficiency of substrate utilization, growth performance, stress tolerance, and the product export capacity of cell factories. In short, this review provides a new framework for resolving the bottlenecks that currently exist in low-efficiency cell factories.

Analysis of plant secondary metabolism using stable isotope-labelled precursors

INTRODUCTION

Analysis of biochemical pathways typically involves feeding a labelled precursor to an organism, and then monitoring the metabolic fate of the label. Initial studies used radioisotopes as a label and then monitored radioactivity in the metabolic products. As analytical equipment improved and became more widely available, preference shifted the use stable 'heavy' isotopes like deuterium (² H)-, carbon-13 (¹³ C)- and nitrogen-15 (¹⁵ N)-atoms as labels. Incorporation of the labels could be monitored by mass spectrometry (MS), as part of a hyphenated tool kits, e.g. Liquid chromatography (LC)-MS, gas chromatography (GC)-MS, LC-MS/MS. MS offers great sensitivity but the exact location of an isotope label in a given metabolite cannot always be unambiguously established. Nuclear magnetic resonance (NMR) can also be used to pick up signals of stable isotopes, and can give information on the precise location of incorporated label in the metabolites. However, the detection limit for NMR is quite a bit higher than that for MS.

OBJECTIVES

A number of experiments involving feeding stable isotope-labelled precursors followed by NMR analysis of the metabolites is presented. The aim is to highlight the use of NMR analysis in identifying the precise fate of isotope labels after precursor feeding experiments. As more powerful NMR equipment becomes available, applications as described in this review may become more commonplace in pathway analysis.

CONCLUSION AND PROSPECTS

NMR is a widely accepted tool for chemical structure elucidation and is now increasingly used in metabolomic studies. In addition, NMR, combined with stable isotope feeding, should be considered as a tool for metabolic flux analyses.

Investigating metabolism by mass spectrometry: From steady state to dynamic view

Metabolism is the set of life-sustaining reactions in organisms. These biochemical reactions are organized in metabolic pathways, in which one metabolite is converted through a series of steps catalyzed by enzymes in another chemical compound. Metabolic reactions are categorized as catabolic, the breaking down of metabolites to produce energy, and/or anabolic, the synthesis of compounds that consume energy. The balance between catabolism of the preferential fuel substrate and anabolism defines the overall metabolism of a cell or tissue. Metabolomics is a powerful tool to gain new insights contributing to the identification of complex molecular mechanisms in the field of biomedical research, both basic and translational. The enormous potential of this kind of analyses consists of two key aspects: (i) the possibility of performing so-called targeted and untargeted experiments through which it is feasible to verify or formulate a hypothesis, respectively, and (ii) the opportunity to run either steady-state analyses to have snapshots of the metabolome at a given time under different experimental conditions or dynamic analyses through the use of labeled tracers. In this review, we will highlight the most important practical (e.g., different sample extraction approaches) and conceptual steps to consider for metabolomic analysis, describing also the main application contexts in which it is used. In addition, we will provide some insights into the most innovative approaches and progress in the field of data analysis and processing, highlighting how this part is essential for the proper extrapolation and interpretation of data.

An insight to flux-balance analysis for biochemical networks

Systems biology is one of the integrated ways to study biological systems and is more favourable than the earlier used approaches. It includes metabolic pathway analysis, modelling, and regulatory as well as signal transduction for getting insights into cellular behaviour. Among the various techniques of modelling, simulation, analysis of networks and pathways, flux-based analysis (FBA) has been recognised because of its extensibility as well as simplicity. It is widely accepted because it is not like a mechanistic simulation which depends on accurate kinetic data. The study of fluxes through the network is informative and can give insights even in the absence of kinetic data. FBA is one of the widely used tools to study biochemical networks and needs information of reaction stoichiometry, growth requirements, specific measurement parameters of the biological system, in particular the reconstruction of the metabolic network for the genome-scale, many of which have already been built previously. It defines the boundaries of flux distributions which are possible and achievable with a defined set of genes. This review article gives an insight into FBA, from the extension of flux balancing to mathematical representation followed by a discussion about the formulation of flux-balance analysis problems, defining constraints for the stoichiometry of the pathways and the tools that can be used in FBA such as FASIMA, COBRA toolbox, and OptFlux. It also includes broader areas in terms of applications which can be covered by FBA as well as the queries which can be addressed through FBA.

Metabolic Flux Analysis-Linking Isotope Labeling and Metabolic Fluxes

Metabolic flux analysis (MFA) is an increasingly important tool to study metabolism quantitatively. Unlike the concentrations of metabolites, the fluxes, which are the rates at which intracellular metabolites interconvert, are not directly measurable. MFA uses stable isotope labeled tracers to reveal information related to the fluxes. The conceptual idea of MFA is that in tracer experiments the isotope labeling patterns of intracellular metabolites are determined by the fluxes, therefore by measuring the labeling patterns we can infer the fluxes in the network. In this review, we will discuss the basic concept of MFA using a simplified upper glycolysis network as an example. We will show how the fluxes are reflected in the isotope labeling patterns. The central idea we wish to deliver is that under metabolic and isotopic steady-state the labeling pattern of a metabolite is the flux-weighted average of the substrates’ labeling patterns. As a result, MFA can tell the relative contributions of converging metabolic pathways only when these pathways make substrates in different labeling patterns for the shared product. This is the fundamental principle guiding the design of isotope labeling experiment for MFA including tracer selection. In addition, we will also discuss the basic biochemical assumptions of MFA, and we will show the flux-solving procedure and result evaluation. Finally, we will highlight the link between isotopically stationary and nonstationary flux analysis.

Quantitative Fluxomics of Circulating Metabolites

Mammalian organs are nourished by nutrients carried by the blood circulation. These nutrients originate from diet and internal stores, and can undergo various interconversions before their eventual use as tissue fuel. Here we develop isotope tracing, mass spectrometry, and mathematical analysis methods to determine the direct sources of circulating nutrients, their interconversion rates, and eventual tissue-specific contributions to TCA cycle metabolism. Experiments with fifteen nutrient tracers enabled extensive accounting for both circulatory metabolic cycles and tissue TCA inputs, across fed and fasted mice on either high-carbohydrate or ketogenic diet. We find that a majority of circulating carbon flux is carried by two major cycles: glucose-lactate and triglyceride-glycerol-fatty acid. Futile cycling through these pathways is prominent when dietary content of the associated nutrients is low, rendering internal metabolic activity robust to food choice. The presented in vivo flux quantification methods are broadly applicable to different physiological and disease states.

Quantitative Analysis of the Physiological Contributions of Glucose to the TCA Cycle

The nutritional source for catabolism in the tricarboxylic acid (TCA) cycle is a fundamental question in metabolic physiology. Limited by data and mathematical analysis, controversy exists. Using isotope-labeling data in vivo across several experimental conditions, we construct multiple models of central carbon metabolism and develop methods based on metabolic flux analysis (MFA) to solve for the preferences of glucose, lactate, and other nutrients used in the TCA cycle. We show that in nearly all circumstances, glucose contributes more than lactate as a substrate to the TCA cycle. This conclusion is verified in different animal strains from different studies and different administrations of ¹³C glucose, and is extended to multiple tissue types. Thus, this quantitative analysis of organismal metabolism defines the relative contributions of nutrient fluxes in physiology, provides a resource for analysis of in vivo isotope tracing data, and concludes that glucose is the major nutrient used in mammals.

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

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

The evolving metabolic landscape of chromatin biology and epigenetics

Molecular inputs to chromatin via cellular metabolism are modifiers of the epigenome. These inputs - which include both nutrient availability as a result of diet and growth factor signalling - are implicated in linking the environment to the maintenance of cellular homeostasis and cell identity. Recent studies have demonstrated that these inputs are much broader than had previously been known, encompassing metabolism from a wide variety of sources, including alcohol and microbiotal metabolism. These factors modify DNA and histones and exert specific effects on cell biology, systemic physiology and pathology. In this Review, we discuss the nature of these molecular networks, highlight their role in mediating cellular responses and explore their modifiability through dietary and pharmacological interventions.

Staring into the void: demystifying microbial metabolomics

Metabolites give us a window into the chemistry of microbes and are split into two subclasses: primary and secondary. Primary metabolites are required for life whereas secondary metabolites have historically been classified as those appearing after exponential growth and are not necessarily needed for survival. Many microbial species are estimated to produce hundreds of metabolites and can be affected by differing nutrients. Using various analytical techniques, metabolites can be directly detected in order to elucidate their biological significance. Currently, a single experiment can produce anywhere from megabytes to terabytes of data. This big data has motivated scientists to develop informatics tools to help target specific metabolites or sets of metabolites. Broadly, it is imperative to identify clear biological questions before embarking on a study of metabolites (metabolomics). For instance, studying the effect of a transposon insertion on phenazine biosynthesis in Pseudomonas is a very different from asking what molecules are present in a specific banana-derived strain of Pseudomonas. This review is meant to serve as a primer for a 'choose your own adventure' approach for microbiologists with limited mass spectrometry expertise, with a strong focus on liquid chromatography mass spectrometry based workflows developed or optimized within the past five years.

Circadian and Sleep Metabolomics Across Species

Under normal circadian function, metabolic control is temporally coordinated across tissues and behaviors with a 24-h period. However, circadian disruption results in negative consequences for metabolic homeostasis including energy or redox imbalances. Yet, circadian disruption has become increasingly prevalent within today's society due to many factors including sleep loss. Metabolic consequences of both have been revealed by metabolomics analyses of circadian biology and sleep. Specifically, two primary analytical platforms, mass spectrometry and nuclear magnetic resonance spectroscopy, have been used to study molecular clock and sleep influences on overall metabolic rhythmicity. For example, human studies have demonstrated the prevalence of metabolic rhythms in human biology, as well as pan-metabolome consequences of sleep disruption. However, human studies are limited to peripheral metabolic readouts primarily through minimally invasive procedures. For further tissue- and organism-specific investigations, a number of model systems have been studied, based upon the conserved nature of both the molecular clock and sleep across species. Here we summarize human studies as well as key findings from metabolomics studies using mice, Drosophila, and zebrafish. While informative, a limitation in existing literature is a lack of interpretation regarding dynamic synthesis or catabolism within metabolite pools. To this extent, future work incorporating isotope tracers, specific metabolite reporters, and single-cell metabolomics may provide a means of exploring dynamic activity in pathways of interest.

Dynamic 13C Flux Analysis Captures the Reorganization of Adipocyte Glucose Metabolism in Response to Insulin

Cellular metabolism is dynamic, but quantifying non-steady metabolic fluxes by stable isotope tracers presents unique computational challenges. Here, we developed an efficient 13C-tracer dynamic metabolic flux analysis (13C-DMFA) framework for modeling central carbon fluxes that vary over time. We used B-splines to generalize the flux parameterization system and to improve the stability of the optimization algorithm. As proof of concept, we investigated how 3T3-L1 cultured adipocytes acutely metabolize glucose in response to insulin. Insulin rapidly stimulates glucose uptake, but intracellular pathways responded with differing speeds and magnitudes. Fluxes in lower glycolysis increased faster than those in upper glycolysis. Glycolysis fluxes rose disproportionally larger and faster than the tricarboxylic acid cycle, with lactate a primary glucose end product. The uncovered array of flux dynamics suggests that glucose catabolism is additionally regulated beyond uptake to help shunt glucose into appropriate pathways. This work demonstrates the value of using dynamic intracellular fluxes to understand metabolic function and pathway regulation.

Metabolic flux ratio analysis by parallel 13C labeling of isoprenoid biosynthesis in Rhodobacter sphaeroides

Metabolic engineering for increased isoprenoid production often benefits from the simultaneous expression of the two naturally available isoprenoid metabolic routes, namely the 2-methyl-D-erythritol 4-phosphate (MEP) pathway and the mevalonate (MVA) pathway. Quantification of the contribution of these pathways to the overall isoprenoid production can help to obtain a better understanding of the metabolism within a microbial cell factory. Such type of investigation can benefit from ¹³C metabolic flux ratio studies. Here, we designed a method based on parallel labeling experiments (PLEs), using [1-¹³C]- and [4-¹³C]glucose as tracers to quantify the metabolic flux ratios in the glycolytic and isoprenoid pathways. By just analyzing a reporter isoprenoid molecule and employing only four equations, we could describe the metabolism involved from substrate catabolism to product formation. These equations infer ¹³C atom incorporation into the universal isoprenoid building blocks, isopentenyl-pyrophosphate (IPP) and dimethylallyl-pyrophosphate (DMAPP). Therefore, this renders the method applicable to the study of any of isoprenoid of interest. As proof of principle, we applied it to study amorpha-4,11-diene biosynthesis in the bacterium Rhodobacter sphaeroides. We confirmed that in this species the Entner-Doudoroff pathway is the major pathway for glucose catabolism, while the Embden-Meyerhof-Parnas pathway contributes to a lesser extent. Additionally, we demonstrated that co-expression of the MEP and MVA pathways caused a mutual enhancement of their metabolic flux capacity. Surprisingly, we also observed that the isoprenoid flux ratio remains constant under exponential growth conditions, independently from the expression level of the MVA pathway. Apart from proposing and applying a tool for studying isoprenoid biosynthesis within a microbial cell factory, our work reveals important insights from the co-expression of MEP and MVA pathways, including the existence of a yet unclear interaction between them.

Applications of stable isotope-based metabolomics and fluxomics toward synthetic biology of cyanobacteria

Unique features of cyanobacteria (e.g., photosynthesis and nitrogen fixation) make them potential candidates for production of biofuels and other value-added biochemicals. As prokaryotes, they can be readily engineered using synthetic and systems biology tools. Metabolic engineering of cyanobacteria for the synthesis of desired compounds requires in-depth knowledge of central carbon and nitrogen metabolism, pathway fluxes, and their regulation. Metabolomics and fluxomics offer the comprehensive analysis of metabolism by directly characterizing the biochemical activities of cells. This information is acquired by measuring the abundance of key metabolites and their rates of interconversion, which can be achieved by labeling cells with stable isotopes, quantifying metabolite pool sizes and isotope incorporation by gas chromatography/liquid chromatography-mass spectrometry GC/LC-MS or nuclear magnetic resonance (NMR), and mathematical modeling to estimate in vivo metabolic fluxes. Herein, we review progress that has been made to adapt metabolomics and fluxomics tools to examine model cyanobacterial species. We summarize the application of metabolic flux analysis (MFA) strategies to identify metabolic bottlenecks that can be targeted to boost cell growth, improve stress tolerance, or enhance biochemical production in cyanobacteria. Despite the advances in metabolomics, fluxomics, and other synthetic and systems biology tools during the past years, further efforts are required to increase our understanding of cyanobacterial metabolism in order to create efficient photosynthetic hosts for the production of value-added compounds. This article is categorized under: Laboratory Methods and Technologies > Metabolomics Biological Mechanisms > Metabolism Analytical and Computational Methods > Analytical Methods.

Label-free time- and space-resolved exometabolite sampling of growing plant roots through nanoporous interfaces

Spatial and temporal profiling of metabolites within and between living systems is vital to understanding how chemical signaling shapes the composition and function of these complex systems. Measurement of metabolites is challenging because they are often not amenable to extrinsic tags, are diverse in nature, and are present with a broad range of concentrations. Moreover, direct imaging by chemically informative tools can significantly compromise viability of the system of interest or lack adequate resolution. Here, we present a nano-enabled and label-free imaging technology using a microfluidic sampling network to track production and distribution of chemical information in the microenvironment of a living organism. We describe the integration of a polyester track-etched (PETE) nanofluidic interface to physically confine the biological sample within the model environment, while allowing fluidic access via an underlying microfluidic network. The nanoporous interface enables sampling of the microenvironment above in a time-dependent and spatially-resolved manner. For demonstration, the diffusional flux through the PETE membrane was characterized to understand membrane performance, and exometabolites from a growing plant root were successfully profiled in a space- and time-resolved manner. This method and device provide a frame-by-frame description of the chemical environment that maps to the physical and biological characteristics of the sample.

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

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

The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis

¹³C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. ¹³C MFA, however, stands out from other "-omics sciences," in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying ¹³C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of ¹³C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an "all-around carefree package" for ¹³C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of ¹³C MFA.

CYP2C19 and 3A4 Dominate Metabolic Clearance and Bioactivation of Terbinafine Based on Computational and Experimental Approaches

Lamisil (terbinafine) is an effective, widely prescribed antifungal drug that causes rare idiosyncratic hepatotoxicity. The proposed toxic mechanism involves a reactive metabolite, 6,6-dimethyl-2-hepten-4-ynal (TBF-A), formed through three N-dealkylation pathways. We were the first to characterize them using in vitro studies with human liver microsomes and modeling approaches, yet knowledge of the individual enzymes catalyzing reactions remained unknown. Herein, we employed experimental and computational tools to assess terbinafine metabolism by specific cytochrome P450 isozymes. In vitro inhibitor phenotyping studies revealed six isozymes were involved in one or more N-dealkylation pathways. CYP2C19 and 3A4 contributed to all pathways, and so, we targeted them for steady-state analyses with recombinant isozymes. N-Dealkylation yielding TBF-A directly was catalyzed by CYP2C19 and 3A4 similarly. Nevertheless, CYP2C19 was more efficient than CYP3A4 at N-demethylation and other steps leading to TBF-A. Unlike microsomal reactions, N-denaphthylation was surprisingly efficient for CYP2C19 and 3A4, which was validated by controls. CYP2C19 was the most efficient among all reactions. Nonetheless, CYP3A4 was more selective at steps leading to TBF-A, making it more effective in terbinafine bioactivation based on metabolic split ratios for competing pathways. Model predictions did not extrapolate to quantitative kinetic constants, yet some results for CYP3A4 and CYP2C19 agreed qualitatively with preferred reaction steps and pathways. Clinical data on drug interactions support the CYP3A4 role in terbinafine metabolism, while CYP2C19 remains understudied. Taken together, knowledge of P450s responsible for terbinafine metabolism and TBF-A formation provides a foundation for investigating and mitigating the impact of P450 variations in toxic risks posed to patients.