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]

In situ Metabolic Profiling of Ovarian Cancer Tumor Xenografts: A Digital Pathology Approach

Metabolic profiling of cancer is a rising interest in the field of biomarker development. One bottleneck of its clinical exploitation, however, is the lack of simple and quantitative techniques that enable to capture the key metabolic traits of tumor from archival samples. In fact, liquid chromatography associated with mass spectrometry is the gold-standard technique for the study of tumor metabolism because it has high levels of accuracy and precision. However, it requires freshly frozen samples, which are difficult to collect in large multi-centric clinical studies. For this reason, we propose here to investigate a set of established metabolism-associated protein markers by exploiting immunohistochemistry coupled with digital pathology. As case study, we quantified expression of MCT1, MCT4, GLS, PHGDH, FAS, and ACC in 17 patient-derived ovarian cancer xenografts and correlated it with survival. Among these markers, the glycolysis-associated marker MCT4 was negatively associated with survival of mice. The algorithm enabling a quantitative analysis of these metabolism-associated markers is an innovative research tool that can be exported to large sets of clinical samples and can remove the variability of individual interpretation of immunohistochemistry results.

Direct and quantitative analysis of altered metabolic flux distributions and cellular ATP production pathway in fumarate hydratase-diminished cells

Fumarate hydratase (FH) is an enzyme in the tricarboxylic acid (TCA) cycle, biallelic loss-of-function mutations of which are associated with hereditary leiomyomatosis and renal cell cancer. However, how FH defect modulates intracellular metabolic fluxes in human cells has remained unclear. This study aimed to reveal metabolic flux alterations induced by reduced FH activity. We applied ¹³C metabolic flux analysis (¹³C-MFA) to an established cell line with diminished FH activity (FH^dim) and parental HEK293 cells. FH^dim cells showed reduced pyruvate import flux into mitochondria and subsequent TCA cycle fluxes. Interestingly, the diminished FH activity decreased FH flux only by about 20%, suggesting a very low need for FH to maintain the oxidative TCA cycle. Cellular ATP production from the TCA cycle was dominantly suppressed compared with that from glycolysis in FH^dim cells. Consistently, FH^dim cells exhibited higher glucose dependence for ATP production and higher resistance to an ATP synthase inhibitor. In summary, using FH^dim cells we demonstrated that FH defect led to suppressed pyruvate import into mitochondria, followed by downregulated TCA cycle activity and altered ATP production pathway balance from the TCA cycle to glycolysis. We confirmed that ¹³C-MFA can provide direct and quantitative information on metabolic alterations induced by FH defect.

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.

Long noncoding RNA KCNQ1OT1 promotes colorectal carcinogenesis by enhancing aerobic glycolysis via hexokinase-2

In this study, we investigated the mechanistic role and prognostic significance of the long coding RNA (lncRNA) KCNQ1OT1 in colorectal cancer (CRC). KCNQ1OT1 levels were significantly higher in CRC tissues than adjacent normal colorectal tissues (n=79). High KCNQ1OT1 expression correlated with poorer prognosis in CRC patients. KCNQ1OT1-silenced CRC cells showed reduced proliferation, colony formation, extracellular acidification, and lactate and glucose secretion. This suggests KCNQ1OT1 promotes CRC cell proliferation by increasing aerobic glycolysis. RNA pull-down assays with biotinylated KCNQ1OT1 followed by mass spectrometry analysis showed that KCNQ1OT1 directly binds to hexokinase 2 (HK2). This was confirmed by RNA immunoprecipitation assays using anti-hexokinase 2 antibody. HK2 protein levels were reduced in KCNQ1OT1 knockdown CRC cells, but were restored by treatment with the proteasomal inhibitor MG132. KCNQ1OT1 knockdown CRC cells also showed higher ubiquitinated-HK2 levels, suggesting KCNQ1OT1 enhances aerobic glycolysis by stabilizing HK2. HK2 overexpression in KCNQ1OT1 knockdown CRC cells restored proliferation and aerobic glycolysis. KCNQ1OT1 levels correlated positively with HK2 expression and prognosis in CRC patients. These findings show that KCNQ1OT1 promotes colorectal carcinogenesis by increasing aerobic glycolysis through HK2.

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.

Metabolic cell communication within tumour microenvironment: models, methods and perspectives

Environmental cues are essential in defining tumour malignancy, by promoting tumour initiation, progression and metastatic spreading. Stromal cells may metabolically cooperate or compete with cancer cells, playing a mandatory role in defining cancer metabolic plasticity, potentially dictating the final tumour outcome. Assessing shared nutrients between different tumoural or stromal compartments is essential to understand the impact of environmental nutrients on the metabolic plasticity of tumours. Here, we review analytical and computational approaches for studying the tumour metabolic microenvironment, the destiny of nutrients shared among tumour and stromal populations, as well as the molecular modules of these metabolic relationships.

ScalaFlux: A scalable approach to quantify fluxes in metabolic subnetworks

13C-metabolic flux analysis (13C-MFA) allows metabolic fluxes to be quantified in living organisms and is a major tool in biotechnology and systems biology. Current 13C-MFA approaches model label propagation starting from the extracellular 13C-labeled nutrient(s), which limits their applicability to the analysis of pathways close to this metabolic entry point. Here, we propose a new approach to quantify fluxes through any metabolic subnetwork of interest by modeling label propagation directly from the metabolic precursor(s) of this subnetwork. The flux calculations are thus purely based on information from within the subnetwork of interest, and no additional knowledge about the surrounding network (such as atom transitions in upstream reactions or the labeling of the extracellular nutrient) is required. This approach, termed ScalaFlux for SCALAble metabolic FLUX analysis, can be scaled up from individual reactions to pathways to sets of pathways. ScalaFlux has several benefits compared with current 13C-MFA approaches: greater network coverage, lower data requirements, independence from cell physiology, robustness to gaps in data and network information, better computational efficiency, applicability to rich media, and enhanced flux identifiability. We validated ScalaFlux using a theoretical network and simulated data. We also used the approach to quantify fluxes through the prenyl pyrophosphate pathway of Saccharomyces cerevisiae mutants engineered to produce phytoene, using a dataset for which fluxes could not be calculated using existing approaches. A broad range of metabolic systems can be targeted with minimal cost and effort, making ScalaFlux a valuable tool for the analysis of metabolic fluxes.

Physiological Role of Glutamate Dehydrogenase in Cancer Cells

NH 4 + increased growth rates and final densities of several human metastatic cancer cells. To assess whether glutamate dehydrogenase (GDH) in cancer cells may catalyze the reverse reaction of NH 4 + fixation, its covalent regulation and kinetic parameters were determined under near-physiological conditions. Increased total protein and phosphorylation were attained in NH 4 + -supplemented metastatic cells, but total cell GDH activity was unchanged. Higher V _max values for the GDH reverse reaction vs. forward reaction in both isolated hepatoma (HepM) and liver mitochondria [rat liver mitochondria (RLM)] favored an NH 4 + -fixing role. GDH sigmoidal kinetics with NH 4 + , ADP, and leucine fitted to Hill equation showed n _H values of 2 to 3. However, the K _0.5 values for NH 4 + were over 20 mM, questioning the physiological relevance of the GDH reverse reaction, because intracellular NH 4 + in tumors is 1 to 5 mM. In contrast, data fitting to the Monod-Wyman-Changeux (MWC) model revealed lower K _m values for NH 4 + , of 6 to 12 mM. In silico analysis made with MWC equation, and using physiological concentrations of substrates and modulators, predicted GDH N-fixing activity in cancer cells. Therefore, together with its thermodynamic feasibility, GDH may reach rates for its reverse, NH 4 + -fixing reaction that are compatible with an anabolic role for supporting growth of cancer cells.

Genomic, transcriptomic, and metabolic characterizations of Escherichia coli adapted to branched-chain higher alcohol tolerance

Microbial-produced branched-chain higher alcohols (BCHAs), such as isopropanol, isobutanol, and isopentanol in Escherichia coli, have emerged as promising alternative biofuels under development. Elucidating and improving the tolerance of E. coli to BCHAs are important issues for microbial production of BCHAs due to their physiological inhibitory effect. Previous works aimed at understanding the genetic basis of E. coli tolerance to BCHAs with a comparative genome, reverse engineering, or transcriptome approach have gained some important insights into the mechanism of tolerance. However, investigation on BCHA tolerance from the whole-genomic, transcriptomic, and metabolic levels via a systematic approach has not yet been completely elucidated. Here, in this study, genomic, transcriptomic, and ¹³C-metabolic flux analyses (¹³C-MFA) of an evolved E. coli strain adapted to BCHA tolerance were conducted. Genome mutation of negative regulation factor (rssB, acrB, and clpX) of RpoS level suggested upregulation of RpoS activity in BCHA tolerance of E. coli. From a more detailed perspective, enhanced energy metabolism was observed to be the main characteristic of E. coli strain tolerant to BCHAs. Enhanced energy metabolism has been achieved through several routes, which included redistribution of the central carbon metabolism, upregulation of the energy generation machinery, and facilitating the operation of electron transferring chain. Evidence of multiple solutions of genotype modification toward BCHA tolerance was also revealed through comparative analysis of previous works from different groups.

The Entner-Doudoroff and Nonoxidative Pentose Phosphate Pathways Bypass Glycolysis and the Oxidative Pentose Phosphate Pathway in Ralstonia solanacearum

Understanding the metabolic versatility of Ralstonia solanacearum is important, as it regulates the trade-off between virulence and metabolism (1, 2) in a wide range of plant hosts. Due to a lack of clear evidence until this work, several published research papers reported on the potential roles of glycolysis and the oxidative pentose phosphate pathway (OxPPP) in R. solanacearum (3, 4). This work provided evidence from ¹³C stable isotope feeding and genome annotation-based comparative metabolic network analysis that the Entner-Doudoroff pathway and non-OxPPP bypass glycolysis and OxPPP during the oxidation of glucose, a component of the host xylem pool that serves as a potential carbon source (5). The outcomes help better define the central carbon metabolic network of R. solanacearum that can be integrated with ¹³C metabolic flux analysis as well as flux balance analysis studies for defining the metabolic phenotypes. The study highlights the need to critically examine phytopathogens whose metabolism is poorly understood.

In Ralstonia solanacearum, a devastating phytopathogen whose metabolism is poorly understood, we observed that the Entner-Doudoroff (ED) pathway and nonoxidative pentose phosphate pathway (non-OxPPP) bypass glycolysis and OxPPP under glucose oxidation. Evidence derived from ¹³C stable isotope feeding and genome annotation-based comparative metabolic network analysis supported the observations. Comparative metabolic network analysis derived from the currently available 53 annotated R. solanacearum strains, including a recently reported strain (F1C1), representing the four phylotypes, confirmed the lack of key genes coding for phosphofructokinase (pfk-1) and phosphogluconate dehydrogenase (gnd) enzymes that are relevant for glycolysis and OxPPP, respectively. R. solanacearum F1C1 cells fed with [¹³C]glucose (99% [1-¹³C]glucose or 99% [1,2-¹³C]glucose or 40% [¹³C₆]glucose) followed by gas chromatography-mass spectrometry (GC-MS)-based labeling analysis of fragments from amino acids, glycerol, and ribose provided clear evidence that rather than glycolysis and the OxPPP, the ED pathway and non-OxPPP are the main routes sustaining metabolism in R. solanacearum. The ¹³C incorporation in the mass ions of alanine (m/z 260 and m/z 232), valine (m/z 288 and m/z 260), glycine (m/z 218), serine (m/z 390 and m/z 362), histidine (m/z 440 and m/z 412), tyrosine (m/z 466 and m/z 438), phenylalanine (m/z 336 and m/z 308), glycerol (m/z 377), and ribose (m/z 160) mapped the pathways supporting the observations. The outcomes help better define the central carbon metabolic network of R. solanacearum that can be integrated with ¹³C metabolic flux analysis as well as flux balance analysis studies for defining the metabolic phenotypes.

IMPORTANCE Understanding the metabolic versatility of Ralstonia solanacearum is important, as it regulates the trade-off between virulence and metabolism (1, 2) in a wide range of plant hosts. Due to a lack of clear evidence until this work, several published research papers reported on the potential roles of glycolysis and the oxidative pentose phosphate pathway (OxPPP) in R. solanacearum (3, 4). This work provided evidence from ¹³C stable isotope feeding and genome annotation-based comparative metabolic network analysis that the Entner-Doudoroff pathway and non-OxPPP bypass glycolysis and OxPPP during the oxidation of glucose, a component of the host xylem pool that serves as a potential carbon source (5). The outcomes help better define the central carbon metabolic network of R. solanacearum that can be integrated with ¹³C metabolic flux analysis as well as flux balance analysis studies for defining the metabolic phenotypes. The study highlights the need to critically examine phytopathogens whose metabolism is poorly understood.

Analysing central metabolism in ultra-high resolution: At the crossroads of carbon and nitrogen

BACKGROUND

Cancer cell metabolism can be characterised by adaptive metabolic alterations, which support abnormal proliferative cell growth with high energetic demand. De novo nucleotide biosynthesis is essential for providing nucleotides for RNA and DNA synthesis, and drugs targeting this biosynthetic pathway have proven to be effective anticancer therapeutics. Nevertheless, cancers are often able to circumvent chemotherapeutic interventions and become therapy resistant. Our understanding of the changing metabolic profile of the cancer cell and the mode of action of therapeutics is dependent on technological advances in biochemical analysis.

SCOPE OF REVIEW

This review begins with information about carbon- and nitrogen-donating pathways to build purine and pyrimidine moieties in the course of nucleotide biosynthesis. We discuss the application of stable isotope resolved metabolomics to investigate the dynamics of cancer cell metabolism and outline the benefits of high-resolution accurate mass spectrometry, which enables multiple tracer studies.

CONCLUSION

With the technological advances in mass spectrometry that allow for the analysis of the metabolome in high resolution, the application of stable isotope resolved metabolomics has become an important technique in the investigation of biological processes. The literature in the area of isotope labelling is dominated by 13C tracer studies. Metabolic pathways have to be considered as complex interconnected networks and should be investigated as such. Moving forward to simultaneous tracing of different stable isotopes will help elucidate the interplay between carbon and nitrogen flow and the dynamics of de novo nucleotide biosynthesis within the cell.

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.

Targeting Cell Metabolism as Cancer Therapy

Significance: Cancer cells exhibit altered metabolic pathways to keep up with biosynthetic and reduction-oxidation needs during tumor proliferation and metastasis. The common induction of metabolic pathways during cancer progression, regardless of cancer histio- or genotype, makes cancer metabolism an attractive target for therapeutic exploitation. Recent Advances: Emerging data suggest that these altered pathways may even result in resistance to anticancer therapies. Identifying specific metabolic dependencies that are unique to cancer cells has proved challenging in this field, limiting the therapeutic window for many candidate drug approaches. Critical Issues: Cancer cells display significant metabolic flexibility in nutrient-limited environments, hampering the longevity of suppressing cancer metabolism through any singular approach. Combinatorial "synthetic lethal" approaches may have a better chance for success and promising strategies are reviewed here. The dynamism of the immune system adds a level of complexity, as various immune populations in the tumor microenvironment often share metabolic pathways with cancer, with successive alterations during immune activation and quiescence. Decoding the reprogramming of metabolic pathways within cancer cells and stem cells, as well as examining metabolic symbiosis between components of the tumor microenvironment, would be essential to further meaningful drug development within the tumor's metabolic ecosystem. Future Directions: In this article, we examine evidence for the therapeutic potential of targeting metabolic alterations in cancer, and we discuss the drawbacks and successes that have stimulated this field.

Stable Isotopes for Tracing Mammalian-Cell Metabolism In Vivo

Metabolism is at the cornerstone of all cellular functions and mounting evidence of its deregulation in different diseases emphasizes the importance of a comprehensive understanding of metabolic regulation at the whole-organism level. Stable-isotope measurements are a powerful tool for probing cellular metabolism and, as a result, are increasingly used to study metabolism in in vivo settings. The additional complexity of in vivo metabolic measurements requires paying special attention to experimental design and data interpretation. Here, we review recent work where in vivo stable-isotope measurements have been used to address relevant biological questions within an in vivo context, summarize different experimental and data interpretation approaches and their limitations, and discuss future opportunities in the field.

Computational data mining method for isotopomer analysis in the quantitative assessment of metabolic reprogramming

Measurement of metabolic flux levels using stable isotope labeling has been successfully used to investigate metabolic redirection and reprogramming in living cells or tissues. The metabolic flux ratio between two reactions can be estimated from the ¹³C-labeling patterns of a few metabolites combined with the knowledge of atom mapping in the complicated metabolic network. However, it remains unclear whether an observed change in the labeling pattern of the metabolites is sufficient evidence of a shift in flux ratio between two metabolic states. In this study, a data analysis method was developed for the quantitative assessment of metabolic reprogramming. The Metropolis-Hastings algorithm was used with an in silico metabolic model to generate a probability distribution of metabolic flux levels under a condition in which the ¹³C-labeling pattern was observed. Reanalysis of literature data demonstrated that the developed method enables analysis of metabolic redirection using whole ¹³C-labeling pattern data. Quantitative assessment by Cohen’s effect size (d) enables a more detailed read-out of metabolic reprogramming information. The developed method will enable future applications of the metabolic isotopomer analysis to various targets, including cultured cells, whole tissues, and organs.

Defining a metabolic landscape of tumours: genome meets metabolism

Cancer is a complex disease of multiple alterations occuring at the epigenomic, genomic, transcriptomic, proteomic and/or metabolic levels. The contribution of genetic mutations in cancer initiation, progression and evolution is well understood. However, although metabolic changes in cancer have long been acknowledged and considered a plausible therapeutic target, the crosstalk between genetic and metabolic alterations throughout cancer types is not clearly defined. In this review, we summarise the present understanding of the interactions between genetic drivers of cellular transformation and cancer-associated metabolic changes, and how these interactions contribute to metabolic heterogeneity of tumours. We discuss the essential question of whether changes in metabolism are a cause or a consequence in the formation of cancer. We highlight two modes of how metabolism contributes to tumour formation. One is when metabolic reprogramming occurs downstream of oncogenic mutations in signalling pathways and supports tumorigenesis. The other is where metabolic reprogramming initiates transformation being either downstream of mutations in oncometabolite genes or induced by chronic wounding, inflammation, oxygen stress or metabolic diseases. Finally, we focus on the factors that can contribute to metabolic heterogeneity in tumours, including genetic heterogeneity, immunomodulatory factors and tissue architecture. We believe that an in-depth understanding of cancer metabolic reprogramming, and the role of metabolic dysregulation in tumour initiation and progression, can help identify cellular vulnerabilities that can be exploited for therapeutic use.

Steady-state and Flux-based Trehalose Estimation as an Indicator of Carbon Flow from Gluconeogenesis or Glycolysis

Trehalose (and glycogen) is a major storage carbohydrate in many cells, including S. cerevisiae. Typically, trehalose (a disaccharide of glucose) is synthesized and stored through gluconeogenesis. However, trehalose can also be made directly from glucose, if glucose-6-phosphate is channeled away from glycolysis or pentose phosphate pathway. Therefore, analyzing trehalose synthesis, utilization or its accumulation, can be used as a sentinel read-out for either gluconeogenesis or rewired glucose utilization. However, the steady-state measurements alone of trehalose cannot unambiguously distinguish the nature of carbon flux in a system. Here, we first summarize simple steady-state enzymatic assays to measure trehalose (and glycogen), that will have very wide uses. Subsequently, we describe methods of highly sensitive, quantitative LC-MS/MS based to measure trehalose. We include methods of ¹³C stable-isotope based pulse-labeling experiments (using different carbon sources) with which to measure rates of trehalose synthesis, from different carbon metabolism pathways. This approach can be used to unambiguously determine the extent of carbon flux into trehalose coming from gluconeogenesis, or directly from glucose/glycolysis. These protocols collectively enable comprehensive steady-state as well as carbon flux based measurements of trehalose. This permits a dissection of carbon flux to distinguish between cells in a gluconeogenic state (conventionally leading to trehalose synthesis), or cells with rewired glucose metabolism (also leading to trehalose synthesis). While the methods presented are optimized for yeast, these methods can be easily adapted to several types of cells, including many microbes.

Simultaneous tracers and a unified model of positional and mass isotopomers for quantification of metabolic flux in liver

Computational models based on the metabolism of stable isotope tracers can yield valuable insight into the metabolic basis of disease. The complexity of these models is limited by the number of tracers and the ability to characterize tracer labeling in downstream metabolites. NMR spectroscopy is ideal for multiple tracer experiments since it precisely detects the position of tracer nuclei in molecules, but it lacks sensitivity for detecting low-concentration metabolites. GC-MS detects stable isotope mass enrichment in low-concentration metabolites, but lacks nuclei and positional specificity. We performed liver perfusions and in vivo infusions of ²H and ¹³C tracers, yielding complex glucose isotopomers that were assigned by NMR and fit to a newly developed metabolic model. Fluxes regressed from ²H and ¹³C NMR positional isotopomer enrichments served to validate GC-MS-based flux estimates obtained from the same experimental samples. NMR-derived fluxes were largely recapitulated by modeling the mass isotopomer distributions of six glucose fragment ions measured by GC-MS. Modest differences related to limited fragmentation coverage of glucose C1-C3 were identified, but fluxes such as gluconeogenesis, glycogenolysis, cataplerosis and TCA cycle flux were tightly correlated between the methods. Most importantly, modeling of GC-MS data could assign fluxes in primary mouse hepatocytes, an experiment that is impractical by ²H or ¹³C NMR.

Subcellular metabolic pathway kinetics are revealed by correcting for artifactual post harvest metabolism

OBJECTIVE

The dynamic regulation of metabolic pathways can be monitored by stable isotope tracing. Yet, many metabolites are part of distinct processes within different subcellular compartments. Standard isotope tracing experiments relying on analyses in whole cells may not accurately reflect compartmentalized metabolic processes. Analysis of compartmentalized metabolism and the dynamic interplay between compartments can potentially be achieved by stable isotope tracing followed by subcellular fractionation. Although it is recognized that metabolism can take place during biochemical fractionation of cells, a clear understanding of how such post-harvest metabolism impacts the interpretation of subcellular isotope tracing data and methods to correct for this are lacking. We set out to directly assess artifactual metabolism, enabling us to develop and test strategies to correct for it. We apply these techniques to examine the compartment-specific metabolic kinetics of 13C-labeled substrates targeting central metabolic pathways.

METHODS

We designed a stable isotope tracing strategy to interrogate post-harvest metabolic activity during subcellular fractionation using liquid chromatography-mass spectrometry (LC-MS).

RESULTS

We show that post-harvest metabolic activity occurs rapidly (within seconds) upon cell harvest. With further characterization we reveal that this post-harvest metabolism is enzymatic and reflects the metabolic capacity of the sub-cellular compartment analyzed, but it is limited in the extent of its propagation into downstream metabolites in metabolic pathways. We also propose and test a post-labeling strategy to assess the amount of post-harvest metabolism occurring in an experiment and then to adjust data to account for this. We validate this approach for both mitochondrial and cytosolic metabolic analyses.

CONCLUSIONS

Our data indicate that isotope tracing coupled with sub-cellular fractionation can reveal distinct and dynamic metabolic features of cellular compartments, and that confidence in such data can be improved by applying a post-labeling correction strategy. We examine compartmentalized metabolism of acetate and glutamine and show that acetyl-CoA is turned over rapidly in the cytosol and acts as a pacemaker of anabolic metabolism in this compartment.

The Translational Status of Cancer Liquid Biopsies

Abstract

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.

Lay Summary

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

A Combined Metabolomics and Fluxomics Analysis Identifies Steps Limiting Oil Synthesis in Maize Embryos

Enhancing fatty acid synthesis (FAS) in maize (Zea mays) has tremendous potential nutritional and economic benefits due to the rapidly growing demand for vegetable oil. In maize kernels, the endosperm and the embryo are the main sites for synthesis and accumulation of starch and oil, respectively. So far, breeding efforts to achieve elevated oil content in maize have resulted in smaller endosperms and therefore lower yield. Directly changing their carbon metabolism may be the key to increasing oil content in maize kernels without affecting yield. To test this hypothesis, the intracellular metabolite levels were compared in maize embryos from two different maize lines, ALEXHO S K SYNTHETIC (Alex) and LH59, which accumulate 48% and 34% of oil, respectively. Comparative metabolomics highlighted the metabolites and pathways that were active in the embryos and important for oil production. The contribution of each pathway to FAS in terms of carbon, reductant, and energy provision was assessed by measuring the carbon flow through the metabolic network (13C-metabolic flux analysis) in developing Alex embryos to build a map of carbon flow through the central metabolism. This approach combined mathematical modeling with biochemical quantification to identify metabolic bottlenecks in FAS in maize embryos. This study describes a combination of innovative tools that will pave the way for controlling seed composition in important food crops.

p13CMFA: Parsimonious 13C metabolic flux analysis

Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are ¹³C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using ¹³C Metabolic Flux Analysis (¹³C MFA) to provide quantitative metabolic maps of flux distributions. However, ¹³C MFA might be unable to reduce the solution space towards a unique solution either in large metabolic networks or when small sets of measurements are integrated. Here we present parsimonious ¹³C MFA (p¹³CMFA), an approach that runs a secondary optimization in the ¹³C MFA solution space to identify the solution that minimizes the total reaction flux. Furthermore, flux minimization can be weighted by gene expression measurements allowing seamless integration of gene expression data with ¹³C data. As proof of concept, we demonstrate how p¹³CMFA can be used to estimate intracellular flux distributions from ¹³C measurements and transcriptomics data. We have implemented p¹³CMFA in Iso2Flux, our in-house developed isotopic steady-state ¹³C MFA software. The source code is freely available on GitHub (https://github.com/cfoguet/iso2flux/releases/tag/0.7.2).

¹³C Metabolic Flux Analysis (¹³C MFA) is a well-established technique that has proven to be a valuable tool in quantifying the metabolic flux profile of central carbon metabolism. When a biological system is incubated with a ¹³C-labeled substrate, ¹³C propagates to metabolites throughout the metabolic network in a flux and pathway-dependent manner. ¹³C MFA integrates measurements of ¹³C enrichment in metabolites to identify the flux distributions consistent with the measured ¹³C propagation. However, there is often a range of flux values that can lead to the observed ¹³C distribution. Indeed, either when the metabolic network is large or a small set of measurements are integrated, the range of valid solutions can be too wide to accurately estimate part of the underlying flux distribution. Here we propose to use flux minimization to select the best flux solution in the¹³C MFA solution space. Furthermore, this approach can integrate gene expression data to give greater weight to the minimization of fluxes through enzymes with low gene expression evidence in order to ensure that the selected solution is biologically relevant. The concept of using flux minimization to select the best solution is widely used in flux balance analysis, but it had never been applied in the framework of ¹³C MFA. We have termed this new approach parsimonious ¹³C MFA (p¹³CMFA).

High-resolution 13C metabolic flux analysis

Precise quantification of metabolic pathway fluxes in biological systems is of major importance in guiding efforts in metabolic engineering, biotechnology, microbiology, human health, and cell culture. ¹³C metabolic flux analysis (¹³C-MFA) is the predominant technique used for determining intracellular fluxes. Here, we present a protocol for ¹³C-MFA that incorporates recent advances in parallel labeling experiments, isotopic labeling measurements, and statistical analysis, as well as best practices developed through decades of experience. Experimental design to ensure that fluxes are estimated with the highest precision is an integral part of the protocol. The protocol is based on growing microbes in two (or more) parallel cultures with ¹³C-labeled glucose tracers, followed by gas chromatography-mass spectrometry (GC-MS) measurements of isotopic labeling of protein-bound amino acids, glycogen-bound glucose, and RNA-bound ribose. Fluxes are then estimated using software for ¹³C-MFA, such as Metran, followed by comprehensive statistical analysis to determine the goodness of fit and calculate confidence intervals of fluxes. The presented protocol can be completed in 4 d and quantifies metabolic fluxes with a standard deviation of ≤2%, a substantial improvement over previous implementations. The presented protocol is exemplified using an Escherichia coli ΔtpiA case study with full supporting data, providing a hands-on opportunity to step through a complex troubleshooting scenario. Although applications to prokaryotic microbial systems are emphasized, this protocol can be easily adjusted for application to eukaryotic organisms.

Characterization of Cell Glycocalyx with Mass Spectrometry Methods

The cell membrane plays an important role in protecting the cell from its extracellular environment. As such, extensive work has been devoted to studying its structure and function. Crucial intercellular processes, such as signal transduction and immune protection, are mediated by cell surface glycosylation, which is comprised of large biomolecules, including glycoproteins and glycosphingolipids. Because perturbations in glycosylation could result in dysfunction of cells and are related to diseases, the analysis of surface glycosylation is critical for understanding pathogenic mechanisms and can further lead to biomarker discovery. Different mass spectrometry-based techniques have been developed for glycan analysis, ranging from highly specific, targeted approaches to more comprehensive profiling studies. In this review, we summarized the work conducted for extensive analysis of cell membrane glycosylation, particularly those employing liquid chromatography with mass spectrometry (LC-MS) in combination with various sample preparation techniques.

An unconventional uptake rate objective function approach enhances applicability of genome-scale models for mammalian cells

Constraint-based modeling has been applied to analyze metabolism of numerous organisms via flux balance analysis and genome-scale metabolic models, including mammalian cells such as the Chinese hamster ovary (CHO) cells-the principal cell factory platform for therapeutic protein production. Unfortunately, the application of genome-scale model methodologies using the conventional biomass objective function is challenged by the presence of overly-restrictive constraints, including essential amino acid exchange fluxes that can lead to improper predictions of growth rates and intracellular flux distributions. In this study, these constraints are found to be reliably predicted by an "essential nutrient minimization" approach. After modifying these constraints with the predicted minimal uptake values, a series of unconventional objective functions are applied to minimize each individual non-essential nutrient uptake rate, revealing useful insights about metabolic exchange rates and flows across different cell lines and culture conditions. This unconventional uptake-rate objective functions (UOFs) approach is able to distinguish metabolic differences between three distinct CHO cell lines (CHO-K1, -DG44, and -S) not directly observed using the conventional biomass growth maximization solutions. Further, a comparison of model predictions with experimental data from literature correctly correlates with the specific CHO-DG44-derived cell line used experimentally, and the corresponding dual prices provide fruitful information concerning coupling relationships between nutrients. The UOFs approach is likely to be particularly suited for mammalian cells and other complex organisms which contain multiple distinct essential nutrient inputs, and may offer enhanced applicability for characterizing cell metabolism and physiology as well as media optimization and biomanufacturing control.

Analytical Considerations of Stable Isotope Labelling in Lipidomics

Over the last two decades, lipids have come to be understood as far more than merely components of cellular membranes and forms of energy storage, and are now also being implicated to play important roles in a variety of diseases, with lipid biomarker research one of the most widespread applications of lipidomic techniques both in research and in clinical settings. Stable isotope labelling has become a staple technique in the analysis of small molecule metabolism and dynamics, as it is the only experimental setup by which biosynthesis, remodelling and degradation of biomolecules can be directly measured. Using state-of-the-art analytical technologies such as chromatography-coupled high resolution tandem mass spectrometry, the stable isotope label can be precisely localized and quantified within the biomolecules. The application of stable isotope labelling to lipidomics is however complicated by the diversity of lipids and the complexity of the necessary data analysis. This article discusses key experimental aspects of stable isotope labelling in the field of mass spectrometry-based lipidomics, summarizes current applications and provides an outlook on future developments and potential.

A versatile LC-MS/MS approach for comprehensive, quantitative analysis of central metabolic pathways

Liquid chromatography-mass spectrometry (LC-MS/MS) based approaches are widely used for the identification and quantitation of specific metabolites, and are a preferred approach towards analyzing cellular metabolism. Most methods developed come with specific requirements such as unique columns, ion-pairing reagents and pH conditions, and typically allow measurements in a specific pathway alone. Here, we present a single column-based set of methods for simultaneous coverage of multiple pathways, primarily focusing on central carbon, amino acid, and nucleotide metabolism. We further demonstrate the use of this method for quantitative, stable isotope-based metabolic flux experiments, expanding its use beyond steady-state level measurements of metabolites. The expected kinetics of label accumulation pertinent to the pathway under study are presented with some examples. The methods discussed here are broadly applicable, minimize the need for multiple chromatographic resolution methods, and highlight how simple labeling experiments can be valuable in facilitating a comprehensive understanding of the metabolic state of cells.

Metabolism in dense microbial colonies: 13C metabolic flux analysis of E. coli grown on agar identifies two distinct cell populations with acetate cross-feeding

In this study, we have investigated for the first time the metabolism of E. coli grown on agar using ¹³C metabolic flux analysis (¹³C-MFA). To date, all ¹³C-MFA studies on microbes have been performed with cells grown in liquid culture. Here, we extend the scope of ¹³C-MFA to biological systems where cells are grown in dense microbial colonies. First, we identified new optimal ¹³C tracers to quantify fluxes in systems where the acetate yield cannot be easily measured. We determined that three parallel labeling experiments with the tracers [1,2-¹³C]glucose, [1,6-¹³C]glucose, and [4,5,6-¹³C]glucose permit precise estimation of not only intracellular fluxes, but also of the amount of acetate produced from glucose. Parallel labeling experiments were then performed with wild-type E. coli and E. coli ΔackA grown in liquid culture and on agar plates. Initial attempts to fit the labeling data from wild-type E. coli grown on agar did not produce a statistically acceptable fit. To resolve this issue, we employed the recently developed co-culture ¹³C-MFA approach, where two E. coli subpopulations were defined in the model that engaged in metabolite cross-feeding. The flux results identified two distinct E. coli cell populations, a dominant cell population (92% of cells) that metabolized glucose via conventional metabolic pathways and secreted a large amount of acetate (~40% of maximum theoretical yield), and a second smaller cell population (8% of cells) that consumed the secreted acetate without any glucose influx. These experimental results are in good agreement with recent theoretical simulations. Importantly, this study provides a solid foundation for future investigations of a wide range of problems involving microbial biofilms that are of great interest in biotechnology, ecology and medicine, where metabolite cross-feeding between cell populations is a core feature of the communities.

Microenvironmental regulation of cancer cell metabolism: implications for experimental design and translational studies

Cancers have an altered metabolism, and there is interest in understanding precisely how oncogenic transformation alters cellular metabolism and how these metabolic alterations can translate into therapeutic opportunities. Researchers are developing increasingly powerful experimental techniques to study cellular metabolism, and these techniques have allowed for the analysis of cancer cell metabolism, both in tumors and in ex vivo cancer models. These analyses show that, while factors intrinsic to cancer cells such as oncogenic mutations, alter cellular metabolism, cell-extrinsic microenvironmental factors also substantially contribute to the metabolic phenotype of cancer cells. These findings highlight that microenvironmental factors within the tumor, such as nutrient availability, physical properties of the extracellular matrix, and interactions with stromal cells, can influence the metabolic phenotype of cancer cells and might ultimately dictate the response to metabolically targeted therapies. In an effort to better understand and target cancer metabolism, this Review focuses on the experimental evidence that microenvironmental factors regulate tumor metabolism, and on the implications of these findings for choosing appropriate model systems and experimental approaches.