Stable Isotope Tracing Experiments Using LC-MS.

Metabolomics has emerged as a pivotal field in understanding cellular function, particularly in the context of disease. In numerous diseases, including cancer, alterations in metabolism play an essential role in disease progression and drug response. Hence, unraveling the metabolic rewiring is of importance to find novel diagnostic and therapeutic strategies. Isotope tracing is a powerful technique for delving deeper into the metabolic wiring of cells. By tracking an isotopically labeled substrate through biochemical reactions in the cell, this technique provides a dynamic understanding of cellular metabolism. This chapter outlines a robust isotope tracing protocol utilizing high-resolution mass spectrometry coupled to liquid chromatography in cell culture-based models. We cover essential aspects of experimental design and analyses, providing a valuable resource for researchers aiming to employ isotopic tracing.

Selected Ion Monitoring for Orbitrap-Based Metabolomics.

Orbitrap mass spectrometry in full scan mode enables the simultaneous detection of hundreds of metabolites and their isotope-labeled forms. Yet, sensitivity remains limiting for many metabolites, including low-concentration species, poor ionizers, and low-fractional-abundance isotope-labeled forms in isotope-tracing studies. Here, we explore selected ion monitoring (SIM) as a means of sensitivity enhancement. The analytes of interest are enriched in the orbitrap analyzer by using the quadrupole as a mass filter to select particular ions. In tissue extracts, SIM significantly enhances the detection of ions of low intensity, as indicated by improved signal-to-noise (S/N) ratios and measurement precision. In addition, SIM improves the accuracy of isotope-ratio measurements. SIM, however, must be deployed with care, as excessive accumulation in the orbitrap of similar m/z ions can lead, via space-charge effects, to decreased performance (signal loss, mass shift, and ion coalescence). Ion accumulation can be controlled by adjusting settings including injection time and target ion quantity. Overall, we suggest using a full scan to ensure broad metabolic coverage, in tandem with SIM, for the accurate quantitation of targeted low-intensity ions, and provide methods deploying this approach to enhance metabolome coverage.

Metabolic impact of heterologous protein production in Pseudomonas putida: Insights into carbon and energy flux control.

For engineered microorganisms, the production of heterologous proteins that are often useless to host cells represents a burden on resources, which have to be shared with normal cellular processes. Within a certain metabolic leeway, this competitive process has no impact on growth. However, once this leeway, or free capacity, is fully utilized, the extra load becomes a metabolic burden that inhibits cellular processes and triggers a broad cellular response, reducing cell growth and often hindering the production of heterologous proteins. In this study, we sought to characterize the metabolic rearrangements occurring in the central metabolism of Pseudomonas putida at different levels of metabolic load. To this end, we constructed a P. putida KT2440 strain that expressed two genes encoding fluorescent proteins, one in the genome under constitutive expression to monitor the free capacity, and the other on an inducible plasmid to probe heterologous protein production. We found that metabolic fluxes are considerably reshuffled, especially at the level of periplasmic pathways, as soon as the metabolic load exceeds the free capacity. Heterologous protein production leads to the decoupling of anabolism and catabolism, resulting in large excess energy production relative to the requirements of protein biosynthesis. Finally, heterologous protein production was found to exert a stronger control on carbon fluxes than on energy fluxes, indicating that the flexible nature of P. putida's central metabolic network is solicited to sustain energy production.

Omic Studies on In Vitro Cystinosis Model: siRNA-Mediated CTNS Gene Silencing in HK-2 Cells.

Cystinosis is an autosomal recessive disease caused by mutations in the CTNS gene encoding a protein called cystinosine, which is a lysosomal cystine transporter. Disease-causing mutations lead to accumulation of cystine crystals in the lysosomes, thereby causing dysfunction of vital organs. Determination of the increased leukocyte cystine level is one of the most used methods for diagnosis. However, this method is expensive, difficult to perform, and may yield different results in different laboratories. In this study, a disease model was created with CTNS gene-silenced HK2 cells, which can mimic cystinosis in cell culture, and multiomics methods (ie, proteomics, metabolomics, and fluxomics) were implemented at this cell culture to investigate new biomarkers for the diagnosis. CTNS-silenced cell line exhibited distinct metabolic profiles compared with the control cell line. Pathway analysis highlighted significant alterations in various metabolic pathways, including alanine, aspartate, and glutamate metabolism; glutathione metabolism; aminoacyl-tRNA biosynthesis; arginine and proline metabolism; beta-alanine metabolism; ascorbate and aldarate metabolism; and histidine metabolism upon CTNS silencing. Fluxomics analysis revealed increased cycle rates of Krebs cycle intermediates such as fumarate, malate, and citrate, accompanied by enhanced activation of inorganic phosphate and ATP production. Furthermore, proteomic analysis unveiled differential expression levels of key proteins involved in crucial cellular processes. Notably, peptidyl-prolyl cis-trans isomerase A, translation elongation factor 1-beta (EF-1beta), and 60S acidic ribosomal protein decreased in CTNS-silenced cells. Additionally, levels of P0 and tubulin α-1A chain were reduced, whereas levels of 40S ribosomal protein S8 and Midasin increased. Overall, our study, through the utilization of an in vitro cystinosis model and comprehensive multiomics approach, led to the way toward the identification of potential new biomarkers while offering valuable insights into the pathogenesis of cystinosis.

Metabolic flux reprogramming in Mycobacterium tuberculosis-infected human macrophages.

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

High throughput 13C-metabolic flux analysis of 3-hydroxypropionic acid producing Pichia pastoris reveals limited availability of acetyl-CoA and ATP due to tight control of the glycolytic flux.

BACKGROUND: Production of 3-hydroxypropionic acid (3-HP) through the malonyl-CoA pathway has yielded promising results in Pichia pastoris (Komagataella phaffii), demonstrating the potential of this cell factory to produce this platform chemical and other acetyl-CoA-derived products using glycerol as a carbon source. However, further metabolic engineering of the original P. pastoris 3-HP-producing strains resulted in unexpected outcomes, e.g., significantly lower product yield and/or growth rate. To gain an understanding on the metabolic constraints underlying these observations, the fluxome (metabolic flux phenotype) of ten 3-HP-producing P. pastoris strains has been characterized using a high throughput 13C-metabolic flux analysis platform. Such platform enabled the operation of an optimised workflow to obtain comprehensive maps of the carbon flux distribution in the central carbon metabolism in a parallel-automated manner, thereby accelerating the time-consuming strain characterization step in the design-build-test-learn cycle for metabolic engineering of P. pastoris. RESULTS: We generated detailed maps of the carbon fluxes in the central carbon metabolism of the 3-HP producing strain series, revealing the metabolic consequences of different metabolic engineering strategies aimed at improving NADPH regeneration, enhancing conversion of pyruvate into cytosolic acetyl-CoA, or eliminating by-product (arabitol) formation. Results indicate that the expression of the POS5 NADH kinase leads to a reduction in the fluxes of the pentose phosphate pathway reactions, whereas an increase in the pentose phosphate pathway fluxes was observed when the cytosolic acetyl-CoA synthesis pathway was overexpressed. Results also show that the tight control of the glycolytic flux hampers cell growth due to limited acetyl-CoA biosynthesis. When the cytosolic acetyl-CoA synthesis pathway was overexpressed, the cell growth increased, but the product yield decreased due to higher growth-associated ATP costs. Finally, the six most relevant strains were also cultured at pH 3.5 to assess the effect of a lower pH on their fluxome. Notably, similar metabolic fluxes were observed at pH 3.5 compared to the reference condition at pH 5. CONCLUSIONS: This study shows that existing fluoxomics workflows for high-throughput analyses of metabolic phenotypes can be adapted to investigate P. pastoris, providing valuable information on the impact of genetic manipulations on the metabolic phenotype of this yeast. Specifically, our results highlight the metabolic robustness of P. pastoris's central carbon metabolism when genetic modifications are made to increase the availability of NADPH and cytosolic acetyl-CoA. Such knowledge can guide further metabolic engineering of these strains. Moreover, insights into the metabolic adaptation of P. pastoris to an acidic pH have also been obtained, showing the capability of the fluoxomics workflow to assess the metabolic impact of environmental changes.

Omics data for sampling thermodynamically feasible kinetic models.

Kinetic models are key to understanding and predicting the dynamic behaviour of metabolic systems. Traditional models require kinetic parameters which are not always available and are often estimated in vitro. Ensemble models overcome this challenge by sampling thermodynamically feasible models around a measured reference point. However, it is unclear if the convenient distributions used to generate the ensemble produce a natural distribution of model parameters and hence if the model predictions are reasonable. In this paper, we produced a detailed kinetic model for the central carbon metabolism of Escherichia coli. The model consists of 82 reactions (including 13 reactions with allosteric regulation) and 79 metabolites. To sample the model, we used metabolomic and fluxomic data from a single steady-state time point for E. coli K-12 MG1655 growing on glucose minimal M9 medium (average sampling time for 1000 models: 11.21 ± 0.14 min). Afterwards, in order to examine whether our sampled models are biologically sound, we calculated Km, Vmax and kcat for the reactions and compared them to previously published values. Finally, we used metabolic control analysis to identify enzymes with high control over the fluxes in the central carbon metabolism. Our analyses demonstrate that our platform samples thermodynamically feasible kinetic models, which are in agreement with previously published experimental results and can be used to investigate metabolic control patterns within cells. This renders it a valuable tool for the study of cellular metabolism and the design of metabolic pathways.

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.

Metabolic changes underlying drug resistance in the multiple myeloma tumor microenvironment.

Multiple myeloma (MM) is characterized by the clonal expansion of malignant plasma cells in the bone marrow (BM). MM remains an incurable disease, with the majority of patients experiencing multiple relapses from different drugs. The MM tumor microenvironment (TME) and in particular bone-marrow stromal cells (BMSCs) play a crucial role in the development of drug resistance. Metabolic reprogramming is emerging as a hallmark of cancer that can potentially be exploited for cancer treatment. Recent studies show that metabolism is further adjusted in MM cells during the development of drug resistance. However, little is known about the role of BMSCs in inducing metabolic changes that are associated with drug resistance. In this Perspective, we summarize current knowledge concerning the metabolic reprogramming of MM, with a focus on those changes associated with drug resistance to the proteasome inhibitor Bortezomib (BTZ). In addition, we present proof-of-concept fluxomics (glucose isotope-tracing) and Seahorse data to show that co-culture of MM cells with BMSCs skews the metabolic phenotype of MM cells towards a drug-resistant phenotype, with increased oxidative phosphorylation (OXPHOS), serine synthesis pathway (SSP), TCA cycle and glutathione (GSH) synthesis. Given the crucial role of BMSCs in conveying drug resistance, insights into the metabolic interaction between MM and BMSCs may ultimately aid in the identification of novel metabolic targets that can be exploited for therapy.

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.

Whole-Body Mouse Fluxomic Analysis to Detect Metabolic Disruptions Associated with Microcephaly: Using 13C Isotopes.

Diverse metabolic disorders can disrupt brain growth, and analyzing metabolism in animal models of microcephaly may reveal new mechanisms of pathogenesis. The metabolism of functioning cells in a living organism is constantly changing in response to a changing environment, circadian rhythms, consumed food, drugs, progressing sicknesses, aging, and many other factors. Metabolic profiling can give important insights into the working machinery of the cell. However, a frozen snapshot of the interconnected, complex network of reactions gives very limited information about this system. Flux analysis using stable isotope labels enables more robust metabolic studies that consider interrogate metabolite processing and changes in molecular concentrations over time.

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.

13C metabolic flux analysis: Classification and characterization from the perspective of mathematical modeling and application in physiological research of neural cell.

13C metabolic flux analysis (13C-MFA) has emerged as a forceful tool for quantifying in vivo metabolic pathway activity of different biological systems. This technology plays an important role in understanding intracellular metabolism and revealing patho-physiology mechanism. Recently, it has evolved into a method family with great diversity in experiments, analytics, and mathematics. In this review, we classify and characterize the various branch of 13C-MFA from a unified perspective of mathematical modeling. By linking different parts in the model to each step of its workflow, the specific technologies of 13C-MFA are put into discussion, including the isotope labeling model (ILM), isotope pattern measuring technique, optimization algorithm and statistical method. Its application in physiological research in neural cell has also been reviewed.

Marine bacteria and omic approaches: A novel and potential repository for bioremediation assessment.

Marine environments accommodating diverse assortments of life constitute a great pool of differentiated natural resources. The cumulative need to remedy unpropitious effects of anthropogenic activities on estuaries and coastal marine ecosystems has propelled the development of effective bioremediation strategies. Marine bacteria producing biosurfactants are promising agents for bio-remediating oil pollution in marine environments, making them prospective candidates for enhancing oil recovery. Molecular omics technologies are considered an emerging field of research in ecological and diversity assessment owing to their utility in environmental surveillance and bioremediation of polluted sites. A thorough literature review was undertaken to understand the applicability of different omic techniques used for bioremediation assessment using marine bacteria. This review further establishes that for bioremediation of environmental pollutants (i.e. heavy metals, hydrocarbons, xenobiotic and numerous recalcitrant compounds), organisms isolated from marine environments can be better used for their removal. The literature survey shows that omics approaches can provide exemplary knowledge about microbial communities and their role in the bioremediation of environmental pollutants. This review centres on applications of marine bacteria in enhanced bioremediation, using the omics approaches that can be a vital biological contrivance in environmental monitoring to tackle environmental degradation. The paper aims to identify the gaps in investigations involving marine bacteria to help researchers, ecologists and decision-makers to develop a holistic understanding regarding their utility in bioremediation assessment.

Integrated Multiomics, Bioinformatics, and Computational Modeling Approaches to Central Metabolism in Organs.

Data-driven research led by computational systems biology methods, encompassing bioinformatics of multiomics datasets and mathematical modeling, are critical for discovery. Herein, we describe a multiomics (metabolomics-fluxomics) approach as applied to heart function in diabetes. The methodology presented has general applicability and enables the quantification of the fluxome or set of metabolic fluxes from cytoplasmic and mitochondrial compartments in central catabolic pathways of glucose and fatty acids. Additionally, we present, for the first time, a general method to reduce the dimension of detailed kinetic, and in general stoichiometric models of metabolic networks at the steady state, to facilitate their optimization and avoid numerical problems. Representative results illustrate the powerful mechanistic insights that can be gained from this integrative and quantitative methodology.

Fluxomics - New Metabolomics Approaches to Monitor Metabolic Pathways.

Fluxomics is an innovative -omics research field that measures the rates of all intracellular fluxes in the central metabolism of biological systems. Fluxomics gathers data from multiple different -omics fields, portraying the whole picture of molecular interactions. Recently, fluxomics has become one of the most relevant approaches to investigate metabolic phenotypes. Metabolic flux using 13C-labeled molecules is increasingly used to monitor metabolic pathways, to probe the corresponding gene-RNA and protein-metabolite interaction networks in actual time. Thus, fluxomics reveals the functioning of multi-molecular metabolic pathways and is increasingly applied in biotechnology and pharmacology. Here, we describe the main fluxomics approaches and experimental platforms. Moreover, we summarize recent fluxomic results in different biological systems.

Recent advances in proteomics and metabolomics in plants.

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

Glutamine restores mitochondrial respiration in bleomycin-injured epithelial cells.

Whether from known or unknown causes, loss of epithelial repair plays a central role in the pathogenesis of pulmonary fibrosis. Recently, diminished mitochondrial function has been implicated as a factor contributing to the loss of epithelial repair but the mechanisms mediating these changes have not been defined. Here, we investigated the factors contributing to mitochondrial respiratory dysfunction after bleomycin, a widely accepted agent for modeling pulmonary fibrosis in mice and in vitro systems. In agreement with previous reports, we found that mitochondrial respiration was decreased in lung epithelial cells exposed to bleomycin, but also observed that responses differed depending on the type of metabolic fuel available to cells. For example, we found that mitochondrial respiration was dramatically reduced when glucose served as the primary fuel. Moreover, this associated with a marked decrease in glucose uptake, expression of glucose uptake transport 1 and capacity to augment glycolysis to either glucose or oligomycin. Conversely, mitochondrial respiration was largely preserved if glutamine was present in culture medium. The addition of glutamine also led to increased intracellular metabolite levels, including multiple TCA cycle intermediates and the glycolytic intermediate lactate, as well as reduced DNA damage and cell death to bleomycin. Taken together, these findings indicate that glutamine, rather than glucose, supports mitochondrial respiration and metabolite production in injured lung epithelial cells, and suggest that this shift away from glucose utilization serves to protect the lung epithelium from injury.

SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions.

BACKGROUND: The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms-two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. RESULTS: We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6-27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. CONCLUSIONS: SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

Designing and Development of FRET-Based Nanosensor for Real Time Analysis of N-Acetyl-5-Neuraminic Acid in Living Cells.

N-acetyl-5-neuraminic acid (NeuAc) plays crucial role in improving the growth, brain development, brain health maintenance, and immunity enhancement of infants. Commercially, it is used in the production of antiviral drugs, infant milk formulas, cosmetics, dietary supplements, and pharmaceutical products. Because of the rapidly increasing demand, metabolic engineering approach has attracted increasing attention for NeuAc biosynthesis. However, knowledge of metabolite flux in biosynthetic pathways is one of the major challenges in the practice of metabolic engineering. So, an understanding of the flux of NeuAc is needed to determine its cellular level at real time. The analysis of the flux can only be performed using a tool that has the capacity to measure metabolite level in cells without affecting other metabolic processes. A Fluorescence Resonance Energy Transfer (FRET)-based genetically-encoded nanosensor has been generated in this study to monitor the level of NeuAc in prokaryotic and eukaryotic cells. Sialic acid periplasmic binding protein (SiaP) from Haemophilus influenzae was exploited as a sensory element for the generation of nanosensor. The enhanced cyan fluorescent protein (ECFP) and Venus were used as Fluroscence Resonance Energy Transfer (FRET) pair. The nanosensor, which was termed fluorescent indicator protein for sialic acid (FLIP-SA), was successfully transformed into, and expressed in Escherichia coli BL21 (DE3) cells. The expressed protein of the nanosensor was isolated and purified. The purified nanosensor protein was characterized to assess the affinity, specificity, and stability in the pH range. The developed nanosensor exhibited FRET change after addition to NeuAc. The developed nanosensor was highly specific, exhibited pH stability, and detected NeuAc levels in the nanomolar to milimolar range. FLIP-SA was successfully introduced in bacterial and yeast cells and reported the real-time intracellular levels of NeuAc non-invasively. The FLIP-SA is an excellent tool for the metabolic flux analysis of the NeuAc biosynthetic pathway and, thus, may help unravel the regulatory mechanism of the metabolic pathway of NeuAc. Furthermore, FLIP-SA can be used for the high-throughput screening of E. coli mutant libraries for varied NeuAc production levels.