INCA: a computational platform for isotopically non-stationary metabolic flux analysis

Robustness of mitochondrial biogenesis and respiration explain aerobic glycolysis

A long-standing observation is that in fast-growing cells, respiration rate declines with increasing growth rate and is compensated by an increase in fermentation, despite respiration being more efficient than fermentation. This apparent preference for fermentation even in the presence of oxygen is known as aerobic glycolysis, and occurs in bacteria, yeast, and cancer cells. Considerable work has focused on understanding the potential benefits that might justify this seemingly wasteful metabolic strategy, but its mechanistic basis remains unclear. Here we show that aerobic glycolysis results from the saturation of mitochondrial respiration and the decoupling of mitochondrial biogenesis from the production of other cellular components. Respiration rate is insensitive to acute perturbations of cellular energetic demands or nutrient supplies, and is explained simply by the amount of mitochondria per cell. Mitochondria accumulate at a nearly constant rate across different growth conditions, resulting in mitochondrial amount being largely determined by cell division time. In contrast, glucose uptake rate is not saturated, and is accurately predicted by the abundances and affinities of glucose transporters. Combining these models of glucose uptake and respiration provides a quantitative, mechanistic explanation for aerobic glycolysis. The robustness of specific respiration rate and mitochondrial biogenesis, paired with the flexibility of other bioenergetic and biosynthetic fluxes, may play a broad role in shaping eukaryotic cell metabolism.

Elucidation of triacylglycerol catabolism in Yarrowia lipolytica: How cells balance acetyl-CoA and excess reducing equivalents

Yarrowia lipolytica is an industrial yeast that can convert waste oil to value-added products. However, it is unclear how this yeast metabolizes lipid feedstocks, specifically triacylglycerol (TAG) substrates. This study used 13C-metabolic flux analysis (13C-MFA), genome-scale modeling, and transcriptomics analyses to investigate Y. lipolytica W29 growth with oleic acid, glycerol, and glucose. Transcriptomics data were used to guide 13C-MFA model construction and to validate the 13C-MFA results. The 13C-MFA data were then used to constrain a genome-scale model (GSM), which predicted Y. lipolytica fluxes, cofactor balance, and theoretical yields of terpene products. The three data sources provided new insights into cellular regulation during catabolism of glycerol and fatty acid components of TAG substrates, and how their consumption routes differ from glucose catabolism. We found that (1) over 80% of acetyl-CoA from oleic acid is processed through the glyoxylate shunt, a pathway that generates less CO2 compared to the TCA cycle, (2) the carnitine shuttle is a key regulator of the cytosolic acetyl-CoA pool in oleic acid and glycerol cultures, (3) the oxidative pentose phosphate pathway and mannitol cycle are key routes for NADPH generation, (4) the mannitol cycle and alternative oxidase activity help balance excess NADH generated from β-oxidation of oleic acid, and (5) asymmetrical gene expressions and GSM simulations of enzyme usage suggest an increased metabolic burden for oleic acid catabolism.

Central transcriptional regulator controls photosynthetic growth and carbon storage in response to high light

Carbon capture and biochemical storage are some of the primary drivers of photosynthetic yield and productivity. To elucidate the mechanisms governing carbon allocation, we designed a photosynthetic light response test system for genetic and metabolic carbon assimilation tracking, using microalgae as simplified plant models. The systems biology mapping of high light-responsive photophysiology and carbon utilization dynamics between two variants of the same Picochlorum celeri species, TG1 and TG2 elucidated metabolic bottlenecks and transport rates of intermediates using instationary 13C-fluxomics. Simultaneous global gene expression dynamics showed 73% of the annotated genes responding within one hour, elucidating a singular, diel-responsive transcription factor, closely related to the CCA1/LHY clock genes in plants, with significantly altered expression in TG2. Transgenic P. celeri TG1 cells expressing the TG2 CCA1/LHY gene, showed 15% increase in growth rates and 25% increase in storage carbohydrate content, supporting a coordinating regulatory function for a single transcription factor.

Pharmacological SERCA activation limits diet-induced steatohepatitis and restores liver metabolic function in mice

Metabolic dysfunction-associated steatotic liver disease is the most common form of liver disease and poses significant health risks to patients who progress to metabolic dysfunction-associated steatohepatitis. Fatty acid overload alters endoplasmic reticulum (ER) calcium stores and induces mitochondrial oxidative stress in hepatocytes, leading to hepatocellular inflammation and apoptosis. Obese mice have impaired liver sarco/ER Ca2+-ATPase (SERCA) function, which normally maintains intracellular calcium homeostasis by transporting Ca2+ ions from the cytoplasm to the ER. We hypothesized that restoration of SERCA activity would improve diet-induced steatohepatitis in mice by limiting ER stress and mitochondrial dysfunction. WT and melanocortin-4 receptor KO (Mc4r-/-) mice were placed on either chow or Western diet (WD) for 8 weeks. Half of the WD-fed mice were administered CDN1163 to activate SERCA, which reduced liver fibrosis and inflammation. SERCA activation also restored glucose tolerance and insulin sensitivity, improved histological markers of metabolic dysfunction-associated steatohepatitis, increased expression of antioxidant enzymes, and decreased expression of oxidative stress and ER stress genes. CDN1163 decreased hepatic citric acid cycle flux and liver pyruvate cycling, enhanced expression of mitochondrial respiratory genes, and shifted hepatocellular [NADH]/[NAD+] and [NADPH]/[NADP+] ratios to a less oxidized state, which was associated with elevated PUFA content of liver lipids. In sum, the data demonstrate that pharmacological SERCA activation limits metabolic dysfunction-associated steatotic liver disease progression and prevents metabolic dysfunction induced by WD feeding in mice.

Stable Isotope Tracing Analysis in Cancer Research: Advancements and Challenges in Identifying Dysregulated Cancer Metabolism and Treatment Strategies

Metabolic reprogramming is a hallmark of cancer, driving the development of therapies targeting cancer metabolism. Stable isotope tracing has emerged as a widely adopted tool for monitoring cancer metabolism both in vitro and in vivo. Advances in instrumentation and the development of new tracers, metabolite databases, and data analysis tools have expanded the scope of cancer metabolism studies across these scales. In this review, we explore the latest advancements in metabolic analysis, spanning from experimental design in stable isotope-labeling metabolomics to sophisticated data analysis techniques. We highlight successful applications in cancer research, particularly focusing on ongoing clinical trials utilizing stable isotope tracing to characterize disease progression, treatment responses, and potential mechanisms of resistance to anticancer therapies. Furthermore, we outline key challenges and discuss potential strategies to address them, aiming to enhance our understanding of the biochemical basis of cancer metabolism.

Rethinking 13C-metabolic flux analysis - The Bayesian way of flux inference

Metabolic reaction rates (fluxes) play a crucial role in comprehending cellular phenotypes and are essential in areas such as metabolic engineering, biotechnology, and biomedical research. The state-of-the-art technique for estimating fluxes is metabolic flux analysis using isotopic labelling (13C-MFA), which uses a dataset-model combination to determine the fluxes. Bayesian statistical methods are gaining popularity in the field of life sciences, but the use of 13C-MFA is still dominated by conventional best-fit approaches. The slow take-up of Bayesian approaches is, at least partly, due to the unfamiliarity of Bayesian methods to metabolic engineering researchers. To address this unfamiliarity, we here outline similarities and differences between the two approaches and highlight particular advantages of the Bayesian way of flux analysis. With a real-life example, re-analysing a moderately informative labelling dataset of E. coli, we identify situations in which Bayesian methods are advantageous and more informative, pointing to potential pitfalls of current 13C-MFA evaluation approaches. We propose the use of Bayesian model averaging (BMA) for flux inference as a means of overcoming the problem of model uncertainty through its tendency to assign low probabilities to both, models that are unsupported by data, and models that are overly complex. In this capacity, BMA resembles a tempered Ockham's razor. With the tempered razor as a guide, BMA-based 13C-MFA alleviates the problem of model selection uncertainty and is thereby capable of becoming a game changer for metabolic engineering by uncovering new insights and inspiring novel approaches.

Disruption of hepatic mitochondrial pyruvate and amino acid metabolism impairs gluconeogenesis and endurance exercise capacity in mice

Exercise robustly increases the glucose demands of skeletal muscle. This demand is met by not only muscle glycogenolysis but also accelerated liver glucose production from hepatic glycogenolysis and gluconeogenesis to fuel mechanical work and prevent hypoglycemia during exercise. Hepatic gluconeogenesis during exercise is dependent on highly coordinated responses within and between muscle and liver. Specifically, exercise increases the rate at which gluconeogenic precursors such as pyruvate/lactate or amino acids are delivered from muscle to the liver, extracted by the liver, and channeled into glucose. Herein, we examined the effects of interrupting hepatic gluconeogenic efficiency and capacity on exercise performance by deleting mitochondrial pyruvate carrier 2 (MPC2) and/or alanine transaminase 2 (ALT2) in the liver of mice. We found that deletion of MPC2 or ALT2 alone did not significantly affect time to exhaustion or postexercise glucose concentrations in treadmill exercise tests, but mice lacking both MPC2 and ALT2 in hepatocytes (double knockout, DKO) reached exhaustion faster and exhibited lower circulating glucose during and after exercise. Use of 2H/1³C metabolic flux analyses demonstrated that DKO mice exhibited lower endogenous glucose production owing to decreased glycogenolysis and gluconeogenesis at rest and during exercise. Decreased gluconeogenesis was accompanied by lower anaplerotic, cataplerotic, and TCA cycle fluxes. Collectively, these findings demonstrate that the transition of the liver to the gluconeogenic mode is critical for preventing hypoglycemia and sustaining performance during exercise. The results also illustrate the need for interorgan cross talk during exercise as described by the Cahill and Cori cycles.NEW & NOTEWORTHY Martino and colleagues examined the effects of inhibiting hepatic gluconeogenesis on exercise performance and systemic metabolism during treadmill exercise in mice. Combined inhibition of gluconeogenesis from lactate/pyruvate and alanine impaired exercise endurance and led to hypoglycemia during and after exercise. In contrast, suppressing either pyruvate-mediated or alanine-mediated gluconeogenesis alone had no effect on these parameters. These findings provide new insight into the molecular nodes that coordinate the metabolic responses of muscle and liver during exercise.

Synthetically-primed adaptation of Pseudomonas putida to a non-native substrate D-xylose

To broaden the substrate scope of microbial cell factories towards renewable substrates, rational genetic interventions are often combined with adaptive laboratory evolution (ALE). However, comprehensive studies enabling a holistic understanding of adaptation processes primed by rational metabolic engineering remain scarce. The industrial workhorse Pseudomonas putida was engineered to utilize the non-native sugar D-xylose, but its assimilation into the bacterial biochemical network via the exogenous xylose isomerase pathway remained unresolved. Here, we elucidate the xylose metabolism and establish a foundation for further engineering followed by ALE. First, native glycolysis is derepressed by deleting the local transcriptional regulator gene hexR. We then enhance the pentose phosphate pathway by implanting exogenous transketolase and transaldolase into two lag-shortened strains and allow ALE to finetune the rewired metabolism. Subsequent multilevel analysis and reverse engineering provide detailed insights into the parallel paths of bacterial adaptation to the non-native carbon source, highlighting the enhanced expression of transaldolase and xylose isomerase along with derepressed glycolysis as key events during the process.

13C Metabolic Flux Analysis in Chondrocytes Reveals a Novel Switch in Metabolic Phenotype

Chondrocytes are typically known for their anaerobic metabolism both in vivo and under culture conditions in vitro. However, chondrocytes have been shown to display greater biosynthetic activity when subjected to conditions that elicit aerobic metabolism. We have previously shown that tissue formation by chondrocytes can be upregulated by controlling nutrient availability and that this response arises from changes in glucose metabolism. The aim of the present study was to further characterize these changes through 13C-metabolic flux analysis (13C-MFA), as well as to determine the most optimal response. Primary bovine chondrocytes were grown in scaffold-free high-density tissue culture. [U-13C] glucose labeling experiments were combined with a tissue-specific metabolic network model to carry out 13C-MFA under varying levels of nutrient availability. 13C-MFA results demonstrated that when subjected to increasing nutrient availability, chondrocytes switch from a predominately anaerobic to a mixed aerobic-anaerobic phenotype. This metabolic switch was attributed to the saturation of the lactate fermentation pathway and metabolite overflow toward the tricarboxylic acid cycle. This effect appears to be similar to, but the inverse of, the Crabtree effect ("inverse Crabtree effect"). The relationships between metabolic flux and nutrient availability were then utilized to identify culture conditions that promote enhanced tissue formation. This novel metabolic effect presents a simple but effective approach for enhancing the biosynthetic response of chondrocytes-a key requirement to develop functional engineered cartilaginous tissue for joint resurfacing.

SIMPEL: using stable isotopes to elucidate dynamics of context specific metabolism

The capacity to leverage high resolution mass spectrometry (HRMS) with transient isotope labeling experiments is an untapped opportunity to derive insights on context-specific metabolism, that is difficult to assess quantitatively. Tools are needed to comprehensively mine isotopologue information in an automated, high-throughput way without errors. We describe a tool, Stable Isotope-assisted Metabolomics for Pathway Elucidation (SIMPEL), to simplify analysis and interpretation of isotope-enriched HRMS datasets. The efficacy of SIMPEL is demonstrated through examples of central carbon and lipid metabolism. In the first description, a dual-isotope labeling experiment is paired with SIMPEL and isotopically nonstationary metabolic flux analysis (INST-MFA) to resolve fluxes in central metabolism that would be otherwise challenging to quantify. In the second example, SIMPEL was paired with HRMS-based lipidomics data to describe lipid metabolism based on a single labeling experiment. Available as an R package, SIMPEL extends metabolomics analyses to include isotopologue signatures necessary to quantify metabolic flux.

Boundary flux analysis: an emerging strategy for investigating metabolic pathway activity in large cohorts

Many biological phenotypes are rooted in metabolic pathway activity rather than the concentrations of individual metabolites. Despite this, most metabolomics studies only capture steady-state metabolism - not metabolic flux. Although sophisticated metabolic flux analysis strategies have been developed, these methods are technically challenging and difficult to implement in large-cohort studies. Recently, a new boundary flux analysis (BFA) approach has emerged that captures large-scale metabolic flux phenotypes by quantifying changes in metabolite levels in the media of cultured cells. This approach is advantageous because it is relatively easy to implement yet captures complex metabolic flux phenotypes. We describe the opportunities and challenges of BFA and illustrate how it can be harnessed to investigate a wide transect of biological phenomena.

Exercise training adaptations in liver glycogen and glycerolipids require hepatic AMP-activated protein kinase in mice

Regular exercise elicits adaptations in glucose and lipid metabolism that allow the body to meet energy demands of subsequent exercise bouts more effectively and mitigate metabolic diseases including fatty liver. Energy discharged during the acute exercise bouts that comprise exercise training may be a catalyst for liver adaptations. During acute exercise, liver glycogenolysis and gluconeogenesis are accelerated to supply glucose to working muscle. Lower liver energy state imposed by gluconeogenesis and related pathways activates AMP-activated protein kinase (AMPK), which conserves ATP partly by promoting lipid oxidation. This study tested the hypothesis that AMPK is necessary for liver glucose and lipid adaptations to training. Liver-specific AMPKα1α2 knockout (AMPKα1α2fl/fl+AlbCre) mice and littermate controls (AMPKα1α2fl/fl) completed sedentary and exercise training protocols. Liver nutrient fluxes were quantified at rest or during acute exercise following training. Liver metabolites and molecular regulators of metabolism were assessed. Training increased liver glycogen in AMPKα1α2fl/fl mice, but not in AMPKα1α2fl/fl+AlbCre mice. The inability to increase glycogen led to lower glycogenolysis, glucose production, and circulating glucose during acute exercise in trained AMPKα1α2fl/fl+AlbCre mice. Deletion of AMPKα1α2 attenuated training-induced declines in liver diacylglycerides. In particular, training lowered the concentration of unsaturated and elongated fatty acids comprising diacylglycerides in AMPKα1α2fl/fl mice, but not in AMPKα1α2fl/fl+AlbCre mice. Training increased liver triacylglycerides and the desaturation and elongation of fatty acids in triacylglycerides of AMPKα1α2fl/fl+AlbCre mice. These lipid responses were independent of differences in tricarboxylic acid cycle fluxes. In conclusion, AMPK is required for liver training adaptations that are critical to glucose and lipid metabolism.NEW & NOTEWORTHY This study shows that the energy sensor and transducer, AMP-activated protein kinase (AMPK), is necessary for an exercise training-induced: 1) increase in liver glycogen that is necessary for accelerated glycogenolysis during exercise, 2) decrease in liver glycerolipids independent of tricarboxylic acid (TCA) cycle flux, and 3) decline in the desaturation and elongation of fatty acids comprising liver diacylglycerides. The mechanisms defined in these studies have implications for use of regular exercise or AMPK-activators in patients with fatty liver.

Model validation and selection in metabolic flux analysis and flux balance analysis

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

Combining Gas Exchange and Rapid Quenching of Leaf Tissue for Mass Spectrometry Analysis Directly in Gas Exchange Cuvette

Isotopically nonstationary metabolic flux analysis (INST-MFA) is a powerful technique for studying plant central metabolism, which involves introducing a 13CO2 tracer to plant leaves and sampling the labeled metabolic intermediates during the transient period before reaching an isotopic steady state. The metabolic intermediates involved in the C3 cycle have exceptionally fast turnover rates, with some intermediates turning over many times a second. As a result, it is necessary to rapidly introduce the label and then rapidly quench the plant tissue to determine concentrations in the light or capture the labeling kinetics of these intermediates at early labeling time points. Here, we describe a rapid quenching (0.1-0.5 s) system for 13CO2 labeling experiments in plant leaves to minimize metabolic changes during labeling and quenching experiments. This system is integrated into a commercially available gas exchange analyzer to measure initial rates of gas exchange, precisely control ambient conditions, and monitor the conversion from 12CO2 to 13CO2.

Daylength variation affects growth, photosynthesis, leaf metabolism, partitioning, and metabolic fluxes

Daylength, a seasonal and latitudinal variable, exerts a substantial impact on plant growth. However, the relationship between daylength and growth is nonproportional, suggesting the existence of adaptive mechanisms. Thus, our study aimed to comprehensively investigate the adaptive strategies employed by plants in response to daylength variation. We grew false flax (Camelina sativa) plants, a model oilseed crop, under long-day (LD) and short-day (SD) conditions and used growth measurements, gas exchange measurements, and isotopic labeling techniques, including 13C, 14C, and 2H2O, to determine responses to different daylengths. Our findings revealed that daylength influences various growth parameters, photosynthetic physiology, carbon partitioning, metabolic fluxes, and metabolite levels. SD plants employed diverse mechanisms to compensate for reduced CO2 fixation in the shorter photoperiod. These mechanisms included enhanced photosynthetic rates and reduced respiration in the light (RL), leading to increased shoot investment. Additionally, SD plants exhibited reduced rates of the glucose 6-phosphate (G6P) shunt and greater partitioning of sugars into starch, thereby sustaining carbon availability during the longer night. Isotopic labeling results further demonstrated substantial alterations in the partitioning of amino acids and TCA cycle intermediates between rapidly and slowly turning over pools. Overall, the results point to multiple developmental, physiological, and metabolic ways in which plants adapt to different daylengths to maintain growth.

Bayesian 13C-Metabolic Flux Analysis of Parallel Tracer Experiments in Granulocytes: A Directional Shift within the Non-Oxidative Pentose Phosphate Pathway Supports Phagocytosis

The pentose phosphate pathway (PPP) plays a key role in the cellular regulation of immune function; however, little is known about the interplay of metabolic adjustments in granulocytes, especially regarding the non-oxidative PPP. For the determination of metabolic mechanisms within glucose metabolism, we propose a novel set of measures for 13C-metabolic flux analysis based on ex vivo parallel tracer experiments ([1,2-13C]glucose, [U-13C]glucose, [4,5,6-13C]glucose) and gas chromatography-mass spectrometry labeling measurements of intracellular metabolites, such as sugar phosphates and their fragments. A detailed constraint analysis showed that the permission range for net and irreversible fluxes was limited to a three-dimensional space. The overall workflow, including its Bayesian flux estimation, resulted in precise flux distributions and pairwise confidence intervals, some of which could be represented as a line due to the strength of their correlation. The principal component analysis that was enabled by these behaviors comprised three components that explained 99.6% of the data variance. It showed that phagocytic stimulation reversed the direction of non-oxidative PPP net fluxes from ribose-5-phosphate biosynthesis toward glycolytic pathways. This process was closely associated with the up-regulation of the oxidative PPP to promote the oxidative burst.

Observing exocrine pancreas metabolism using a novel pancreas perfusion technique in combination with hyperpolarized [1-13 C]pyruvate

In a clinical setting, ex vivo perfusions are routinely used to maintain and assess organ viability prior to transplants. Organ perfusions are also a model system to examine metabolic flux while retaining the local physiological structure, with significant success using hyperpolarized (HP) 13 C NMR in this context. We use a novel exocrine pancreas perfusion technique via the common bile duct to assess acinar cell metabolism with HP [1-13 C]pyruvate. The exocrine component of the pancreas produces digestive enzymes through the ductal system and is often neglected in research on the pancreas. Real-time production of [1-13 C]lactate, [1-13 C]alanine, [1-13 C]malate, [4-13 C]malate, [1-13 C]aspartate, and H13 CO3 - was detected. The appearance of these resonances indicates flux through both pyruvate dehydrogenase and pyruvate carboxylase. We studied excised pancreata from C57BL/6J mice and NOD.Rag1-/- .AI4α/β mice, a commonly used model of Type 1 Diabetes (T1D). Pancreata from the T1D mice displayed increased lactate to alanine ratio without changes in oxygen consumption, signifying increased cytosolic NADH levels. The mass isotopologue analysis of the extracted pancreas tissue using gas chromatography-mass spectrometry revealed confirmatory 13 C enrichment in multiple TCA cycle metabolites that are products of pyruvate carboxylation. The methodology presented here has the potential to provide insight into mechanisms underlying several pancreatic diseases, such as diabetes, pancreatitis, and pancreatic cancer.

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

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

Metabolic flux reprogramming in Mycobacterium tuberculosis-infected human macrophages

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

Metabolic engineering of Synechococcus elongatus 7942 for enhanced sucrose biosynthesis

The capability of cyanobacteria to produce sucrose from CO2 and light has a remarkable societal and biotechnological impact since sucrose can serve as a carbon and energy source for a variety of heterotrophic organisms and can be converted into value-added products. However, most metabolic engineering efforts have focused on understanding local pathway alterations that drive sucrose biosynthesis and secretion in cyanobacteria rather than analyzing the global flux re-routing that occurs following induction of sucrose production by salt stress. Here, we investigated global metabolic flux alterations in a sucrose-secreting (cscB-overexpressing) strain relative to its wild-type Synechococcus elongatus 7942 parental strain. We used targeted metabolomics, 13C metabolic flux analysis (MFA), and genome-scale modeling (GSM) as complementary approaches to elucidate differences in cellular resource allocation by quantifying metabolic profiles of three cyanobacterial cultures - wild-type S. elongatus 7942 without salt stress (WT), wild-type with salt stress (WT/NaCl), and the cscB-overexpressing strain with salt stress (cscB/NaCl) - all under photoautotrophic conditions. We quantified the substantial rewiring of metabolic fluxes in WT/NaCl and cscB/NaCl cultures relative to WT and identified a metabolic bottleneck limiting carbon fixation and sucrose biosynthesis. This bottleneck was subsequently mitigated through heterologous overexpression of glyceraldehyde-3-phosphate dehydrogenase in an engineered sucrose-secreting strain. Our study also demonstrates that combining 13C-MFA and GSM is a useful strategy to both extend the coverage of MFA beyond central metabolism and to improve the accuracy of flux predictions provided by GSM.

An end-to-end pipeline for succinic acid production at an industrially relevant scale using Issatchenkia orientalis

Microbial production of succinic acid (SA) at an industrially relevant scale has been hindered by high downstream processing costs arising from neutral pH fermentation for over three decades. Here, we metabolically engineer the acid-tolerant yeast Issatchenkia orientalis for SA production, attaining the highest titers in sugar-based media at low pH (pH 3) in fed-batch fermentations, i.e. 109.5 g/L in minimal medium and 104.6 g/L in sugarcane juice medium. We further perform batch fermentation using sugarcane juice medium in a pilot-scale fermenter (300×) and achieve 63.1 g/L of SA, which can be directly crystallized with a yield of 64.0%. Finally, we simulate an end-to-end low-pH SA production pipeline, and techno-economic analysis and life cycle assessment indicate our process is financially viable and can reduce greenhouse gas emissions by 34-90% relative to fossil-based production processes. We expect I. orientalis can serve as a general industrial platform for production of organic acids.

Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation

In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.

Isotopically non-stationary 13 C metabolic flux analysis of two closely related fast-growing cyanobacteria, Synechococcus elongatus PCC 11801 and 11802

Synechococcus elongatus PCC 11801 and 11802 are closely related cyanobacterial strains that are fast-growing and tolerant to high light and temperature. These strains hold significant promise as chassis for photosynthetic production of chemicals from carbon dioxide. A detailed quantitative understanding of the central carbon pathways would be a reference for future metabolic engineering studies with these strains. We conducted isotopic non-stationary 13 C metabolic flux analysis to quantitively assess the metabolic potential of these two strains. This study highlights key similarities and differences in the central carbon flux distribution between these and other model/non-model strains. The two strains demonstrated a higher Calvin-Benson-Bassham (CBB) cycle flux coupled with negligible flux through the oxidative pentose phosphate pathway and the photorespiratory pathway and lower anaplerosis fluxes under photoautotrophic conditions. Interestingly, PCC 11802 shows the highest CBB cycle and pyruvate kinase flux values among those reported in cyanobacteria. The unique tricarboxylic acid (TCA) cycle diversion in PCC 11801 makes it ideal for the large-scale production of TCA cycle-derived chemicals. Additionally, dynamic labeling transients were measured for intermediates of amino acid, nucleotide, and nucleotide sugar metabolism. Overall, this study provides the first detailed metabolic flux maps of S. elongatus PCC 11801 and 11802, which may aid metabolic engineering efforts in these strains.

FreeFlux: A Python Package for Time-Efficient Isotopically Nonstationary Metabolic Flux Analysis

13C metabolic flux analysis is a powerful tool for metabolism characterization in metabolic engineering and synthetic biology. However, the widespread adoption of this tool is hindered by limited software availability and computational efficiency. Currently, the most widely accepted 13C-flux tools, such as INCA and 13CFLUX2, are developed in a closed-source environment. While several open-source packages or software are available, they are either computationally inefficient or only suitable for flux estimation at isotopic steady state. To address the need for a time-efficient computational tool for the more complicated flux analysis at an isotopically nonstationary state, especially for understanding the single-carbon substrate metabolism, we present FreeFlux. FreeFlux is an open-source Python package that performs labeling pattern simulation and flux analysis at both isotopic steady state and transient state, enabling a more comprehensive analysis of cellular metabolism. FreeFlux provides a set of interfaces to manipulate the objects abstracted from a labeling experiment and computational process, making it easy to integrate into other programs or pipelines. The flux estimation by FreeFlux is fast and reliable, and its validity has been confirmed by comparison with results from other computational tools using both synthetic and experimental data. FreeFlux is freely available at https://github.com/Chaowu88/freeflux with a detailed online tutorial and documentation provided at https://freeflux.readthedocs.io/en/latest/index.html.

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

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

Overexpression of peroxisome proliferator-activated receptor γ co-activator-1⍺ (PGC-1⍺) in Chinese hamster ovary cells increases oxidative metabolism and IgG productivity

Chinese hamster ovary (CHO) cells are used extensively to produce protein therapeutics, such as monoclonal antibodies (mAbs), in the biopharmaceutical industry. MAbs are large proteins that are energetically demanding to synthesize and secrete; therefore, high-producing CHO cell lines that are engineered for maximum metabolic efficiency are needed to meet increasing demands for mAb production. Previous studies have identified that high-producing cell lines possess a distinct metabolic phenotype when compared to low-producing cell lines. In particular, it was found that high mAb production is correlated to lactate consumption and elevated TCA cycle flux. We hypothesized that enhancing flux through the mitochondrial TCA cycle and oxidative phosphorylation would lead to increased mAb productivities and final titers. To test this hypothesis, we overexpressed peroxisome proliferator-activated receptor γ co-activator-1⍺ (PGC-1⍺), a gene that promotes mitochondrial metabolism, in an IgG-producing parental CHO cell line. Stable cell pools overexpressing PGC-1⍺ exhibited increased oxygen consumption, indicating increased mitochondrial metabolism, as well as increased mAb specific productivity compared to the parental line. We also performed 13C metabolic flux analysis (MFA) to quantify how PGC-1⍺ overexpression alters intracellular metabolic fluxes, revealing not only increased TCA cycle flux, but global upregulation of cellular metabolic activity. This study demonstrates the potential of rationally engineering the metabolism of industrial cell lines to improve overall mAb productivity and to increase the abundance of high-producing clones in stable cell pools.

FASN deficiency induces a cytosol-to-mitochondria citrate flux to mitigate detachment-induced oxidative stress

Fatty acid synthase (FASN) maintains de novo lipogenesis (DNL) to support rapid growth in most proliferating cancer cells. Lipogenic acetyl-coenzyme A (CoA) is primarily produced from carbohydrates but can arise from glutamine-dependent reductive carboxylation. Here, we show that reductive carboxylation also occurs in the absence of DNL. In FASN-deficient cells, reductive carboxylation is mainly catalyzed by isocitrate dehydrogenase-1 (IDH1), but IDH1-generated cytosolic citrate is not utilized for supplying DNL. Metabolic flux analysis (MFA) shows that FASN deficiency induces a net cytosol-to-mitochondria citrate flux through mitochondrial citrate transport protein (CTP). Previously, a similar pathway has been shown to mitigate detachment-induced oxidative stress in anchorage-independent tumor spheroids. We further report that tumor spheroids show reduced FASN activity and that FASN-deficient cells acquire resistance to oxidative stress in a CTP- and IDH1-dependent manner. Collectively, these data indicate that by inducing a cytosol-to-mitochondria citrate flux, anchorage-independent malignant cells can gain redox capacity by trading off FASN-supported rapid growth.

MYC overrides HIF-1α to regulate proliferating primary cell metabolism in hypoxia

Hypoxia requires metabolic adaptations to sustain energetically demanding cellular activities. While the metabolic consequences of hypoxia have been studied extensively in cancer cell models, comparatively little is known about how primary cell metabolism responds to hypoxia. Thus, we developed metabolic flux models for human lung fibroblast and pulmonary artery smooth muscle cells proliferating in hypoxia. Unexpectedly, we found that hypoxia decreased glycolysis despite activation of hypoxia-inducible factor 1α (HIF-1α) and increased glycolytic enzyme expression. While HIF-1α activation in normoxia by prolyl hydroxylase (PHD) inhibition did increase glycolysis, hypoxia blocked this effect. Multi-omic profiling revealed distinct molecular responses to hypoxia and PHD inhibition, and suggested a critical role for MYC in modulating HIF-1α responses to hypoxia. Consistent with this hypothesis, MYC knockdown in hypoxia increased glycolysis and MYC over-expression in normoxia decreased glycolysis stimulated by PHD inhibition. These data suggest that MYC signaling in hypoxia uncouples an increase in HIF-dependent glycolytic gene transcription from glycolytic flux.

Systems biology and metabolic modeling for cultivated meat: A promising approach for cell culture media optimization and cost reduction

The cultivated meat industry, also known as cell-based meat, cultured meat, lab-grown meat, or meat alternatives, is a growing field that aims to generate animal tissues ex-vivo in a cost-effective manner that achieves price parity with traditional agricultural products. However, cell culture media costs account for 55%-90% of production costs. To address this issue, efforts are aimed at optimizing media composition. Systems biology-driven approaches have been successfully used to improve the biomass and productivity of multiple bioproduction platforms, like Chinese hamster ovary cells, by accelerating the development of cell line-specific media and reducing research and development and production costs related to cell media and its optimization. In this review, we summarize systems biology modeling approaches, methods for cell culture media and bioprocess optimization, and metabolic studies done in animals of interest to the cultivated meat industry. More importantly, we identify current gaps in knowledge that prevent the identification of metabolic bottlenecks. These include the lack of genome-scale metabolic models for some species (pigs and ducks), a lack of accurate biomass composition studies for different growth conditions, and 13 C-metabolic flux analysis (MFA) studies for many of the species of interest for the cultivated meat industry (only shrimp and duck cells have been subjected to 13 C-MFA). We also highlight the importance of characterizing the metabolic requirements of cells at the organism, breed, and cell line-specific levels, and we outline future steps that this nascent field needs to take to achieve price parity and production efficiency similar to those of other bioproduction platforms. Practical Application: Our article summarizes systems biology techniques for cell culture media design and bioprocess optimization, which may be used to significantly reduce cell-based meat production costs. We also present the results of experimental studies done on some of the species of interest to the cultivated meat industry and highlight why modeling approaches are required for multiple species, cell-types, and cell lines.

Systematic comparison of local approaches for isotopically nonstationary metabolic flux analysis

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

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.

Automating the design-build-test-learn cycle towards next-generation bacterial cell factories

Automation is playing an increasingly significant role in synthetic biology. Groundbreaking technologies, developed over the past 20 years, have enormously accelerated the construction of efficient microbial cell factories. Integrating state-of-the-art tools (e.g. for genome engineering and analytical techniques) into the design-build-test-learn cycle (DBTLc) will shift the metabolic engineering paradigm from an almost artisanal labor towards a fully automated workflow. Here, we provide a perspective on how a fully automated DBTLc could be harnessed to construct the next-generation bacterial cell factories in a fast, high-throughput fashion. Innovative toolsets and approaches that pushed the boundaries in each segment of the cycle are reviewed to this end. We also present the most recent efforts on automation of the DBTLc, which heralds a fully autonomous pipeline for synthetic biology in the near future.

An automated workflow for multi-omics screening of microbial model organisms

Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation might enable to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.

Inter-organelle cross-talk supports acetyl-coenzyme A homeostasis and lipogenesis under metabolic stress

Proliferating cells rely on acetyl-CoA to support membrane biogenesis and acetylation. Several organelle-specific pathways are available for provision of acetyl-CoA as nutrient availability fluctuates, so understanding how cells maintain acetyl-CoA homeostasis under such stresses is critically important. To this end, we applied 13C isotope tracing cell lines deficient in these mitochondrial [ATP-citrate lyase (ACLY)]-, cytosolic [acetyl-CoA synthetase (ACSS2)]-, and peroxisomal [peroxisomal biogenesis factor 5 (PEX5)]-dependent pathways. ACLY knockout in multiple cell lines reduced fatty acid synthesis and increased reliance on extracellular lipids or acetate. Knockout of both ACLY and ACSS2 (DKO) severely stunted but did not entirely block proliferation, suggesting that alternate pathways can support acetyl-CoA homeostasis. Metabolic tracing and PEX5 knockout studies link peroxisomal oxidation of exogenous lipids as a major source of acetyl-CoA for lipogenesis and histone acetylation in cells lacking ACLY, highlighting a role for inter-organelle cross-talk in supporting cell survival in response to nutrient fluctuations.

Bayesian kinetic modeling for tracer-based metabolomic data

BACKGROUND

Stable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly [Formula: see text]-enriched glucose ([Formula: see text]-Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups.

RESULTS

We propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts' knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux .

CONCLUSIONS

Our new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data.

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

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

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

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

FASN-deficiency induces a cytosol-to-mitochondria citrate flux to mitigate detachment-induced oxidative stress

Fatty acid synthase (FASN) maintains de novo lipogenesis (DNL) to support rapid growth in most proliferating cancer cells. Lipogenic acetyl-CoA is primarily produced from carbohydrates but can arise from glutamine-dependent reductive carboxylation under hypoxia. Here we show that reductive carboxylation also occurs in the absence of DNL in cells with defective FASN. In this state, reductive carboxylation was mainly catalyzed by isocitrate dehydrogenase-1 (IDH1) in the cytosol, but IDH1-generated citrate was not used for DNL. Metabolic flux analysis (MFA) revealed that FASN-deficiency induced a net cytosol-to-mitochondria citrate flux through citrate transport protein (CTP). A similar pathway was previously shown to mitigate detachment-induced mitochondrial reactive oxygen species (mtROS) in anchorage-independent tumor spheroids. We further demonstrate that FASN-deficient cells acquire resistance to oxidative stress in a CTP- and IDH1-dependent manner. Together with the reduced FASN activity in tumor spheroids, these data indicate that anchorage-independent malignant cells trade FASN-supported rapid growth for a cytosol-to-mitochondria citrate flux to gain redox capacity against detachment-induced oxidative stress.

Oxidation of hyperpolarized [1-13 C]pyruvate in isolated rat kidneys

Kidneys play a central role in numerous disorders but current imaging methods have limited utility to probe renal metabolism. Hyperpolarized (HP) 13 C magnetic resonance imaging is uniquely suited to provide metabolite-specific information about key biochemical pathways and it offers the further advantage that renal imaging is practical in humans. This study evaluated the feasibility of hyperpolarization examinations in a widely used model for analysis of renal physiology, the isolated kidney, which enables isolation of renal metabolism from the effects of other organs and validation of HP results by independent measurements. Isolated rat kidneys were supplied with either HP [1-13 C]pyruvate only or HP [1-13 C]pyruvate plus octanoate. Metabolic activity in both groups was confirmed by stable renal oxygen consumption. HP [1-13 C]pyruvate was readily metabolized to [13 C]bicarbonate, [1-13 C]lactate, and [1-13 C]alanine, detectable seconds after HP [1-13 C]pyruvate was injected. Octanoate suppressed but did not eliminate the production of HP [13 C]bicarbonate from [1-13 C]pyruvate. Steady-state flux analyses using non-HP 13 C substrates validated the utilization of HP [1-13 C]pyruvate, as observed by HP 13 C NMR. In the presence of octanoate, lactate is generated from a tricarboxylic acid cycle intermediate, oxaloacetate. The isolated rat kidney may serve as an excellent model for investigating and establishing new HP 13 C metabolic probes for future kidney imaging applications.

Improved 13C metabolic flux analysis in Escherichia coli metabolism: application of a high-resolution MS (GC-EI-QTOF) for comprehensive assessment of MS/MS fragments

Gas chromatography-tandem mass spectrometry with electron ionization (GC-EI-MS/MS) provides rich information on stable-isotope labeling for 13C-metabolic flux analysis (13C-MFA). To pave the way for the routine application of tandem MS data for metabolic flux quantification, we aimed to compile a comprehensive library of GC-EI-MS/MS fragments of tert-butyldimethylsilyl (TBDMS) derivatized proteinogenic amino acids. First, we established an analytical workflow that combines high-resolution gas chromatography-quadrupole time-of-flight mass spectrometry and fully 13C-labeled biomass to identify and structurally elucidate tandem MS amino acid fragments. Application of the high-mass accuracy MS procedure resulted into the identification of 129 validated precursor-product ion pairs of 13 amino acids with 30 fragments being accepted for 13C-MFA. The practical benefit of the novel tandem MS data was demonstrated by a proof-of-concept study, which confirmed the importance of the compiled library for high-resolution 13C-MFA.

ONE SENTENCE SUMMARY

An analytical workflow that combines high-resolution mass spectrometry (MS) and fully 13C-labeled biomass to identify and structurally elucidate tandem MS amino acid fragments, which provide positional information and therefore offering significant advantages over traditional MS to improve 13C-metabolic flux analysis.

Enrichment of hepatic glycogen and plasma glucose from H₂18 O informs gluconeogenic and indirect pathway fluxes in naturally feeding mice

Deuterated water (2 H2 O) is a widely used tracer of carbohydrate biosynthesis in both preclinical and clinical settings, but the significant kinetic isotope effects (KIE) of 2 H can distort metabolic information and mediate toxicity. 18 O-water (H2 18 O) has no significant KIE and is incorporated into specific carbohydrate oxygens via well-defined mechanisms, but to date it has not been evaluated in any animal model. Mice were given H2 18 O during overnight feeding and 18 O-enrichments of liver glycogen, triglyceride glycerol (TG), and blood glucose were quantified by 13 C NMR and mass spectrometry (MS). Enrichment of oxygens 5 and 6 relative to body water informed indirect pathway contributions from the Krebs cycle and triose phosphate sources. Compared with mice fed normal chow (NC), mice whose NC was supplemented with a fructose/glucose mix (i.e., a high sugar [HS] diet) had significantly higher indirect pathway contributions from triose phosphate sources, consistent with fructose glycogenesis. Blood glucose and liver TG 18 O-enrichments were quantified by MS. Blood glucose 18 O-enrichment was significantly higher for HS versus NC mice and was consistent with gluconeogenic fructose metabolism. TG 18 O-enrichment was extensive for both NC and HS mice, indicating a high turnover of liver triglyceride, independent of diet. Thus H2 18 O informs hepatic carbohydrate biosynthesis in similar detail to 2 H2 O but without KIE-associated risks.

Parameter Identification in Metabolic Reaction Networks by Means of Multiple Steady-State Measurements

In this work, we investigate some theoretical aspects related to the estimation approach proposed by Liebermeister and Klipp, 2006, in which general rate laws, derived from standardized enzymatic mechanisms, are exploited to kinetically describe the fluxes of a metabolic reaction network, and multiple metabolic steady-state measurements are exploited to estimate the unknown kinetic parameters. Further mathematical details are deeply investigated, and necessary conditions on the amount of information required to solve the identification problem are given. Moreover, theoretical results for the parameter identifiability are provided, and symmetrical and modular properties of the proposed approach are highlighted when the global identification problem is decoupled into smaller and simpler identification problems related to the single reactions of the network. Among the advantages of the proposed innovative approach are (i) non-restrictive conditions to guarantee the solvability of the parameter estimation problem, (ii) the unburden of the usual computational complexity for such identification problems, and (iii) the ease of obtaining the required number of measurements, which are actually steady-state data, experimentally easier to obtain with respect to the time-dependent ones. A simple example concludes the paper, highlighting the mentioned advantages of the method and the implementation of the related theoretical result.

Exogenous pyruvate and recombinant human basic fibroblast growth factor maintain pluripotency and enhance global metabolic activity of bovine embryonic stem cells grown on low-density feeder layers

A suitable microenvironment or niche is essential for self-renewal and pluripotency of stem cells cultured in vitro, including bovine embryonic stem cells (bESCs). Feeder cells participate in the construction of stem cell niche by secreting growth factors and extracellular matrix proteins. In this study, metabolomics and transcriptomics analyses were used to investigate the effects of low-density feeder cells on bESCs. The results showed that bESCs co-cultured with low-density feeder cells experienced a decrease in pluripotent gene expression, cell differentiation, and a reduction of central carbon metabolic activity. When cell-permeable pyruvate (Pyr) and recombinant human basic fibroblast growth factor (rhbFGF) were added to the culture system, the pluripotency of bESCs on low-density feeder layers was rescued, and acetyl-coenzyme A (AcCoA) synthesis and fatty acid de novo synthesis increased. In addition, rhbFGF enhances the effects of Pyr and activates the overall metabolic level of bESCs grown on low-density feeder layers. This study explored the rescue effects of exogenous Pyr and rhbFGF on bESCs cultured on low-density feeder layers, which will provide a reference for improvement of the PSC culture system through the supplementation of energy metabolites and growth factors.

Isotopically non-stationary metabolic flux analysis of heterotrophic Arabidopsis thaliana cell cultures

Fluxes are the ultimate phenotype of metabolism and their accurate quantification is fundamental to any understanding of metabolic networks. Steady state metabolic flux analysis has been the method of choice for quantifying fluxes in heterotrophic cells, but it is unable to measure fluxes during short-lived metabolic states, such as a transient oxidative load. Isotopically non-stationary metabolic flux analysis (INST-MFA) can be performed over shorter timescales (minutes - hours) and might overcome this limitation. INST-MFA has recently been applied to photosynthesising leaves, but agriculturally important tissues such as roots and storage organs, or plants during the night are heterotrophic. Here we outline the application of INST-MFA to heterotrophic plant cells. Using INST-MFA we were able to identify changes in the fluxes supported by phosphoenolpyruvate carboxylase and malic enzyme under oxidative load, highlighting the potential of INST-MFA to measure fluxes during short-lived metabolic states. We discuss the challenges in applying INST-MFA, and highlight further development required before it can be routinely used to quantify fluxes in heterotrophic plant cells.

Q-Flux: A method to assess hepatic mitochondrial succinate dehydrogenase, methylmalonyl-CoA mutase, and glutaminase fluxes in vivo

The mammalian succinate dehydrogenase (SDH) complex has recently been shown as capable of operating bidirectionally. Here, we develop a method (Q-Flux) capable of measuring absolute rates of both forward (VSDH(F)) and reverse (VSDH(R)) flux through SDH in vivo while also deconvoluting the amount of glucose derived from four discreet carbon sources in the liver. In validation studies, a mitochondrial uncoupler increased net SDH flux by >100% in awake rodents but also increased SDH cycling. During hyperglucagonemia, attenuated pyruvate cycling enhances phosphoenolpyruvate carboxykinase efficiency to drive increased gluconeogenesis, which is complemented by increased glutaminase (GLS) flux, methylmalonyl-CoA mutase (MUT) flux, and glycerol conversion to glucose. During hyperinsulinemic-euglycemic clamp, both pyruvate carboxylase and GLS are suppressed, while VSDH(R) is increased. Unstimulated MUT is a minor anaplerotic reaction but is readily induced by small amounts of propionate, which elicits glucagon-like metabolic rewiring. Taken together, Q-Flux yields a comprehensive picture of hepatic mitochondrial metabolism and should be broadly useful to researchers.

Stochastic simulation algorithm for isotope-based dynamic flux analysis

Carbon isotope labeling method is a standard metabolic engineering tool for flux quantification in living cells. To cope with the high dimensionality of isotope labeling systems, diverse algorithms have been developed to reduce the number of variables or operations in metabolic flux analysis (MFA), but lacks generalizability to non-stationary metabolic conditions. In this study, we present a stochastic simulation algorithm (SSA) derived from the chemical master equation of the isotope labeling system. This algorithm allows to compute the time evolution of isotopomer concentrations in non-stationary conditions, with the valuable property that computational time does not scale with the number of isotopomers. The efficiency and limitations of the algorithm is benchmarked for the forward and inverse problems of 13C-DMFA in the pentose phosphate pathways, and is compared with EMU-based methods for NMFA and MFA including the central carbon metabolism. Overall, SSA constitutes an alternative class to deterministic approaches for metabolic flux analysis that is well adapted to comprehensive dataset including parallel labeling experiments, and whose limitations associated to the sampling size can be overcome by using Monte Carlo sampling approaches.

Integrated flux and pool size analysis in plant central metabolism reveals unique roles of glycine and serine during photorespiration

Photorespiration is an essential process juxtaposed between plant carbon and nitrogen metabolism that responds to dynamic environments. Photorespiration recycles inhibitory intermediates arising from oxygenation reactions catalysed by Rubisco back into the C₃ cycle, but it is unclear what proportions of its nitrogen-containing intermediates (glycine and serine) are exported into other metabolisms in vivo and how these pool sizes affect net CO₂ gas exchange during photorespiratory transients. Here, to address this uncertainty, we measured rates of amino acid export from photorespiration using isotopically non-stationary metabolic flux analysis. This analysis revealed that ~23-41% of the photorespiratory carbon was exported from the pathway as serine under various photorespiratory conditions. Furthermore, we determined that the build-up and relaxation of glycine pools constrained a large portion of photosynthetic acclimation during photorespiratory transients. These results reveal the unique and important roles of glycine and serine in successfully maintaining various photorespiratory fluxes that occur under environmental fluctuations in nature and providing carbon and nitrogen for metabolism.

Engineering of Pseudomonas putida for accelerated co-utilization of glucose and cellobiose yields aerobic overproduction of pyruvate explained by an upgraded metabolic model

Pseudomonas putida KT2440 is an attractive bacterial host for biotechnological production of valuable chemicals from renewable lignocellulosic feedstocks as it can valorize lignin-derived aromatics or glucose obtainable from cellulose. P. putida EM42, a genome-reduced variant of strain KT2440 endowed with advantageous physiological properties, was recently engineered for growth on cellobiose, a major cellooligosaccharide product of enzymatic cellulose hydrolysis. Co-utilization of cellobiose and glucose was achieved in a mutant lacking periplasmic glucose dehydrogenase Gcd (PP_1444). However, the cause of the co-utilization phenotype remained to be understood and the Δgcd strain had a significant growth defect. In this study, we investigated the basis of the simultaneous uptake of the two sugars and accelerated the growth of P. putida EM42 Δgcd mutant for the bioproduction of valuable compounds from glucose and cellobiose. We show that the gcd deletion lifted the inhibition of the exogenous β-glucosidase BglC from Thermobifida fusca exerted by the intermediates of the periplasmic glucose oxidation pathway. The additional deletion of hexR gene, which encodes a repressor of the upper glycolysis genes, failed to restore rapid growth on glucose. The reduced growth rate of the Δgcd mutant was partially compensated by the implantation of heterologous glucose and cellobiose transporters (Glf from Zymomonas mobilis and LacY from Escherichia coli, respectively). Remarkably, this intervention resulted in the accumulation of pyruvate in aerobic P. putida cultures. We demonstrated that the excess of this key metabolic intermediate can be redirected to the enhanced biosynthesis of ethanol and lactate. The pyruvate overproduction phenotype was then unveiled by an upgraded genome-scale metabolic model constrained with proteomic and kinetic data. The model pointed to the saturation of glucose catabolism enzymes due to unregulated substrate uptake and it predicted improved bioproduction of pyruvate-derived chemicals by the engineered strain. This work sheds light on the co-metabolism of cellulosic sugars in an attractive biotechnological host and introduces a novel strategy for pyruvate overproduction in bacterial cultures under aerobic conditions.