Exercise-induced changes in myocardial glucose utilization during periods of active cardiac growth.

Exercise training can promote physiological cardiac growth, which has been suggested to involve changes in glucose metabolism to facilitate hypertrophy of cardiomyocytes. In this study, we used a dietary, in vivo isotope labeling approach to examine how exercise training influences the metabolic fate of carbon derived from dietary glucose in the heart during acute, active, and established phases of exercise-induced cardiac growth. Male and female FVB/NJ mice were subjected to treadmill running for up to 4 weeks and cardiac growth was assessed by gravimetry. Cardiac metabolic responses to exercise were assessed via in vivo tracing of [13C6]-glucose via mass spectrometry and nuclear magnetic resonance. We found that the half-maximal cardiac growth response was achieved by approximately 1 week of daily exercise training, with near maximal growth observed in male mice with 2 weeks of training; however, female mice were recalcitrant to exercise-induced cardiac growth and required a higher daily intensity of exercise training to achieve significant, albeit modest, increases in cardiac mass. We also found that increases in the energy charge of adenylate and guanylate nucleotide pools precede exercise-induced changes in cardiac size and were associated with higher glucose tracer enrichment in the TCA pool and in amino acids (aspartate, glutamate) sourced by TCA intermediates. Our data also indicate that the activity of collateral biosynthetic pathways of glucose metabolism may not be markedly altered by exercise. Overall, this study provides evidence that metabolic remodeling in the form of heightened energy charge and increased TCA cycle activity and cataplerosis precedes cardiac growth caused by exercise training in male mice.

Cyclin D1 extensively reprograms metabolism to support biosynthetic pathways in hepatocytes.

Cell proliferation requires metabolic reprogramming to accommodate biosynthesis of new cell components, and similar alterations occur in cancer cells. However, the mechanisms linking the cell cycle machinery to metabolism are not well defined. Cyclin D1, along with its main partner cyclin-dependent kinase 4 (Cdk4), is a pivotal cell cycle regulator and driver oncogene that is overexpressed in many cancers. Here, we examine hepatocyte proliferation to define novel effects of cyclin D1 on biosynthetic metabolism. Metabolomic studies reveal that cyclin D1 broadly promotes biosynthetic pathways including glycolysis, the pentose phosphate pathway, and the purine and pyrimidine nucleotide synthesis in hepatocytes. Proteomic analyses demonstrate that overexpressed cyclin D1 binds to numerous metabolic enzymes including those involved in glycolysis and pyrimidine synthesis. In the glycolysis pathway, cyclin D1 activates aldolase and GAPDH, and these proteins are phosphorylated by cyclin D1/Cdk4 in vitro. De novo pyrimidine synthesis is particularly dependent on cyclin D1. Cyclin D1/Cdk4 phosphorylates the initial enzyme of this pathway, carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase (CAD), and metabolomic analysis indicates that cyclin D1 depletion markedly reduces the activity of this enzyme. Pharmacologic inhibition of Cdk4 along with the downstream pyrimidine synthesis enzyme dihydroorotate dehydrogenase synergistically inhibits proliferation and survival of hepatocellular carcinoma cells. These studies demonstrate that cyclin D1 promotes a broad network of biosynthetic pathways in hepatocytes, and this model may provide insights into potential metabolic vulnerabilities in cancer cells.

Stable isotope-resolved metabolomics analyses of metabolic phenotypes reveal variable glutamine metabolism in different patient-derived models of non-small cell lung cancer from a single patient.

INTRODUCTION: Stable isotope tracers have been increasingly used in preclinical cancer model systems, including cell culture and mouse xenografts, to probe the altered metabolism of a variety of cancers, such as accelerated glycolysis and glutaminolysis and generation of oncometabolites. Comparatively little has been reported on the fidelity of the different preclinical model systems in recapitulating the aberrant metabolism of tumors. OBJECTIVES: We have been developing several different experimental model systems for systems biochemistry analyses of non-small cell lung cancer (NSCLC1) using patient-derived tissues to evaluate appropriate models for metabolic and phenotypic analyses. METHODS: To address the issue of fidelity, we have carried out a detailed Stable Isotope-Resolved Metabolomics study of freshly resected tissue slices, mouse patient derived xenografts (PDXs), and cells derived from a single patient using both 13C6-glucose and 13C5,15N2-glutamine tracers. RESULTS: Although we found similar glucose metabolism in the three models, glutamine utilization was markedly higher in the isolated cell culture and in cell culture-derived xenografts compared with the primary cancer tissue or direct tissue xenografts (PDX). CONCLUSIONS: This suggests that caution is needed in interpreting cancer biochemistry using patient-derived cancer cells in vitro or in xenografts, even at very early passage, and that direct analysis of patient derived tissue slices provides the optimal model for ex vivo metabolomics. Further research is needed to determine the generality of these observations.

Polarization and ß-Glucan Reprogram Immunomodulatory Metabolism in Human Macrophages and Ex Vivo in Human Lung Cancer Tissues.

Immunomodulatory (IM) metabolic reprogramming in macrophages (Mϕs) is fundamental to immune function. However, limited information is available for human Mϕs, particularly in response plasticity, which is critical to understanding the variable efficacy of immunotherapies in cancer patients. We carried out an in-depth analysis by combining multiplex stable isotope-resolved metabolomics with reversed phase protein array to map the dynamic changes of the IM metabolic network and key protein regulators in four human donors' Mϕs in response to differential polarization and M1 repolarizer ß-glucan (whole glucan particles [WGPs]). These responses were compared with those of WGP-treated ex vivo organotypic tissue cultures (OTCs) of human non-small cell lung cancer. We found consistently enhanced tryptophan catabolism with blocked NAD+ and UTP synthesis in M1-type Mϕs (M1-Mϕs), which was associated with immune activation evidenced by increased release of IL-1ß/CXCL10/IFN-γ/TNF-α and reduced phagocytosis. In M2a-Mϕs, WGP treatment of M2a-Mϕs robustly increased glucose utilization via the glycolysis/oxidative branch of the pentose phosphate pathway while enhancing UDP-N-acetyl-glucosamine turnover and glutamine-fueled gluconeogenesis, which was accompanied by the release of proinflammatory IL-1ß/TNF-α to above M1-Mϕ's levels, anti-inflammatory IL-10 to above M2a-Mϕ's levels, and attenuated phagocytosis. These IM metabolic responses could underlie the opposing effects of WGP, i.e., reverting M2- to M1-type immune functions but also boosting anti-inflammation. Variable reprogrammed Krebs cycle and glutamine-fueled synthesis of UTP in WGP-treated OTCs of human non-small cell lung cancer were observed, reflecting variable M1 repolarization of tumor-associated Mϕs. This was supported by correlation with IL-1ß/TNF-α release and compromised tumor status, making patient-derived OTCs unique models for studying variable immunotherapeutic efficacy in cancer patients.

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.

Ultrahigh resolution MS1/MS2-based reconstruction of metabolic networks in mammalian cells reveals changes for selenite and arsenite action.

Metabolic networks are complex, intersecting, and composed of numerous enzyme-catalyzed biochemical reactions that transfer various molecular moieties among metabolites. Thus, robust reconstruction of metabolic networks requires metabolite moieties to be tracked, which cannot be readily achieved with mass spectrometry (MS) alone. We previously developed an Ion Chromatography-ultrahigh resolution-MS1/data independent-MS2 method to track the simultaneous incorporation of the heavy isotopes 13C and 15N into the moieties of purine/pyrimidine nucleotides in mammalian cells. Ultrahigh resolution-MS1 resolves and counts multiple tracer atoms in intact metabolites, while data independent-tandem MS (MS2) determines isotopic enrichment in their moieties without concern for the numerous mass isotopologue source ions to be fragmented. Together, they enabled rigorous MS-based reconstruction of metabolic networks at specific enzyme levels. We have expanded this approach to trace the labeled atom fate of [13C6]-glucose in 3D A549 spheroids in response to the anticancer agent selenite and that of [13C5,15N2]-glutamine in 2D BEAS-2B cells in response to arsenite transformation. We deduced altered activities of specific enzymes in the Krebs cycle, pentose phosphate pathway, gluconeogenesis, and UDP-GlcNAc synthesis pathways elicited by the stressors. These metabolic details help elucidate the resistance mechanism of 3D versus 2D A549 cultures to selenite and metabolic reprogramming that can mediate the transformation of BEAS-2B cells by arsenite.

A negative reciprocal regulatory axis between cyclin D1 and HNF4α modulates cell cycle progression and metabolism in the liver.

Hepatocyte nuclear factor 4α (HNF4α) is a master regulator of liver function and a tumor suppressor in hepatocellular carcinoma (HCC). In this study, we explore the reciprocal negative regulation of HNF4α and cyclin D1, a key cell cycle protein in the liver. Transcriptomic analysis of cultured hepatocyte and HCC cells found that cyclin D1 knockdown induced the expression of a large network of HNF4α-regulated genes. Chromatin immunoprecipitation-sequencing (ChIP-seq) demonstrated that cyclin D1 inhibits the binding of HNF4α to thousands of targets in the liver, thereby diminishing the expression of associated genes that regulate diverse metabolic activities. Conversely, acute HNF4α deletion in the liver induces cyclin D1 and hepatocyte cell cycle progression; concurrent cyclin D1 ablation blocked this proliferation, suggesting that HNF4α maintains proliferative quiescence in the liver, at least, in part, via repression of cyclin D1. Acute cyclin D1 deletion in the regenerating liver markedly inhibited hepatocyte proliferation after partial hepatectomy, confirming its pivotal role in cell cycle progression in this in vivo model, and enhanced the expression of HNF4α target proteins. Hepatocyte cyclin D1 gene ablation caused markedly increased postprandial liver glycogen levels (in a HNF4α-dependent fashion), indicating that the cyclin D1-HNF4α axis regulates glucose metabolism in response to feeding. In AML12 hepatocytes, cyclin D1 depletion led to increased glucose uptake, which was negated if HNF4α was depleted simultaneously, and markedly elevated glycogen synthesis. To summarize, mutual repression by cyclin D1 and HNF4α coordinately controls the cell cycle machinery and metabolism in the liver.

In vivo deep network tracing reveals phosphofructokinase-mediated coordination of biosynthetic pathway activity in the myocardium.

Glucose metabolism comprises numerous amphibolic metabolites that provide precursors for not only the synthesis of cellular building blocks but also for ATP production. In this study, we tested how phosphofructokinase-1 (PFK1) activity controls the fate of glucose-derived carbon in murine hearts in vivo. PFK1 activity was regulated by cardiac-specific overexpression of kinase- or phosphatase-deficient 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase transgenes in mice (termed GlycoLo or GlycoHi mice, respectively). Dietary delivery of 13C6-glucose to these mice, followed by deep network metabolic tracing, revealed that low rates of PFK1 activity promote selective routing of glucose-derived carbon to the purine synthesis pathway to form 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR). Consistent with a mechanism of physical channeling, we found multimeric protein complexes that contained phosphoribosylaminoimidazole carboxylase (PAICS)-an enzyme important for AICAR biosynthesis, as well as chaperone proteins such as Hsp90 and other metabolic enzymes. We also observed that PFK1 influenced glucose-derived carbon deposition in glycogen, but did not affect hexosamine biosynthetic pathway activity. These studies demonstrate the utility of deep network tracing to identify metabolic channeling and changes in biosynthetic pathway activity in the heart in vivo and present new potential mechanisms by which metabolic branchpoint reactions modulate biosynthetic pathways.

Resolving Enantiomers of 2-Hydroxy Acids by Nuclear Magnetic Resonance.

Biologically important 2-hydroxy carboxylates such as lactate, malate, and 2-hydroxyglutarate exist in two enantiomeric forms that cannot be distinguished under achiral conditions. The D and L (or R, S) enantiomers have different biological origins and functions, and therefore, there is a need for a simple method for resolving, identifying, and quantifying these enantiomers. We have adapted and improved a chiral derivatization technique for nuclear magnetic resonance (NMR), which needs no chromatography for enantiomer resolution, with greater than 90% overall recovery. This method was developed for 2-hydroxyglutarate (2HG) to produce diastereomers resolvable by column chromatography. We have applied the method to lactate, malate, and 2HG. The limit of quantification was determined to be about 1 nmol for 2HG with coefficients of variation of less than 5%. We also demonstrated the method on an extract of a renal carcinoma bearing an isocitrate dehydrogenase-2 (IDH2) variant that produces copious quantities of 2HG and showed that it is the D enantiomer that was exclusively produced. We also demonstrated in the same experiment that the lactate produced in the same sample was the L enantiomer.

An obligatory role for neurotensin in high-fat-diet-induced obesity.

Obesity and its associated comorbidities (for example, diabetes mellitus and hepatic steatosis) contribute to approximately 2.5 million deaths annually and are among the most prevalent and challenging conditions confronting the medical profession. Neurotensin (NT; also known as NTS), a 13-amino-acid peptide predominantly localized in specialized enteroendocrine cells of the small intestine and released by fat ingestion, facilitates fatty acid translocation in rat intestine, and stimulates the growth of various cancers. The effects of NT are mediated through three known NT receptors (NTR1, 2 and 3; also known as NTSR1, 2, and NTSR3, respectively). Increased fasting plasma levels of pro-NT (a stable NT precursor fragment produced in equimolar amounts relative to NT) are associated with increased risk of diabetes, cardiovascular disease and mortality; however, a role for NT as a causative factor in these diseases is unknown. Here we show that NT-deficient mice demonstrate significantly reduced intestinal fat absorption and are protected from obesity, hepatic steatosis and insulin resistance associated with high fat consumption. We further demonstrate that NT attenuates the activation of AMP-activated protein kinase (AMPK) and stimulates fatty acid absorption in mice and in cultured intestinal cells, and that this occurs through a mechanism involving NTR1 and NTR3 (also known as sortilin). Consistent with the findings in mice, expression of NT in Drosophila midgut enteroendocrine cells results in increased lipid accumulation in the midgut, fat body, and oenocytes (specialized hepatocyte-like cells) and decreased AMPK activation. Remarkably, in humans, we show that both obese and insulin-resistant subjects have elevated plasma concentrations of pro-NT, and in longitudinal studies among non-obese subjects, high levels of pro-NT denote a doubling of the risk of developing obesity later in life. Our findings directly link NT with increased fat absorption and obesity and suggest that NT may provide a prognostic marker of future obesity and a potential target for prevention and treatment.

NMR Analysis of Carboxylate Isotopomers of 13C-Metabolites by Chemoselective Derivatization with 15N-Cholamine.

A substantial fraction of common metabolites contains carboxyl functional groups. Their 13C isotopomer analysis by nuclear magnetic resonance (NMR) is hampered by the low sensitivity of the 13C nucleus, the slow longitudinal relaxation for the lack of an attached proton, and the relatively low chemical shift dispersion of carboxylates. Chemoselective (CS) derivatization is a means of tagging compounds in a complex mixture via a specific functional group. 15N1-cholamine has been shown to be a useful CS agent for carboxylates, producing a peptide bond that can be detected via 15N-attached H with high sensitivity in heteronuclear single quantum coherence experiments. Here, we report an improved method of derivatization and show how 13C-enrichment at the carboxylate and/or the adjacent carbon can be determined via one- and two-bond coupling of the carbons adjacent to the cholamine 15N atom in the derivatives. We have applied this method for the determination of 13C isotopomer distribution in the extracts of A549 cell culture and liver tissue from a patient-derived xenograft mouse.

An Ion Chromatography-Ultrahigh-Resolution-MS1/Data-Independent High-Resolution MS2 Method for Stable Isotope-Resolved Metabolomics Reconstruction of Central Metabolic Networks.

The metabolome comprises a complex network of interconnecting enzyme-catalyzed reactions that involve transfers of numerous molecular subunits. Thus, the reconstruction of metabolic networks requires metabolite substructures to be tracked. Subunit tracking can be achieved by tracing stable isotopes through metabolic transformations using NMR and ultrahigh -resolution (UHR)-mass spectrometry (MS). UHR-MS1 readily resolves and counts isotopic labels in metabolites but requires tandem MS to help identify isotopic enrichment in substructures. However, it is challenging to perform chromatography-based UHR-MS1 with its long acquisition time, while acquiring MS2 data on many coeluting labeled isotopologues for each metabolite. We have developed an ion chromatography (IC)-UHR-MS1/data-independent(DI)-HR-MS2 method to trace the fate of 13C atoms from [13C6]-glucose ([13C6]-Glc) in 3D A549 spheroids in response to anticancer selenite and simultaneously 13C/15N atoms from [13C5,15N2]-glutamine ([13C5,15N2]-Gln) in 2D BEAS-2B cells in response to arsenite transformation. This method retains the complete isotopologue distributions of metabolites via UHR-MS1 while simultaneously acquiring substructure label information via DI-MS2. These details in metabolite labeling patterns greatly facilitate rigorous reconstruction of multiple, intersecting metabolic pathways of central metabolism, which are illustrated here for the purine/pyrimidine nucleotide biosynthesis. The pathways reconstructed based on subunit-level isotopologue analysis further reveal specific enzyme-catalyzed reactions that are impacted by selenite or arsenite treatments.

JAK2-mutant hematopoietic cells display metabolic alterations that can be targeted to treat myeloproliferative neoplasms.

Increased energy requirement and metabolic reprogramming are hallmarks of cancer cells. We show that metabolic alterations in hematopoietic cells are fundamental to the pathogenesis of mutant JAK2-driven myeloproliferative neoplasms (MPNs). We found that expression of mutant JAK2 augmented and subverted metabolic activity of MPN cells, resulting in systemic metabolic changes in vivo, including hypoglycemia, adipose tissue atrophy, and early mortality. Hypoglycemia in MPN mouse models correlated with hyperactive erythropoiesis and was due to a combination of elevated glycolysis and increased oxidative phosphorylation. Modulating nutrient supply through high-fat diet improved survival, whereas high-glucose diet augmented the MPN phenotype. Transcriptomic and metabolomic analyses identified numerous metabolic nodes in JAK2-mutant hematopoietic stem and progenitor cells that were altered in comparison with wild-type controls. We studied the consequences of elevated levels of Pfkfb3, a key regulatory enzyme of glycolysis, and found that pharmacological inhibition of Pfkfb3 with the small molecule 3PO reversed hypoglycemia and reduced hematopoietic manifestations of MPNs. These effects were additive with the JAK1/2 inhibitor ruxolitinib in vivo and in vitro. Inhibition of glycolysis by 3PO altered the redox homeostasis, leading to accumulation of reactive oxygen species and augmented apoptosis rate. Our findings reveal the contribution of metabolic alterations to the pathogenesis of MPNs and suggest that metabolic dependencies of mutant cells represent vulnerabilities that can be targeted for treating MPNs.

De novo synthesis of serine and glycine fuels purine nucleotide biosynthesis in human lung cancer tissues.

Nucleotide synthesis is essential to proliferating cells, but the preferred precursors for de novo biosynthesis are not defined in human cancer tissues. We have employed multiplexed stable isotope-resolved metabolomics to track the metabolism of [13C6]glucose, D2-glycine, [13C2]glycine, and D3-serine into purine nucleotides in freshly resected cancerous and matched noncancerous lung tissues from nonsmall cell lung cancer (NSCLC) patients, and we compared the metabolism with established NSCLC PC9 and A549 cell lines in vitro Surprisingly, [13C6]glucose was the best carbon source for purine synthesis in human NSCLC tissues, in contrast to the noncancerous lung tissues from the same patient, which showed lower mitotic indices and MYC expression. We also observed that D3-Ser was preferentially incorporated into purine rings over D2-glycine in both tissues and cell lines. MYC suppression attenuated [13C6]glucose, D3-serine, and [13C2]glycine incorporation into purines and reduced proliferation in PC9 but not in A549 cells. Using detailed kinetic modeling, we showed that the preferred use of glucose as a carbon source for purine ring synthesis in NSCLC tissues involves cytoplasmic activation/compartmentation of the glucose-to-serine pathway and enhanced reversed one-carbon fluxes that attenuate exogenous serine incorporation into purines. Our findings also indicate that the substrate for de novo nucleotide synthesis differs profoundly between cancer cell lines and fresh human lung cancer tissues; the latter preferred glucose to exogenous serine or glycine but not the former. This distinction in substrate utilization in purine synthesis in human cancer tissues should be considered when targeting one-carbon metabolism for cancer therapy.

Applications of Chromatography-Ultra High-Resolution MS for Stable Isotope-Resolved Metabolomics (SIRM) Reconstruction of Metabolic Networks.

Metabolism is a complex network of compartmentalized and coupled chemical reactions, which often involve transfers of substructures of biomolecules, thus requiring metabolite substructures to be tracked. Stable isotope resolved metabolomics (SIRM) enables pathways reconstruction, even among chemically identical metabolites, by tracking the provenance of stable isotope-labeled substructures using NMR and ultrahigh resolution (UHR) MS. The latter can resolve and count isotopic labels in metabolites and can identify isotopic enrichment in substructures when operated in tandem MS mode. However, MS2 is difficult to implement with chromatography-based UHR-MS due to lengthy MS1 acquisition time that is required to obtain the molecular isotopologue count, which is further exacerbated by the numerous isotopologue source ions to fragment. We review here recent developments in tandem MS applications of SIRM to obtain more detailed information about isotopologue distributions in metabolites and their substructures.

Inhibition of Anaplerotic Glutaminolysis Underlies Selenite Toxicity in Human Lung Cancer.

Large clinical trials and model systems studies suggest that the chemical form of selenium dictates chemopreventive and chemotherapeutic efficacy. Selenite induces excess ROS production, which mediates autophagy and eventual cell death in non-small cell lung cancer adenocarcinoma A549 cells. As the mechanisms underlying these phenotypic effects are unclear, the clinical relevance of selenite for cancer therapy remains to be determined. The authors' previous stable isotope-resolved metabolomics and gene expression analysis showed that selenite disrupts glycolysis, the Krebs cycle, and polyamine metabolism in A549 cells, potentially through perturbed glutaminolysis, a vital anaplerotic process for proliferation of many cancer cells. Herein, the role of the glutaminolytic enzyme glutaminase 1 (GLS1) in selenite's toxicity in A549 cells and in patient-derived lung cancer tissues is investigated. Using [13 C6 ]-glucose and [13 C5 ,15 N2 ]-glutamine tracers, selenite's action on metabolic networks is determined. Selenite inhibits glutaminolysis and glutathione synthesis by suppressing GLS1 expression, and blocks the Krebs cycle, but transiently activates pyruvate carboxylase activity. Glutamate supplementation partially rescues these anti-proliferative and oxidative stress activities. Similar metabolic perturbations and necrosis are observed in selenite-treated human patients' cancerous lung tissues ex vivo. The results support the hypothesis that GLS1 suppression mediates part of the anti-cancer activity of selenite both in vitro and ex vivo.

SDA: a semi-parametric differential abundance analysis method for metabolomics and proteomics data.

BACKGROUND: Identifying differentially abundant features between different experimental groups is a common goal for many metabolomics and proteomics studies. However, analyzing data from mass spectrometry (MS) is difficult because the data may not be normally distributed and there is often a large fraction of zero values. Although several statistical methods have been proposed, they either require the data normality assumption or are inefficient. RESULTS: We propose a new semi-parametric differential abundance analysis (SDA) method for metabolomics and proteomics data from MS. The method considers a two-part model, a logistic regression for the zero proportion and a semi-parametric log-linear model for the possibly non-normally distributed non-zero values, to characterize data from each feature. A kernel-smoothed likelihood method is developed to estimate model coefficients and a likelihood ratio test is constructed for differential abundant analysis. The method has been implemented into an R package, SDAMS, which is available at https://www.bioconductor.org/packages/release/bioc/html/SDAMS.html . CONCLUSION: By introducing the two-part semi-parametric model, SDA is able to handle both non-normally distributed data and large fraction of zero values in a MS dataset. It also allows for adjustment of covariates. Simulations and real data analyses demonstrate that SDA outperforms existing methods.

Acute loss of iron-sulfur clusters results in metabolic reprogramming and generation of lipid droplets in mammalian cells.

Iron-sulfur (Fe-S) clusters are ancient cofactors in cells and participate in diverse biochemical functions, including electron transfer and enzymatic catalysis. Although cell lines derived from individuals carrying mutations in the Fe-S cluster biogenesis pathway or siRNA-mediated knockdown of the Fe-S assembly components provide excellent models for investigating Fe-S cluster formation in mammalian cells, these experimental strategies focus on the consequences of prolonged impairment of Fe-S assembly. Here, we constructed and expressed dominant-negative variants of the primary Fe-S biogenesis scaffold protein iron-sulfur cluster assembly enzyme 2 (ISCU2) in human HEK293 cells. This approach enabled us to study the early metabolic reprogramming associated with loss of Fe-S-containing proteins in several major cellular compartments. Using multiple metabolomics platforms, we observed a ∼12-fold increase in intracellular citrate content in Fe-S-deficient cells, a surge that was due to loss of aconitase activity. The excess citrate was generated from glucose-derived acetyl-CoA, and global analysis of cellular lipids revealed that fatty acid biosynthesis increased markedly relative to cellular proliferation rates in Fe-S-deficient cells. We also observed intracellular lipid droplet accumulation in both acutely Fe-S-deficient cells and iron-starved cells. We conclude that deficient Fe-S biogenesis and acute iron deficiency rapidly increase cellular citrate concentrations, leading to fatty acid synthesis and cytosolic lipid droplet formation. Our findings uncover a potential cause of cellular steatosis in nonadipose tissues.

Metabolic reprogramming and Notch activity distinguish between non-small cell lung cancer subtypes.

BACKGROUND: Previous studies suggested that the metabolism is differently reprogrammed in the major subtypes of non-small cell lung cancer (NSCLC), squamous cell carcinomas (SCC) and adenocarcinomas (AdC). However, a comprehensive analysis of this differential metabolic reprogramming is lacking. METHODS: Publicly available gene expression data from human lung cancer samples and cell lines were analysed. Stable isotope resolved metabolomics were performed on SCC and ADC tumours in human patients and in freshly resected tumour slices. RESULTS: Analysis of multiple transcriptomics data from human samples identified a SCC-distinguishing enzyme gene signature. SCC tumours from patients infused with [U-13C]-glucose and SCC tissue slices incubated with stable isotope tracers demonstrated differential glucose and glutamine catabolism compared to AdCs or non-cancerous lung, confirming increased activity through pathways defined by the SCC metabolic gene signature. Furthermore, the upregulation of Notch target genes was a distinguishing feature of SCCs, which correlated with the metabolic signature. Notch and MYC-driven murine lung tumours recapitulated the SCC-distinguishing metabolic reprogramming. However, the differences between SCCs and AdCs disappear in established cell lines in 2D culture. CONCLUSIONS: Our data emphasise the importance of studying lung cancer metabolism in vivo. They also highlight potential targets for therapeutic intervention in SCC patients including differentially expressed enzymes that catalyse reactions in glycolysis, glutamine catabolism, serine, nucleotide and glutathione biosynthesis.