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Proliferating cells have increased mitochondrial activity relative to quiescent cells. Metabolic pathways in mitochondria are saturated in proliferating cells exhibiting the Warburg effect. Mitochondrial activity is constrained by the flux of NAD+ turnover in proliferating cells. The Warburg effect occurs in proliferating cells because glycolysis outpaces the maximum rate of glucose oxidation. A long-standing question in cancer biology has been why oxygenated tumors ferment the majority of glucose they consume to lactate rather than oxidizing it in their mitochondria, a phenomenon known as the 'Warburg effect.' An abundance of evidence shows not only that most cancer cells have fully functional mitochondria but also that mitochondrial activity is important to proliferation. It is therefore difficult to rationalize the metabolic benefit of cancer cells switching from respiration to fermentation. An emerging perspective is that rather than mitochondrial metabolism being suppressed in tumors, as is often suggested, mitochondrial activity increases to the level of saturation. As such, the Warburg effect becomes a signature of excess glucose being released as lactate due to mitochondrial overload. [ABSTRACT FROM AUTHOR]

In November 2018, the American Society for Mass Spectrometry hosted the Annual Fall Workshop on informatic methods in metabolomics. The Workshop included sixteen lectures presented by twelve invited speakers. The focus of the talks was untargeted metabolomics performed with liquid chromatography/mass spectrometry. In this review, we highlight five recurring topics that were covered by multiple presenters: (i) data sharing, (ii) artifacts and contaminants, (iii) feature degeneracy, (iv) database organization, and (v) requirements for metabolite identification. Our objective here is to present viewpoints that were widely shared among participants, as well as those in which varying opinions were articulated. We note that most of the presenting speakers employed different data processing software, which underscores the diversity of informatic programs currently being used in metabolomics. We conclude with our thoughts on the potential role of reference datasets as a step towards standardizing data processing methods in metabolomics. [ABSTRACT FROM AUTHOR]

The authors offer insights on a study published within the issue and conducted by J. Koone and colleagues, which focused on the three-dimensional (3D) printing of lithophane data in chemical sciences. They explored the need to improve current assistive technologies in the field of chemical sciences, the perceived benefits of lithophanes to people with blindness that are interested in learning chemistry, and the tactile accuracy demonstrated by blind participants in interpreting the lithophanes.

When using liquid chromatography/mass spectrometry (LC/MS) to perform untargeted metabolomics, it is now routine to detect tens of thousands of features from biological samples. Poor understanding of the data, however, has complicated interpretation and masked the number of unique metabolites actually being measured in an experiment. Here we place an upper bound on the number of unique metabolites detected in Escherichia coli samples analyzed with one untargeted metabolomics method. We first group multiple features arising from the same analyte, which we call "degenerate features", using a context-driven annotation approach. Surprisingly, this analysis revealed thousands of previously unreported degeneracies that reduced the number of unique analytes to ~2961. We then applied an orthogonal approach to remove nonbiological features from the data using the 13C-based credentialing technology. This further reduced the number of unique analytes to less than 1000. Our 90% reduction in data is 5- fold greater than previously published studies. On the basis of the results, we propose an alternative approach to untargeted metabolomics that relies on thoroughly annotated reference data sets. To this end, we introduce the creDBle database (http:// creDBle.wustl.edu), which contains accurate mass, retention time, and MS/MS fragmentation data as well as annotations of all credentialed features. [ABSTRACT FROM AUTHOR]

Global metabolomics describes the comprehensive analysis of small molecules in a biological system without bias. With mass spectrometry-based methods, global metabolomic data sets typically comprise thousands of peaks, each of which is associated with a mass-to-charge ratio, retention time, fold change, p-value, and relative intensity. Although several visualization schemes have been used for metabolomic data, most commonly used representations exclude important data dimensions and therefore limit interpretation of global data sets. Given that metabolite identification through tandem mass spectrometry data acquisition is a time-limiting step of the untargeted metabolomic workflow, simultaneous visualization of these parameters from large sets of data could facilitate compound identification and data interpretation. Here, we present such a visualization scheme of global metabolomic data using a so-called "cloud plot" to represent multidimensional data from septic mice. While much attention has been dedicated to lipid compounds as potential biomarkers for sepsis, the cloud plot shows that alterations in hydrophilic metabolites may provide an early signature of the disease prior to the onset of clinical symptoms. The cloud plot is an effective representation of global mass spectrometry-based metabolomic data, and we describe how to extract it as standard output from our XCMS metabolomic software. [ABSTRACT FROM AUTHOR]

Patients with cirrhosis exhibit features of circadian disruption. Hyperammonaemia has been suggested to impair both homeostatic and circadian sleep regulation. Here, we tested if hyperammonaemia directly disrupts circadian rhythm generation in the central pacemaker, the suprachiasmatic nuclei (SCN) of the hypothalamus. Wheel‐running activity was recorded from mice fed with a hyperammonaemic or normal diet for ~35 days in a 12:12 light–dark (LD) cycle followed by ~15 days in constant darkness (DD). The expression of the clock protein PERIOD2 (PER2) was recorded from SCN explants before, during and after ammonia exposure, ±glutamate receptor antagonists. In LD, hyperammonaemic mice advanced their daily activity onset time by ~1 h (16.8 ± 0.3 vs. 18.1 ± 0.04 h, p =.009) and decreased their total activity, concentrating it during the first half of the night. In DD, hyperammonaemia reduced the amplitude of daily activity (551.5 ± 27.7 vs. 724.9 ± 59 counts, p =.007), with no changes in circadian period. Ammonia (≥0.01 mM) rapidly and significantly reduced PER2 amplitude, and slightly increased circadian period. The decrease in PER2 amplitude correlated with decreased synchrony among circadian cells in the SCN and increased extracellular glutamate, which was rescued by AMPA glutamate receptor antagonists. These data suggest that hyperammonaemia affects circadian regulation of rest‐activity behaviour by increasing extracellular glutamate in the SCN. [ABSTRACT FROM AUTHOR]

Recently, interest in untargeted metabolomics has become prevalent in the general scientific community among an increasing number of investigators. The majority of these investigators, however, do not have the bioinformatic expertise that has been required to process metabolornic data by using command-line driven software programs. Here we introduce a novel platform to process untargeted metabolomic data that uses an intuitive graphical interface and does not require installation or technical expertise. This platform, called XCMS Online, is a web-based version of the widely used XCMS software that allows users to easily upload and process liquid chromatography/mass spectrometry data with only a few mouse clicks. XCMS Online provides a solution for the complete untargeted metabolornic workilow including feature detection, retention time correction, alignment, annotation, statistical analysis, and data visualization. Results can be browsed online in an interactive, customizable table showing statistics, chromatograms, and putative METLIN identities for each metabolite. Additionally, all results and images can be downloaded as zip files for ofiline analysis and publication. XCMS Online is available at https://xcmsonlinescripps.edu. [ABSTRACT FROM AUTHOR]

Metabolites, the chemical entities that are transformed during metabolism, provide a functional readout of cellular biochemistry. With emerging technologies in mass spectrometry, thousands of metabolites can now be quantitatively measured from minimal amounts of biological material, which has thereby enabled systems-level analyses. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanism are being revealed and are shaping our understanding of cell biology, physiology and medicine. [ABSTRACT FROM AUTHOR]

Global metabolomics has emerged as a powerful tool to interrogate cellular biochemistry at the systems level by tracking alterations in the levels of small molecules. One approach to define cellular dynamics with respect to this dysregulation of small molecules has been to consider metabolic flux as a function of time. While flux measurements have proven effective for model organisms, acquiring multiple time points at appropriate temporal intervals for many sample types (e.g., clinical specimens) is challenging. As an alternative, meta-analysis provides another strategy for delineating metabolic cause and effect perturbations. That is, the combination of untargeted metabolomic data from multiple pairwise comparisons enables the association of specific changes in small molecules with unique phenotypic alterations. We recently developed metabolomic software called metaXCMS to automate these types of higher order comparisons. Here we discuss the potential of metaXCMS for analyzing proteomic datasets and highlight the biological value of combining meta-results from both metabolomic and proteomic analyses. The combined meta-analysis has the potential to facilitate efforts in functional genomics and the identification of metabolic disruptions related to disease pathogenesis. [ABSTRACT FROM AUTHOR]

Mass spectrometry-based untargeted metabolomics often results in the observation of hundreds to thousands of features that are differentially regulated between sample classes. A major challenge in interpreting the data is distinguishing metabolites that are causally associated with the phenotype of interest from those that are unrelated hut altered in downstream pathways as an effect. To facilitate this distinction, here we describe new software called metaXCMS for performing second-order ("meta") analysis of untargeted metabolomics data from multiple sample groups representing different models of the same phenotype. While the original version of XCMS was designed for the direct comparison of two sample groups, metaXCMS enables meta-analysis of an unlimited number of sample dasses to facilitate prioritization of the data and increase the probability of identifying metabolites causally related to the phenotype of interest. metaXCMS is used to import XCMS results that are subsequently filtered, realigned, and ultimately compared to identify shared metabolites that are upor down-regulated across all sample groups. We demonstrate the software's utility by identifying histamine as a metabolite that is commonly altered in three different models of pain. metaXCMS is freely available at http://metlin.scripps.edu/ metaxcmS/. [ABSTRACT FROM AUTHOR]

Nanostructure-Initiator Mass Spectrometry (NIMS) is a matrix-free desorption/ionization approach that is par- ticularly well-suited for unbiased (untargeted) metabolo- mics. An overview of the NIMS technoloejr and its appli- cation in the detection of biofluid and tissue metabolites are presented. [ABSTRACT FROM AUTHOR]

Abstract: The increasing frequency of Enterococcus faecium isolates with multidrug resistance is a serious clinical problem given the severely limited number of therapeutic options available to treat these infections. Oritavancin is a promising new alternative in clinical development that has potent antimicrobial activity against both staphylococcal and enterococcal vancomycin-resistant pathogens. Using solid-state NMR to detect changes in the cell-wall structure and peptidoglycan precursors of whole cells after antibiotic-induced stress, we report that vancomycin and oritavancin have different modes of action in E. faecium. Our results show the accumulation of peptidoglycan precursors after vancomycin treatment, consistent with transglycosylase inhibition, but no measurable difference in cross-linking. In contrast, after oritavancin exposure, we did not observe the accumulation of peptidoglycan precursors. Instead, the number of cross-links is significantly reduced, showing that oritavancin primarily inhibits transpeptidation. We propose that the activity of oritavancin is the result of a secondary binding interaction with the E. faecium peptidoglycan. The hypothesis is supported by results from 13C{19F} rotational-echo double-resonance (REDOR) experiments on whole cells enriched with l-[1-13C]lysine and complexed with desleucyl [19F]oritavancin. These experiments establish that an oritavancin derivative with a damaged d-Ala–d-Ala binding pocket still binds to E. faecium peptidoglycan. The 13C{19F} REDOR dephasing maximum indicates that the secondary binding site of oritavancin is specific to nascent and template peptidoglycan. We conclude that the inhibition of transpeptidation by oritavancin in E. faecium is the result of the large number of secondary binding sites relative to the number of primary binding sites. [Copyright &y& Elsevier]

The molecular details of the biosynthesis and resulting architecture of the bacterial cell wall remain unclear but are essential to understanding the activity of glycopeptide antibiotics, the recognition of pathogens by hosts, and the processes of bacterial growth and division. Here we report a new strategy to elucidate bacterial cell-wall architecture based on time-dependent isotope labeling of bacterial cells quantified by liquid chromatography/accurate mass measurement mass spectrometry. The results allow us to track the fate of cell-wall precursors (which contain the vancomycin-binding site) in Enterococcus faecium, a leading antibiotic-resistant pathogen. By comparing isotopic enrichments of postinsertionally modified cell-wall precursors, we find that tripeptides and species without aspartic acid/asparagine (Asp/Asn, Asx) bridges are specific to mature cell wall. Additionally, we find that the sequence of cell-wall maturation varies throughout a cell cycle. We suggest that actively dividing E. faecium cells have three zones of unique peptidoglycan processing. Our results reveal new organizational characteristics of the bacterial cell wall that are important to understanding tertiary structure and designing novel drugs for antibiotic-resistant pathogens. [ABSTRACT FROM AUTHOR]

Enterococcus faecium, an opportunistic pathogen that causes a significant number of hospital-acquired infections each year, presents a serious clinical challenge because an increasing number of infections are resistant to the so-called antibiotic of last resort, vancomycin. Vancomycin and other new glycopeptide derivatives target the bacterial cell wall, thereby perturbing its biosynthesis. To help determine the modes of action of glycopeptide antibiotics, we have developed a bottom-up mass spectrometry approach complemented by solid-state nuclear magnetic resonance (NMR) to elucidate important structural characteristics of vancomycin-susceptible E. faecium peptidoglycan. Using accurate-mass measurements and integrating ion-current chromatographic peaks of digested peptidoglycan, we identified individual muropeptide species and approximated the relative amount of each. Even though the organism investigated is susceptible to vancomycin, only 3% of the digested peptidoglycan has the well-known d-Ala-d-Ala vancomycin-binding site. The data are consistent with a previously proposed template model of cell-wall biosynthesis where d-Ala-d-Ala stems that are not cross-linked are cleaved in mature peptidoglycan. Additionally, our mass-spectrometry approach allowed differentiation and quantification of muropeptide species seen as unresolved chromatographic peaks. Our method provides an estimate of the extent of muropeptides containing O-acetylation, amidation, hydroxylation, and the number of species forming cyclic imides. The varieties of muropeptides on which the modifications are detected suggest that significant processing occurs in mature peptidoglycan where several enzymes are active in editing cell-wall structure. [Copyright &y& Elsevier]

Vancomycin and other antibacterial glycopeptide analogues target the cell wall and affect the enzymatic processes involved with cell-wall biosynthesis. Understanding the structure and organization of the peptidoglycan is the first step in establishing the mode of action of these glycopeptides. We have used solid-state NMR to determine the relative concentrations of stem-links (64%), bridge-links (61%), and cross-links (49%) in the cell walls of vancomycin-susceptible Enterococcusfaecium (ATTC 49624). Furthermore, we have determined that in vivo only 7% of the peptidoglycan stems terminate in D-Ala- D-Ala, the well-known vancomycin-binding site. Presumably, D-Ala-D-Ala is cleaved from uncross-linked stems in mature peptidoglycan by an active carboxypeptidase. We believe that most of the few pentapeptide stems ending in D-Ala-D-Ala occur in the template and nascent peptidoglycan strands that are crucial for cell-wall biosynthesis. [ABSTRACT FROM AUTHOR]

Glucose has long been considered the primary fuel source for the brain. However, glucose levels fluctuate in the brain during sleep or circuit activity, posing major metabolic stress. Here, we demonstrate that the mammalian brain uses pyruvate as a fuel source, and pyruvate can support neuronal viability in the absence of glucose. Nerve terminals are sites of metabolic vulnerability, and we show that mitochondrial pyruvate uptake is a critical step in oxidative ATP production in hippocampal terminals. We find that the mitochondrial pyruvate carrier is post-translationally modified by lysine acetylation, which, in turn, modulates mitochondrial pyruvate uptake. Our data reveal that the mitochondrial pyruvate carrier regulates distinct steps in neurotransmission, namely, the spatiotemporal pattern of synaptic vesicle release and the efficiency of vesicle retrieval--functions that have profound implications for synaptic plasticity. In summary, we identify pyruvate as a potent neuronal fuel and mitochondrial pyruvate uptake as a critical node for the metabolic control of neurotransmission in hippocampal terminals. [ABSTRACT FROM AUTHOR]

Neuropathic pain is a debilitating condition for which the development of effective treatments has been limited by an incomplete understanding of its chemical basis. We show by using untargeted metabolomics that sphingomyelin-ceramide metabolism is altered in the dorsal horn of rats with neuropathic pain and that the upregulated, endogenous metabolite N,N-dimethylsphingosine induces mechanical hypersensitivity in vivo. These results demonstrate the utility of metabolomics to implicate unexplored biochemical pathways in disease. [ABSTRACT FROM AUTHOR]

Background: Syringomyelia (SM) is characterized by the development of fluid-filled cavities, referred to as syrinxes, within the spinal cord tissue. The molecular etiology of SM post-spinal cord injury (SCI) is not well understood and only invasive surgical based treatments are available to treat SM clinically. This study builds upon our previous omics studies and in vitro cellular investigations to further understand local fluid osmoregulation in post-traumatic SM (PTSM) to highlight important pathways for future molecular interventions. Methods: A rat PTSM model consisting of a laminectomy at the C7 to T1 level followed by a parenchymal injection of 2 μL quisqualic acid (QA) and an injection of 5 μL kaolin in the subarachnoid space was utilized 6 weeks after initial surgery, parenchymal fluid and cerebrospinal fluid (CSF) were collected, and the osmolality of fluids were analyzed. Immunohistochemistry (IHC), metabolomics analysis using LC–MS, and mass spectrometry-based imaging (MSI) were performed on injured and laminectomy-only control spinal cords. Results: We demonstrated that the osmolality of the local parenchymal fluid encompassing syrinxes was higher compared to control spinal cords after laminectomy, indicating a local osmotic imbalance due to SM injury. Moreover, we also found that parenchymal fluid is more hypertonic than CSF, indicating establishment of a local osmotic gradient in the PTSM injured spinal cord (syrinx site) forcing fluid into the spinal cord parenchyma to form and/or expand syrinxes. IHC results demonstrated upregulation of betaine, ions, water channels/transporters, and enzymes (BGT1, AQP1, AQP4, CHDH) at the syrinx site as compared to caudal and rostral sites to the injury, implying extensive local osmoregulation activities at the syrinx site. Further, metabolomics analysis corroborated alterations in osmolality at the syrinx site by upregulation of small molecule osmolytes including betaine, carnitine, glycerophosphocholine, arginine, creatine, guanidinoacetate, and spermidine. Conclusions: In summary, PTSM results in local osmotic disturbance that propagates at 6 weeks following initial injury. This coincides with and may contribute to syrinx formation/expansion. [ABSTRACT FROM AUTHOR]

Enzymatic reactions are crucial to explore the mechanistic function of metabolites and proteins in cellular processes and to understand the etiology of diseases. The increasing number of interconnected metabolic reactions allows the development of in silico deep learning-based methods to discover new enzymatic reaction links between metabolites and proteins to further expand the landscape of existing metabolite–protein interactome. Computational approaches to predict the enzymatic reaction link by metabolite–protein interaction (MPI) prediction are still very limited. In this study, we developed a Variational Graph Autoencoders (VGAE)-based framework to predict MPI in genome-scale heterogeneous enzymatic reaction networks across ten organisms. By incorporating molecular features of metabolites and proteins as well as neighboring information in the MPI networks, our MPI-VGAE predictor achieved the best predictive performance compared to other machine learning methods. Moreover, when applying the MPI-VGAE framework to reconstruct hundreds of metabolic pathways, functional enzymatic reaction networks and a metabolite–metabolite interaction network, our method showed the most robust performance among all scenarios. To the best of our knowledge, this is the first MPI predictor by VGAE for enzymatic reaction link prediction. Furthermore, we implemented the MPI-VGAE framework to reconstruct the disease-specific MPI network based on the disrupted metabolites and proteins in Alzheimer's disease and colorectal cancer, respectively. A substantial number of novel enzymatic reaction links were identified. We further validated and explored the interactions of these enzymatic reactions using molecular docking. These results highlight the potential of the MPI-VGAE framework for the discovery of novel disease-related enzymatic reactions and facilitate the study of the disrupted metabolisms in diseases. [ABSTRACT FROM AUTHOR]

Global profiling of metabolites in biological samples by liquid chromatography/mass spectrometry results in datasets too large to evaluate manually. Fortunately, a variety of software programs are now available to automate the data analysis. Selection of the appropriate processing solution is dependent upon experimental design. Most metabolomic studies a decade ago had a relatively simple experimental design in which the intensities of compounds were compared between only two sample groups. More recently, however, increasingly sophisticated applications have been pursued. Examples include comparing compound intensities between multiple sample groups and unbiasedly tracking the fate of specific isotopic labels. The latter types of applications have necessitated the development of new software programs, which have introduced additional functionalities that facilitate data analysis. The objective of this review is to provide an overview of the freely available bioinformatic solutions that are either based upon or are compatible with the algorithms in XCMS, which we broadly refer to here as the ‘XCMS family’ of software. These include CAMERA, credentialing, Warpgroup, metaXCMS, X 13 CMS, and XCMS Online. Together, these informatic technologies can accommodate most cutting-edge metabolomic applications and offer unique advantages when compared to the original XCMS program. [ABSTRACT FROM AUTHOR]

Motivation: Current informatic techniques for processing raw chromatography/mass spectrometry data break down under several common, non-ideal conditions. Importantly, hydrophilic liquid interaction chromatography (a key separation technology for metabolomics) produces data which are especially challenging to process. We identify three critical points of failure in current informatic workflows: compound specific drift, integration region variance, and naive missing value imputation. We implement the Warpgroup algorithm to address these challenges. Results: Warpgroup adds peak subregion detection, consensus integration bound detection, and intelligent missing value imputation steps to the conventional informatic workflow. When compared with the conventional workflow, Warpgroup made major improvements to the processed data. The coefficient of variation for peaks detected in replicate injections of a complex Escherichia Coli extract were halved (a reduction of 19%). Integration regions across samples were much more robust. Additionally, many signals lost by the conventional workflow were 'rescued' by the Warpgroup refinement, thereby resulting in greater analyte coverage in the processed data. [ABSTRACT FROM AUTHOR]

Motivation: The goal of large-scale metabolite profiling is to compare the relative concentrations of as many metabolites extracted frombiological samples as possible. This is typically accomplished by measuring the abundances of thousands of ions with high-resolution and high mass accuracy mass spectrometers. Although the data from these instruments provide a comprehensive fingerprint of each sample, identifying the structures of the thousands of detected ions is still challenging and time intensive. An alternative, less-comprehensive approach is to use triple quadrupole (QqQ) mass spectrometry to analyze predetermined sets of metabolites (typically fewer than several hundred). This is done using authentic standards to develop QqQ experiments that specifically detect only the targeted metabolites, with the advantage that the need for ion identification after profiling is eliminated. Results: Here, we propose a framework to extend the application of QqQ mass spectrometers to large-scale metabolite profiling. We aim to provide a foundation for designing QqQ multiple reaction monitoring (MRM) experiments for each of the 82 696 metabolites in the METLIN metabolite database. First, we identify common fragmentation products from the experimental fragmentation data in METLIN. Then, we model the likelihoods of each precursor structure in METLIN producing each common fragmentation product. With these likelihood estimates, we select ensembles of common fragmentation products that minimize our uncertainty about metabolite identities. We demonstrate encouraging performance and, based on our results, we suggest how our method can be integrated with future work to develop large-scale MRM experiments. [ABSTRACT FROM AUTHOR]

Renewed interest in metabolic research over the last two decades has inspired an explosion of technological developments for studying metabolism. At the forefront of methodological innovation is an approach referred to as “untargeted” or “discovery” metabolomics. The experimental objective of this technique is to comprehensively measure the entire metabolome, which constitutes a largely undefined set of molecules. Given its potential comprehensive coverage, untargeted metabolomics is often the first choice of experiments for investigators pursuing a metabolic research question. It is important to recognize, however, that untargeted metabolomics may not always be the optimal experimental approach. Conventionally, untargeted metabolomics only provides information about relative differences in metabolite pool sizes. Therefore, depending on the specific scientific question at hand, a complementary approach involving stable isotopes (such as metabolic flux analysis) may be better suited to provide biological insights. Unlike untargeted metabolomics, stable-isotope methods can provide information about differences in reaction rates. [ABSTRACT FROM AUTHOR]

Mass spectrometry-based metabolomics is the comprehensive study of naturally occurring small molecules collectively known as the metabolome. Given the vast structural diversity and chemical properties of endogenous metabolites, biological extraction and chromatography methods bias the number, property, and concentration of metabolites detected by mass spectrometry and creates a challenge for global untargeted studies. In this work, we used Escherichia coli bacterial cells to explore the influence of solvent polarity, temperature, and pH in extracting polar and nonpolar metabolites simultaneously. In addition, we explored chromatographic conditions involving different stationary and mobile phases that optimize the separation and ionization of endogenous metabolite extracts as well as a mixture of synthetic standards. Our results reveal that hot polar solvents are the most efficient in extracting both hydrophilic and hydrophobic metabolites simultaneously. In addition, ammonium fluoride in the mobile phase substantially improved ionization efficiency in negative electrospray ionization mode by an average increase in signal intensity of 5.7 and over a 2-fold increase in the total number of features detected. The improvement in sensitivity with ammonium fluoride resulted in 3.5 times as many metabolite hits in databases compared to ammonium acetate or formic acid enriched mobile phases and allowed for the identification of unique metabolites involved in fundamental cellular pathways. [ABSTRACT FROM AUTHOR]

Analytical and biological variability are issues of central importance to human metabolomics studies. Here both types of variation are examined in human plasma and cerebrospinal fluid (CSF) using a global liquid chromatography/mass spectrometiy (LC/MS) metabolomics strategy. The platform shows small analytical variation with a median coefficient of variation (CV) of 15—16% for both plasma and CSF sample matrixes when the integrated area of each peak in the mass spectra is considered. Analysis of biological variation shows that human CSF has a median CV of 35% and plasma has a median CV of 46%. To understand the difference in CV between the biofluids, we compared plasma and CSF independently obtained from different healthy humans. Additionally, we analyzed another group of patients from whom we compared matched (SF and plasma (plasma and (SF obtained from the same human subject). A similar number of features was observed in both biofluids, although the majority of features appeared with greater intensity in plasma. More than a dozen metabolites shared between the human (SF and plasma metabolomes were identified based on accurate mass measurements, retention times, and MS/MS spectra. The fold change in these metabolites was consistent with the median biological CV determined for all peaks. The measured median biological CV together with analysis of intragroup variation of healthy individuals suggests that fold changes above 2 in metabolomics studies investigating plasma or (SF are statistically relevant with respect to the inherent variability of a healthy control group. These data demonstrate the reproducibility of the global metabolomics platform using LC/MS and reveal the robustness of the approach for biomarker discovery. [ABSTRACT FROM AUTHOR]

Des-N-methylleucyl-4-(4-fluorophenyl)benzyl-vancomycifl (DFPBV) retains activity against vancomycin-resistant pathogens despite its damaged D-Ala-D-Ala binding cleft. Using solid-state nuclear magnetic resonance (NMR), a DFPBV binding site in the cell walls of whole cells of Staphylococcus aureus has been identified. The cell walls were labeled with D-[1-13C]alanine, [1-13C]glycine, and L-[ϵ-15N]lysine. Internuclear distances from 19F of the DFPBV to the 15C and 15N labels of the cell-wall peptidoglycan were determined by rotational-echo double-resonance (REDOR) NMR. The 15C{19F} and 15N{19F} REDOR spectra show that, in situ, DFPBV binds to the peptidoglycan as a monomer with its vancosamine hydrophobic side chain positioned near a pentaglycyl bridge. This result suggests that the antimicrobial activity of other vancosamine-modified glycopeptides depends upon both D-Ala-D-Ala stem- terminus recognition (primary binding site) and stem-bridge recognition (secondary binding site). [ABSTRACT FROM AUTHOR]

The mitochondrial pyruvate carrier (MPC) is an inner mitochondrial membrane complex that plays a critical role in intermediary metabolism. Inhibition of the MPC, especially in liver, may have efficacy for treating type 2 diabetes mellitus. Herein, we examined the antidiabetic effects of zaprinast and 7ACC2, small molecules which have been reported to act as MPC inhibitors. Both compounds activated a bioluminescence resonance energy transfer-based MPC reporter assay (reporter sensitive to pyruvate) and potently inhibited pyruvate-mediated respiration in isolated mitochondria. Furthermore, zaprinast and 7ACC2 acutely improved glucose tolerance in diet-induced obese mice in vivo. Although some findings were suggestive of improved insulin sensitivity, hyperinsulinemic-euglycemic clamp studies did not detect enhanced insulin action in response to 7ACC2 treatment. Rather, our data suggest acute glucose-lowering effects of MPC inhibition may be due to suppressed hepatic gluconeogenesis. Finally, we used reporter sensitive to pyruvate to screen a chemical library of drugs and identified 35 potentially novel MPC modulators. Using available evidence, we generated a pharmacophore model to prioritize which hits to pursue. Our analysis revealed carsalam and six quinolone antibiotics, as well as 7ACC1, share a common pharmacophore with 7ACC2. We validated that these compounds are novel inhibitors of the MPC and suppress hepatocyte glucose production and demonstrated that one quinolone (nalidixic acid) improved glucose tolerance in obese mice. In conclusion, these data demonstrate the feasibility of therapeutic targeting of the MPC for treating diabetes and provide scaffolds that can be used to develop potent and novel classes of MPC inhibitors. [ABSTRACT FROM AUTHOR]

Natural killer (NK) cells are essential for early protection against virus infection and must metabolically adapt to the energy demands of activation. Here, we found upregulation of the metabolic adaptor hypoxia-inducible factor-1α (HIF1α) is a feature of mouse NK cells during murine cytomegalovirus (MCMV) infection in vivo. HIF1α-deficient NK cells failed to control viral load, causing increased morbidity. No defects were found in effector functions of HIF1αKO NK cells; however, their numbers were significantly reduced. Loss of HIF1α did not affect NK cell proliferation during in vivo infection and in vitro cytokine stimulation. Instead, we found that HIF1α-deficient NK cells showed increased expression of the pro-apoptotic protein Bim and glucose metabolism was impaired during cytokine stimulation in vitro. Similarly, during MCMV infection HIF1α-deficient NK cells upregulated Bim and had increased caspase activity. Thus, NK cells require HIF1α-dependent metabolic functions to repress Bim expression and sustain cell numbers for an optimal virus response. [ABSTRACT FROM AUTHOR]

In rodents, obesity and aging impair nicotinamide adenine dinucleotide (NAD+) biosynthesis, which contributes to metabolic dysfunction. Nicotinamide mononucleotide (NMN) availability is a rate-limiting factor in mammalian NAD+ biosynthesis. We conducted a 10-week, randomized, placebo-controlled, double-blind trial to evaluate the effect of NMN supplementation on metabolic function in postmenopausal women with prediabetes who were overweight or obese. Insulin-stimulated glucose disposal, assessed by using the hyperinsulinemic-euglycemic clamp, and skeletal muscle insulin signaling [phosphorylation of protein kinase AKT and mechanistic target of rapamycin (mTOR)] increased after NMN supplementation but did not change after placebo treatment. NMN supplementation up-regulated the expression of platelet-derived growth factor receptor b and other genes related to muscle remodeling. These results demonstrate that NMN increases muscle insulin sensitivity, insulin signaling, and remodeling in women with prediabetes who are overweight or obese (clinicaltrial.gov NCT 03151239). [ABSTRACT FROM AUTHOR]

Tumor cells have devised unique metabolic strategies to garner enough nutrients to sustain continuous growth and cell division. Oncogenic mutations may alter metabolic pathways to unlock new sources of energy, and cells take the advantage of various scavenging pathways to ingest material from their environment. These changes in metabolism result in a metabolic profile that, in addition to providing the building blocks for macromolecules, can also influence cell signaling pathways to promote tumor initiation and progression. Understanding what pathways tumor cells use to synthesize the materials necessary to support metabolic growth can pave the way for new cancer therapeutics. Potential strategies include depriving tumors of the materials needed to grow or targeting pathways involved in dependencies that arise by virtue of their altered metabolis. [ABSTRACT FROM AUTHOR]

Proliferating cells often have increased glucose consumption and lactate excretion relative to the same cells in the quiescent state, a phenomenon known as the Warburg effect. Despite an increase in glycolysis, however, here we show that non-transformed mouse fibroblasts also increase oxidative phosphorylation (OXPHOS) by nearly two-fold and mitochondrial coupling efficiency by ~30% during proliferation. Both increases are supported by mitochondrial fusion. Impairing mitochondrial fusion by knocking down mitofusion-2 (Mfn2) was sufficient to attenuate proliferation, while overexpressing Mfn2 increased proliferation. Interestingly, impairing mitochondrial fusion decreased OXPHOS but did not deplete ATP levels. Instead, inhibition caused cells to transition from excreting aspartate to consuming it. Transforming fibroblasts with the Ras oncogene induced mitochondrial biogenesis, which further elevated OXPHOS. Notably, transformed fibroblasts continued to have elongated mitochondria and their proliferation remained sensitive to inhibition of Mfn2. Our results suggest that cell proliferation requires increased OXPHOS as supported by mitochondrial fusion. [ABSTRACT FROM AUTHOR]

It has been suggested that some cancer cells rely upon fatty acid oxidation (FAO) for energy. Here we show that when FAO was reduced approximately 90% by pharmacological inhibition of carnitine palmitoyltransferase I (CPT1) with low concentrations of etomoxir, the proliferation rate of various cancer cells was unaffected. Efforts to pharmacologically inhibit FAO more than 90% revealed that high concentrations of etomoxir (200 μM) have an off-target effect of inhibiting complex I of the electron transport chain. Surprisingly, however, when FAO was reduced further by genetic knockdown of CPT1, the proliferation rate of these same cells decreased nearly 2-fold and could not be restored by acetate or octanoic acid supplementation. Moreover, CPT1 knockdowns had altered mitochondrial morphology and impaired mitochondrial coupling, whereas cells in which CPT1 had been approximately 90% inhibited by etomoxir did not. Lipidomic profiling of mitochondria isolated from CPT1 knockdowns showed depleted concentrations of complex structural and signaling lipids. Additionally, expression of a catalytically dead CPT1 in CPT1 knockdowns did not restore mitochondrial coupling. Taken together, these results suggest that transport of at least some long-chain fatty acids into the mitochondria by CPT1 may be required for anabolic processes that support healthy mitochondrial function and cancer cell proliferation independent of FAO. [ABSTRACT FROM AUTHOR]

Although it is common in untargeted metabolomics to apply reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) methods that have been systematically optimized for lipids and central carbon metabolites, here we show that these established protocols provide poor coverage of semipolar metabolites because of inadequate retention. Our objective was to develop an RPLC approach that improved detection of these metabolites without sacrificing lipid coverage. We initially evaluated columns recently released by Waters under the CORTECS line by analyzing 47 small-molecule standards that evenly span the nonpolar and semipolar ranges. An RPLC method commonly used in untargeted metabolomics was considered a benchmarking reference. We found that highly nonpolar and semipolar metabolites cannot be reliably profiled with any single method because of retention and solubility limitations of the injection solvent. Instead, we optimized a multiplexed approach using the CORTECS T3 column to analyze semipolar compounds and the CORTECS C column to analyze lipids. Strikingly, we determined that combining these methods allowed detection of 41 of the total 47 standards, whereas our reference RPLC method detected only 10 of the 47 standards. We then applied credentialing to compare method performance at the comprehensive scale. The tandem method showed more than a fivefold increase in credentialing coverage relative to our RPLC benchmark. Our results demonstrate that comprehensive coverage of metabolites amenable to reversed-phase separation necessitates two reconstitution solvents and chromatographic methods. Thus, we suggest complementing HILIC methods with a dual T3 and C RPLC approach to increase coverage of semipolar metabolites and lipids for untargeted metabolomics. [ABSTRACT FROM AUTHOR]

BACKGROUND: The term "exposome" was coined in 2005 to underscore the importance of the environment to human health and to bring research efforts in line with those on the human genome. The ability to characterize environmental exposures through biomonitoring is key to exposome research efforts. OBJECTIVES: Our objectives were to describe why traditional and nontraditional (exposomic) biomonitoring are both critical in studies aiming to capture the exposome and to make recommendations on how to transition exposure research toward exposomic approaches. We describe the biomonitoring needs of exposome research and approaches and recommendations that will help fill the gaps in the current science. Discussion: Traditional and exposomic biomonitoring approaches have key advantages and disadvantages for assessing exposure. Exposomic approaches differ from traditional biomonitoring methods in that they can include all exposures of potential health significance, whether from endogenous or exogenous sources. Issues of sample availability and quality, identification of unknown analytes, capture of nonpersistent chemicals, integration of methods, and statistical assessment of increasingly complex data sets remain challenges that must continue to be addressed. CONCLUSIONS: To understand the complexity of exposures faced throughout the lifespan, both traditional and nontraditional biomonitoring methods should be used. Through hybrid approaches and the integration of emerging techniques, biomonitoring strategies can be maximized in research to define the exposome. [ABSTRACT FROM AUTHOR]

Analysis of a single analyte by mass spectrometry can result in the detection of more than 100 degenerate peaks. These degenerate peaks complicate spectral interpretation and are challenging to annotate. In mass spectrometry-based metabolomics, this degeneracy leads to inflated false discovery rates, data sets containing an order of magnitude more features than analytes, and an inefficient use of resources during data analysis. Although software has been introduced to annotate spectral degeneracy, current approaches are unable to represent several important classes of peak relationships. These include heterodimers and higher complex adducts, distal fragments, relationships between peaks in different polarities, and complex adducts between features and background peaks. Here we outline sources of peak degeneracy in mass spectra that are not annotated by current approaches and introduce a software package called mz.unity to detect these relationships in accurate mass data. Using mz.unity, we find that data sets contain many more complex relationships than we anticipated. Examples include the adduct of glutamate and nicotinamide adenine dinucleotide (NAD), fragments of NAD detected in the same or opposite polarities, and the adduct of glutamate and a background peak. Further, the complex relationships we identify show that several assumptions commonly made when interpreting mass spectral degeneracy do not hold in general. These contributions provide new tools and insight to aid in the annotation of complex spectral relationships and provide a foundation for improved data set identification. [ABSTRACT FROM AUTHOR]

Experiments in a variety of cell types, including hepatocytes, consistently demonstrate the acutely lipotoxic effects of saturated fatty acids, such as palmitate (PA), but not unsaturated fatty acids, such as oleate (OA). PA + OA co-treatment fully prevents PA lipotoxicity through mechanisms that are not well defined but which have been previously attributed to more efficient esterification and sequestration of PA into triglycerides (TGs) when OA is abundant. However, this hypothesis has never been directly tested by experimentally modulating the relative partitioning of PA/OA between TGs and other lipid fates in hepatocytes. In this study, we found that addition of OA to PA-treated hepatocytes enhanced TG synthesis, reduced total PA uptake and PA lipid incorporation, decreased phospholipid saturation and rescued PA-induced ER stress and lipoapoptosis. Knockdown of diacylglycerol acyltransferase (DGAT), the rate-limiting step in TG synthesis, significantly reduced TG accumulation without impairing OA-mediated rescue of PA lipotoxicity. In both wild-type and DGAT-knockdown hepatocytes, OA co-treatment significantly reduced PA lipid incorporation and overall phospholipid saturation compared to PA-treated hepatocytes. These data indicate that OA's protective effects do not require increased conversion of PA into inert TGs, but instead may be due to OA's ability to compete against PA for cellular uptake and/or esterification and, thereby, normalize the composition of cellular lipids in the presence of a toxic PA load. [ABSTRACT FROM AUTHOR]

Summary Durable antibody production after vaccination or infection is mediated by long-lived plasma cells (LLPCs). Pathways that specifically allow LLPCs to persist remain unknown. Through bioenergetic profiling, we found that human and mouse LLPCs could robustly engage pyruvate-dependent respiration, whereas their short-lived counterparts could not. LLPCs took up more glucose than did short-lived plasma cells (SLPCs) in vivo, and this glucose was essential for the generation of pyruvate. Glucose was primarily used to glycosylate antibodies, but glycolysis could be promoted by stimuli such as low ATP levels and the resultant pyruvate used for respiration by LLPCs. Deletion of Mpc2 , which encodes an essential component of the mitochondrial pyruvate carrier, led to a progressive loss of LLPCs and of vaccine-specific antibodies in vivo. Thus, glucose uptake and mitochondrial pyruvate import prevent bioenergetic crises and allow LLPCs to persist. Immunizations that maximize these plasma cell metabolic properties might thus provide enduring antibody-mediated immunity. [ABSTRACT FROM AUTHOR]

Summary Cellular proliferation requires the formation of new membranes. It is often assumed that the lipids needed for these membranes are synthesized mostly de novo . Here, we show that proliferating fibroblasts prefer to take up palmitate from the extracellular environment over synthesizing it de novo . Relative to quiescent fibroblasts, proliferating fibroblasts increase their uptake of palmitate, decrease fatty acid degradation, and instead direct more palmitate to membrane lipids. When exogenous palmitate is provided in the culture media at physiological concentrations, de novo synthesis accounts for only a minor fraction of intracellular palmitate in proliferating fibroblasts as well as proliferating HeLa and H460 cells. Blocking fatty acid uptake decreased the proliferation rate of fibroblasts, HeLa, and H460 cells, while supplementing media with exogenous palmitate resulted in decreased glucose uptake and rendered cells less sensitive to glycolytic inhibition. Our results suggest that cells scavenging exogenous lipids may be less susceptible to drugs targeting glycolysis and de novo lipid synthesis. [ABSTRACT FROM AUTHOR]

Motivation: Metabolite databases provide a unique window into metabolome research allowing the most commonly searched biomarkers to be catalogued. Omic scale metabolite profiling, or metabolomics, is finding increased utility in biomarker discovery largely driven by improvements in analytical technologies and the concurrent developments in bioinformatics. However, the successful translation of biomarkers into clinical or biologically relevant indicators is limited. Results: With the aim of improving the discovery of translatable metabolite biomarkers, we present search analytics for over one million METLIN metabolite database queries. The most common metabolites found in METLIN were cross-correlated against XCMS Online, the widely used cloud-based data processing and pathway analysis platform. Analysis of the METLIN and XCMS common metabolite data has two primary implications: these metabolites, might indicate a conserved metabolic response to stressors and, this data may be used to gauge the relative uniqueness of potential biomarkers. [ABSTRACT FROM AUTHOR]

Proliferating cells exhibit a metabolic phenotype known as "aerobic glycolysis," which is characterized by an elevated rate of glucose fermentation to lactate irrespective of oxygen availability. Although several theories have been proposed, a rationalization for why proliferating cells seemingly waste glucose carbon by excreting it as lactate remains elusive. Using the NCI-60 cell lines, we determined that lactate excretion is strongly correlated with the activity of mitochondrial NADH shuttles, but not proliferation. Quantifying the fluxes of the malate-aspartate shuttle (MAS), the glycerol 3-phosphate shuttle (G3PS), and lactate dehydrogenase under various conditions demonstrated that proliferating cells primarily transform glucose to lactate when glycolysis outpaces the mitochondrial NADH shuttles. Increasing mitochondrial NADH shuttle fluxes decreased glucose fermentation but did not reduce the proliferation rate. Our results reveal that glucose fermentation, a hallmark of cancer, is a secondary consequence of MAS and G3PS saturation rather than a unique metabolic driver of cellular proliferation. [Display omitted] • Fermentation correlates with NADH shuttle flux but not proliferation in cancer cells • The rate of glycolysis outpaces the flux of NADH shuttles in proliferating cells • Saturating NADH shuttle activity drives lactate production during aerobic glycolysis • The fraction of glucose-derived NADH oxidized in mitochondria is cell-type dependent Oxygenated cancer cells excrete much of the glucose they consume as lactate, a seemingly wasteful process that has been challenging to rationalize. Here, Wang et al. demonstrate that proliferating cells prefer to oxidize glucose. Lactate excretion primarily occurs because glycolysis produces NADH faster than shuttles can transport electrons into mitochondria. [ABSTRACT FROM AUTHOR]

Thermal processes are widely used in small molecule chemical analysis and metabolomics for derivatization, vaporization, chromatography, and ionization, especially in gas chromatography mass spectrometry (GC/MS). In this study the effect of heating was examined on a set of 64 small molecule standards and, separately, on human plasma metabolite extracts. The samples, either derivatized or underivatized, were heated at three different temperatures (60, 100, and 250 °C) at different exposure times (30 s, 60 s, and 300 s). All the samples were analyzed by liquid chromatography coupled to electrospray ionization mass spectrometry (LC/MS) and the data processed by XCMS Online (xcmsonline.scripps.edu). The results showed that heating at an elevated temperature of 100 °C had an appreciable effect on both the underivatized and derivatized molecules, and heating at 250 °C created substantial changes in the profile. For example, over 40% of the molecular peaks were altered in the plasma metabolite analysis after heating (250 °C, 300s) with a significant formation of degradation and transformation products. The analysis of 64 small molecule standards validated the temperature-induced changes observed on the plasma metabolites, where most of the small molecules degraded at elevated temperatures even after minimal exposure times (30 s). For example, tri- and diorganophosphates (e.g., adenosine triphosphate and adenosine diphosphate) were readily degraded into a mono-organophosphate (e.g., adenosine monophosphate) during heating. Nucleosides and nucleotides (e.g., inosine and inosine monophosphate) were also found to be transformed into purine derivatives (e.g., hypoxanthine). A newly formed transformation product, oleoyl ethyl amide, was identified in both the underivatized and derivatized forms of the plasma extracts and small molecule standard mixture, and was likely generated from oleic acid. Overall these analyses show that small molecules and metabolites undergo significant time-sensitive alterations when exposed to elevated temperatures, especially those conditions that mimic sample preparation and analysis in GC/MS experiments. [ABSTRACT FROM AUTHOR]

An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. As a result of this unique integration, we can analyze large profiling datasets and simultaneously obtain structural identifications. Validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometry data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level. [ABSTRACT FROM AUTHOR]

Nonalcoholic fatty liver disease (NAFLD) spectrum disorders affect approximately 1 billion individuals worldwide. However, the drivers of progressive steatohepatitis remain incompletely defined. Ketogenesis can dispose of much of the fat that enters the liver, and dysfunction in this pathway could promote the development of NAFLD. Here, we evaluated mice lacking mitochondrial 3-hydroxymethylgiutaryl CoA synthase (HMGCS2) to determine the role of ketogenesis in preventing diet-induced steatohepatitis. Antisense oligonucleotide-induced loss of HMGCS2 in chow-fed adult mice caused mild hyperglycemia, increased hepatic gluconeogenesis from pyruvate, and augmented production of hundreds of hepatic metabolites, a suite of which indicated activation of the de novo lipogenesis pathway. High-fat diet feeding of mice with insufficient ketogenesis resulted in extensive hepatocyte injury and inflammation, decreased glycemia, deranged hepatic TCA cycle intermediate concentrations, and impaired hepatic gluconeogenesis due to sequestration of free coenzyme A (CoASH). Supplementation of the CoASH precursors pantothenic acid and cysteine normalized TCA intermediates and gluconeogenesis in the livers of ketogenesis-insufficient animals. Together, these findings indicate that ketogenesis is a critical regulator of hepatic acyl-CoA metabolism, glucose metabolism, and TCA cycle function in the absorptive state and suggest that ketogenesis may modulate fatty liver disease. [ABSTRACT FROM AUTHOR]

The METLIN metabolite database has become one of the most widely used resources in metabolomics for making metabolite identifications. However, METLIN is not designed to identify metabolites that have been isotopically labeled. As a result, unbiasedly tracking the transformation of labeled metabolites with isotope-based metabolomics is challenge. Here, we introduce a new database, called isoMETLIN (http://isometlin.scripps.edu/), that has been developed specifically to identify metabolites incorporating isotopic labels. isoMETLIN enables users to search all computed isotopologues derived from METLIN on the basis of mass-to-charge values and specified isotopes of interest, such as 13C or 15N. Additionally, isoMETLIN contains experimental MS/MS data on hundreds of isotopomers. These data assist in localizing the position of isotopic labels within a metabolite. From these experimental MS/MS isotopomer spectra, precursor atoms can be mapped to fragments. The MS/MS spectra of additional isotopomers can then be computationally generated and included within isoMETLIN. Given that isobaric isotopomers cannot be separated chromatographically or by mass but are likely to occur simultaneously in a biological system, we have also implemented a spectral-mixing function in isoMETLIN. This functionality allows users to combine MS/MS spectra from various isotopomers in different ratios to obtain a theoretical MS/MS spectrum that matches the MS/MS spectrum from a biological sample. 'Thus, by searching MS and MS/MS experimental data, isoMETLIN facilitates the identification of isotopologues as well as isotopomers from biological samples and provides a platform to drive the next generation of isotope-based metabolomic studies. [ABSTRACT FROM AUTHOR]

Schwann cells (SCs) promote axonal integrity independently of myelination by poorly understood mechanisms. Current models suggest that SC metabolism is critical for this support function and that SC metabolic deficits may lead to axonal demise. The LKB1-AMP-activated protein kinase (AMPK) kinase pathway targets several downstream effectors, including mammalian target of rapamycin (mTOR), and is a key metabolic regulator implicated in metabolic diseases. We found through molecular, structural and behavioral characterization of SC-specific mutant mice that LKB1 activity is central to axon stability, whereas AMPK and mTOR in SCs are largely dispensable. The degeneration of axons in LKB1 mutants was most dramatic in unmyelinated small sensory fibers, whereas motor axons were comparatively spared. LKB1 deletion in SCs led to abnormalities in nerve energy and lipid homeostasis and to increased lactate release. The latter acts in a compensatory manner to support distressed axons. LKB1 signaling is essential for SC-mediated axon support, a function that may be dysregulated in diabetic neuropathy. [ABSTRACT FROM AUTHOR]

Liquid chromatography/mass spectrometry-based untargeted metabolomics is now an established experimental approach that is being broadly applied by many laboratories worldwide. Interpreting untargeted metabolomic data, however, remains a challenge and limits the translation of results into biologically relevant conclusions. Here we review emerging technologies that can be applied after untargeted profiling to extend biological interpretation of metabolomic data. These technologies include advances in bioinformatic software that enable identification of isotopes and adducts, comprehensive pathway mapping, deconvolution of MS² data, and tracking of isotopically labeled compounds. There are also opportunities to gain additional biological insight by complementing the metabolomic analysis of homogenized samples with recently developed technologies for metabolite imaging of intact tissues. To maximize the value of these emerging technologies, a unified workflow is discussed that builds on the traditional untargeted metabolomic pipeline. Particularly when integrated together, the combination of the advances highlighted in this review helps transform lists of masses and fold changes characteristic of untargeted profiling results into structures, absolute concentrations, pathway fluxes, and localization patterns that are typically needed to understand biology. [ABSTRACT FROM AUTHOR]

XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, "control" versus "disease" experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines. [ABSTRACT FROM AUTHOR]

The article proposes a solution to the time demands of metabolomic data upload to the XCMS Online, an Internet-based platform for uploading metabolomic data and cloud-based processing as of June 2014. Topics highlighted include a background on the role played by the metabolomic software XCMS in the emergence of metabolomics as a technology for cellular biochemistry data processing and demonstration of the efficacy of streaming in laboratories at various locations.

Studies of isotopically labeled compounds have been fundamental to understanding metabolic pathways and fluxes. They have traditionally, however, been used in conjunction with targeted analyses that identify and quantify a limited number of labeled downstream metabolites. Here we describe an alternative workflow that leverages recent advances in untargeted metabolomic technologies to track the fates of isotopically labeled metabolites in a global, unbiased manner. This untargeted approach can be applied to discover novel biochemical pathways and characterize changes in the fates of labeled metabolites as a function of altered biological conditions such as disease. To facilitate the data analysis, we introduce X13CMS, an extension of the widely used mass spectrometry-based metabolomic software package XCMS. X13CMS uses the XCMS platform to detect metabolite peaks and perform retention-time alignment in liquid chromatography/mass spectrometry (LC/MS) data. With the use of the XCMS output, the program then identifies isotopologue groups that correspond to isotopically labeled compounds. The retrieval of these groups is done without any a priori knowledge besides the following input parameters: (i) the mass difference between the unlabeled and labeled isotopes, (ii) the mass accuracy of the instrument used in the analysis, and (iii) the estimated retention-time reproducibility of the chromatographic method. Despite its name, X13CMS can be used to track any isotopic label. Additionally, it detects differential labeling patterns in biological samples collected from parallel control and experimental conditions. We validated the ability of X13CMS to accurately retrieve labeled metabolites from complex biological matrices both with targeted LC/MS/MS analysis of a subset of the hits identified by the program and with labeled standards spiked into cell extracts. We demonstrate the full functionality of X13CMS with an analysis of cultured rat astrocytes treated with uniformly labeled (U-)13C-glucose during lipopolysaccharide (LPS) challenge. Our results show that out of 223 isotopologue groups enriched from U-13C-glucose, 95 have statistically significant differential labeling patterns in astrocytes challenged with LPS compared to unchallenged control cells. Only two of these groups overlap with the 32 differentially regulated peaks identified by XCMS, indicating that X13CMS uncovers different and complementary information from untargeted metabolomic studies. Like XCMS, X13CMS is implemented in R. It is available from our laboratory website at http://pattilab.wustl.edu/x13cms.php. [ABSTRACT FROM AUTHOR]

Mass spectrometry-based metabolomics relies on MS² data for structural characterization of metabolites. To obtain the high-quality MS² data necessary to support metabolite identifications, ions of interest must be purely isolated for fragmentation. Here, we show that metabolomic MS² data are frequently characterized by contaminating ions that prevent structural identification. Although using narrow-isolation windows can minimize contaminating MS² fragments, even narrow windows are not always selective enough, and they can complicate data analysis by removing isotopic patterns from MS² spectra. Moreover, narrow windows can significantly reduce sensitivity. In this work, we introduce a novel, two-part approach for performing metabolomic identifications that addresses these issues. First, we collect MS² scans with less stringent isolation settings to obtain improved sensitivity at the expense of specificity. Then, by evaluating MS² fragment intensities as a function of retention time and precursor mass targeted for MS² analysis, we obtain deconvolved MS² spectra that are consistent with pure standards and can therefore be used for metabolite identification. The value of our approach is highlighted with metabolic extracts from brain, liver, astrocytes, as well as nerve tissue, and performance is evaluated by using pure metabolite standards in combination with simulations based on raw MS² data from the METLIN metabolite database. A R package implementing the algorithms used in our workflow is available on our laboratory website (http://pattilab.wustl.edu/decoms2.php). [ABSTRACT FROM AUTHOR]