H. Paul Benton

Metabolic oxidation regulates embryonic stem cell differentiation

Metabolites offer an important unexplored complement to understanding the pluripotency of stem cells. Using mass spectrometry-based metabolomics, we show that embryonic stem cells are characterized by abundant metabolites with highly unsaturated structures whose levels decrease upon differentiation. By monitoring the reduced and oxidized glutathione ratio as well as ascorbic acid levels, we demonstrate that the stem cell redox status is regulated during differentiation. Based on the oxidative biochemistry of the unsaturated metabolites, we experimentally manipulated specific pathways in embryonic stem cells while monitoring the effects on differentiation. Inhibition of the eicosanoid signaling pathway promoted pluripotency and maintained levels of unsaturated fatty acids. In contrast, downstream oxidized metabolites (e.g., neuroprotectin D1) and substrates of pro-oxidative reactions (e.g., acyl-carnitines), promoted neuronal and cardiac differentiation. We postulate that the highly unsaturated metabolome sustained by stem cells makes them particularly attuned to differentiate in response to in vivo oxidative processes such as inflammation.

Interactive XCMS Online: Simplifying Advanced Metabolomic Data Processing and Subsequent Statistical Analyses

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.

Bioinformatics: The Next Frontier of Metabolomics

Bioinformatic tools are required to carry out essential functions such as statistical analyses and database functionalities. Now, they are also needed for one of the most difficult tasks, helping researchers decide which metabolites are the most biologically meaningful. This can be achieved through aiding the identification process, reducing feature redundancy, putting forward better candidates for tandem mass spectrometry (MS/MS), speeding up or automating the workflow, deconvolving the feature list through meta-analysis or multigroup analysis, or using stable isotopes and pathway mapping. This review thus focuses on the most recent and innovative bioinformatic advancements for identifying metabolites. A primary objective of metabolomics beyond biomarker discovery is to identify the most meaningful metabolites that correlate with disease pathogenesis or other perturbations of metabolism. Metabolites play important roles in biological pathways; their flux or differential regulation (dysregulation) can reveal novel insights into disease and environmental influences. Therefore, one of the most important goals of metabolomic analysis has been to assign metabolite identity so they can be used for further statistical and informed pathway analysis.1,2 Over the past few years, technologies for analyzing metabolites by untargeted or targeted metabolomics have undergone extensive improvements. Strides to establish the most efficient protocols for experimental design, sample extraction techniques, and data acquisition have paid off providing robust complex data sets.3−9 As more is being required of these data sets such as assigning identity and biological meaning to the features, bioinformatics is the area of metabolomics which is currently undergoing the most needed growth. It is often the case that metabolomic analysis results in a list of metabolites with low specificity for the disease or stimulus being studied (Figure ​(Figure1).1). Some of these metabolites seem to be dysregulated in a variety of diseases such as acylcarnitines10−13 and fatty acids.14−17 They may be more indicative of a perturbed systemic cause (appetite, physical activity, diurnal rhythm changes, etc..), sample contamination, or instrumental/bioinformatic noise, rather than a specific biomarker of disease. An example of this can be seen in the analysis of urinary biomarkers of ionizing radiation, where dicarboxylic acids were downregulated in the rat after radiation exposure. It was proven that this observation was actually caused by a decreased appetite after radiation exposure perturbing the β-oxidation pathway and not from radiation-induced cellular changes.18,19 Furthermore, dicarboxylic acids can leach out from plastics during the extraction process, further adding to the ambiguity of their role in ionizing radiation.20 Figure 1 Biomarkers that have high vs low disease specificity. As well as identifying the correct source of the biomarkers, it is also important to identify their physiological role and how to utilize them as therapeutic targets. This first has to start with the identification of the metabolite and is determined by filtering thresholds set by the user which is intrinsically biased. These thresholds include those for fold change and p-value, which are highly dependent on the experiment; in vitro experiments would exhibit lower variation between biological replicates than in vivo. The ease of identifying the metabolite is also determined by its concentration in the sample and previous annotation in metabolite databases. Filtering thresholds for metabolite intensity that are set too high may omit important biologically meaningful metabolites rather than noise. Furthermore, a metabolite that is novel or not curated in a database may not be taken into consideration based on the chemical knowledge of the researcher and what they deem as meaningful. In order to transform the complex list of identified metabolites into markers of disease, or assign what role they play, bioinformatic tools can aid in identifying the potential pathways that the metabolite may belong to. It is then that the researcher can use this knowledge surrounding the biology of the metabolite to probe the mechanism of the disease. Untargeted metabolomics has already been used in such a manner to find the source of neuropathic pain.21N,N-Dimethylsphingosine was dysregulated in a rat model of neuropathic pain, furthermore when dosed to control rats it induced mechanical hypersensitivity. This metabolite implicated the sphingomyelin-ceramide pathway as a potential therapeutic target. Antimetabolite inhibitors of enzymes in this pathway were tested and were able to ameliorate neuropathic pain (unpublished data). This study holds promise for other metabolomic studies to maximize the potential information contained within the data for finding therapeutics of disease rather than only providing lists of dysregulated metabolites.

Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling

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.

Intra- and interlaboratory reproducibility of ultra performance liquid chromatography-time-of-flight mass spectrometry for urinary metabolic profiling.

Liquid chromatography coupled to mass spectrometry (LC-MS) is a major platform in metabolic profiling but has not yet been comprehensively assessed as to its repeatability and reproducibility across multiple spectrometers and laboratories. Here we report results of a large interlaboratory reproducibility study of ultra performance (UP) LC-MS of human urine. A total of 14 stable isotope labeled standard compounds were spiked into a pooled human urine sample, which was subject to a 2- to 16-fold dilution series and run by UPLC coupled to time-of-flight MS at three different laboratories all using the same platform. In each lab, identical samples were run in two phases, separated by at least 1 week, to assess between-day reproducibility. Overall, platform reproducibility was good with median mass accuracies below 12 ppm, median retention time drifts of less than 0.73 s and coefficients of variation of intensity of less than 18% across laboratories and ionization modes. We found that the intensity response was highly linear within each run, with a median R(2) of 0.95 and 0.93 in positive and negative ionization modes. Between-day reproducibility was also high with a mean R(2) of 0.93 for a linear relationship between the intensities of ions recorded in the two phases across the laboratories and modes. Most importantly, between-lab reproducibility was excellent with median R(2) values of 0.96 and 0.98 for positive and negative ionization modes, respectively, across all pairs of laboratories. Interestingly, the three laboratories observed different amounts of adduct formation, but this did not appear to be related to reproducibility observed in each laboratory. These studies show that UPLC-MS is fit for the purpose of targeted urinary metabolite analysis but that care must be taken to optimize laboratory systems for quantitative detection due to variable adduct formation over many compound classes.

Thermal Degradation of Small Molecules: A Global Metabolomic Investigation

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.

METLIN: A Technology Platform for Identifying Knowns and Unknowns

METLIN originated as a database to characterize known metabolites and has since expanded into a technology platform for the identification of known and unknown metabolites and other chemical entities. Through this effort it has become a comprehensive resource containing over 1 million molecules including lipids, amino acids, carbohydrates, toxins, small peptides, and natural products, among other classes. METLINs high-resolution tandem mass spectrometry (MS/MS) database, which plays a key role in the identification process, has data generated from both reference standards and their labeled stable isotope analogues, facilitated by METLIN-guided analysis of isotope-labeled microorganisms. The MS/MS data, coupled with the fragment similarity search function, expand the tools capabilities into the identification of unknowns. Fragment similarity search is performed independent of the precursor mass, relying solely on the fragment ions to identify similar structures within the database. Stable isotope data also facilitate characterization by coupling the similarity search output with the isotopic m/ z shifts. Examples of both are demonstrated here with the characterization of four previously unknown metabolites. METLIN also now features in silico MS/MS data, which has been made possible through the creation of algorithms trained on METLINs MS/MS data from both standards and their isotope analogues. With these informatic and experimental data features, METLIN is being designed to address the characterization of known and unknown molecules.

Metabolic drift in the aging brain

Brain function is highly dependent upon controlled energy metabolism whose loss heralds cognitive impairments. This is particularly notable in the aged individuals and in age-related neurodegenerative diseases. However, how metabolic homeostasis is disrupted in the aging brain is still poorly understood. Here we performed global, metabolomic and proteomic analyses across different anatomical regions of mouse brain at different stages of its adult lifespan. Interestingly, while severe proteomic imbalance was absent, global-untargeted metabolomics revealed an energy metabolic drift or significant imbalance in core metabolite levels in aged mouse brains. Metabolic imbalance was characterized by compromised cellular energy status (NAD decline, increased AMP/ATP, purine/pyrimidine accumulation) and significantly altered oxidative phosphorylation and nucleotide biosynthesis and degradation. The central energy metabolic drift suggests a failure of the cellular machinery to restore metabostasis (metabolite homeostasis) in the aged brain and therefore an inability to respond properly to external stimuli, likely driving the alterations in signaling activity and thus in neuronal function and communication.

Training in metabolomics research. I. Designing the experiment, collecting and extracting samples and generating metabolomics data: Design and execution of a metabolomics experiment

The study of metabolism has had a long history. Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. The National Institutes of Health Common Fund Metabolomics Program was established in 2012 to stimulate interest in the approaches and technologies of metabolomics. To deliver one of the programs goals, the University of Alabama at Birmingham has hosted an annual 4-day short course in metabolomics for faculty, postdoctoral fellows and graduate students from national and international institutions. This paper is the first part of a summary of the training materials presented in the course to be used as a resource for all those embarking on metabolomics research. The complete set of training materials including slide sets and videos can be viewed at http://www.uab.edu/proteomics/metabolomics/workshop/workshop_june_2015.php. Copyright

Training in metabolomics research. I. Designing the experiment, collecting and extracting samples and generating metabolomics data

Metabolomics is perhaps the most challenging of the -omics fields, given the complexity of an organisms metabolome and the rapid rate at which it changes. When one sets out to study metabolism there are numerous dynamic variables that can influence metabolism that must be considered. Recognizing the experimental challenges confronting researchers who undertake metabolism studies, workshops like the one at University of Alabama at Birmingham have been established to offer instructional guidance. A summary of the UAB course training materials is being published as a two-part Special Feature Tutorial. In this months Part I the authors discuss details of good experimental design and sample collection and handling. In an upcoming Part II, the authors discuss in detail the various aspects of data analysis.