Caroline H. Johnson

Metabolomics: beyond biomarkers and towards mechanisms

Metabolomics, which is the profiling of metabolites in biofluids, cells and tissues, is routinely applied as a tool for biomarker discovery. Owing to innovative developments in informatics and analytical technologies, and the integration of orthogonal biological approaches, it is now possible to expand metabolomic analyses to understand the systems-level effects of metabolites. Moreover, because of the inherent sensitivity of metabolomics, subtle alterations in biological pathways can be detected to provide insight into the mechanisms that underlie various physiological conditions and aberrant processes, including diseases.

Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database

Untargeted metabolomics provides a comprehensive platform for identifying metabolites whose levels are altered between two or more populations. By using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS), hundreds to thousands of peaks with a unique m/z ratio and retention time are routinely detected from most biological samples in an untargeted profiling experiment. Each peak, termed a metabolomic feature, can be characterized on the basis of its accurate mass, retention time and tandem mass spectral fragmentation pattern. Here a seven-step protocol is suggested for such a characterization by using the METLIN metabolite database. The protocol starts from untargeted metabolomic LC-Q-TOF-MS data that have been analyzed with the bioinformatics program XCMS, and it describes a strategy for selecting interesting features as well as performing subsequent targeted tandem MS. The seven steps described will require 2–4 h to complete per feature, depending on the compound.

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.

Xenobiotic Metabolomics: Major Impact on the Metabolome

Xenobiotics are encountered by humans on a daily basis and include drugs, environmental pollutants, cosmetics, and even components of the diet. These chemicals undergo metabolism and detoxication to produce numerous metabolites, some of which have the potential to cause unintended effects such as toxicity. They can also block the action of enzymes or receptors used for endogenous metabolism or affect the efficacy and/or bioavailability of a coadministered drug. Therefore, it is essential to determine the full metabolic effects that these chemicals have on the body. Metabolomics, the comprehensive analysis of small molecules in a biofluid, can reveal biologically relevant perturbations that result from xenobiotic exposure. This review discusses the impact that genetic, environmental, and gut microflora variation has on the metabolome, and how these variables may interact, positively and negatively, with xenobiotic metabolism.

Challenges and opportunities of metabolomics

The metabolome is a data‐rich source of information concerning all the low‐molecular‐weight metabolites in a biofluid, which can indicate early biological changes to the host due to perturbations in metabolic pathways. Major changes can be seen after minor stimuli, which make it a valuable target for analysis. Due to the diverse and sensitive nature of the metabolome, studies must be designed in a manner to maintain consistency, reduce variation between subjects, and optimize information recovery. Technological advancements in experimental design, mouse models and instrumentation have aided in this effort. Metabolomics has the ultimate potential to be valuable in a clinical setting where it could be used for early diagnosis of a disease and as a predictor of treatment response and survival. During drug treatment, the metabolic status of an individual could be monitored and used to indicate possible toxic effects. Metabolomics therefore has great potential for improving diagnosis, treatment and aftercare of disease.

Metabolism Links Bacterial Biofilms and Colon Carcinogenesis

Bacterial biofilms in the colon alter the host tissue microenvironment. A role for biofilms in colon cancer metabolism has been suggested but to date has not been evaluated. Using metabolomics, we investigated the metabolic influence that microbial biofilms have on colon tissues and the related occurrence of cancer. Patient-matched colon cancers and histologically normal tissues, with or without biofilms, were examined. We show the upregulation of polyamine metabolites in tissues from cancer hosts with significant enhancement of N(1), N(12)-diacetylspermine in both biofilm-positive cancer and normal tissues. Antibiotic treatment, which cleared biofilms, decreased N(1), N(12)-diacetylspermine levels to those seen in biofilm-negative tissues, indicating that host cancer and bacterial biofilm structures contribute to the polyamine metabolite pool. These results show that colonic mucosal biofilms alter the cancer metabolome to produce a regulator of cellular proliferation and colon cancer growth potentially affecting cancer development and progression.

Radiation Metabolomics. 4. UPLC-ESI-QTOFMS-Based Metabolomics for Urinary Biomarker Discovery in Gamma-Irradiated Rats

Radiation metabolomics has aided in the identification of a number of biomarkers in cells and mice by ultra-performance liquid chromatography-coupled time-of-flight mass spectrometry (UPLC-ESI-QTOFMS) and in rats by gas chromatography-coupled mass spectrometry (GCMS). These markers have been shown to be both dose- and time-dependent. Here UPLC-ESI-QTOFMS was used to analyze rat urine samples taken from 12 rats over 7 days; they were either sham-irradiated or &ggr;-irradiated with 3 Gy after 4 days of metabolic cage acclimatization. Using multivariate data analysis, nine urinary biomarkers of &ggr; radiation in rats were identified, including a novel mammalian metabolite, N-acetyltaurine. These upregulated urinary biomarkers were confirmed through tandem mass spectrometry and comparisons with authentic standards. They include thymidine, 2′-deoxyuridine, 2′deoxyxanthosine, N1-acetylspermidine, N-acetylglucosamine/galactosamine-6-sulfate, N-acetyltaurine, N-hexanoylglycine, taurine and, tentatively, isethionic acid. Of these metabolites, 2′-deoxyuridine and thymidine were previously identified in the rat by GCMS (observed as uridine and thymine) and in the mouse by UPLC-ESI-QTOFMS. 2′Deoxyxanthosine, taurine and N-hexanoylglycine were also seen in the mouse by UPLC-ESI-QTOFMS. These are now unequivocal cross-species biomarkers for ionizing radiation exposure. Downregulated biomarkers were shown to be related to food deprivation and starvation mechanisms. The UPLC-ESI-QTOFMS approach has aided in the advance for finding common biomarkers of ionizing radiation exposure.

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.

Optimization of harvesting, extraction, and analytical protocols for UPLC-ESI-MS-based metabolomic analysis of adherent mammalian cancer cells

AbstractIn this study, a liquid chromatography mass spectrometry (LC/MS)-based metabolomics protocol was optimized for quenching, harvesting, and extraction of metabolites from the human pancreatic cancer cell line Panc-1. Trypsin/ethylenediaminetetraacetic acid (EDTA) treatment and cell scraping in water were compared for sample harvesting. Four different extraction methods were compared to investigate the efficiency of intracellular metabolite extraction, including pure acetonitrile, methanol, methanol/chloroform/H2O, and methanol/chloroform/acetonitrile. The separation efficiencies of hydrophilic interaction chromatography (HILIC) and reversed-phase liquid chromatography (RPLC) with UPLC-QTOF-MS were also evaluated. Global metabolomics profiles were compared; the number of total detected features and the recovery and relative extraction efficiencies of target metabolites were assessed. Trypsin/EDTA treatment caused substantial metabolite leakage proving it inadequate for metabolomics studies. Direct scraping after flash quenching with liquid nitrogen was chosen to harvest Panc-1 cells which allowed for samples to be stored before extraction. Methanol/chloroform/H2O was chosen as the optimal extraction solvent to recover the highest number of intracellular features with the best reproducibility. HILIC had better resolution for intracellular metabolites of Panc-1 cells. This optimized method therefore provides high sensitivity and reproducibility for a variety of cellular metabolites and can be applicable to further LC/MS-based global metabolomics study on Panc-1 cell lines and possibly other cancer cell lines with similar chemical and physical properties. FigureOptimized harvesting, extraction and analytical protocols for cell metabolomics analysis.